EXAM 3 Flashcards by Estefany Hernandez

7.1 What is Data Exploration?

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Define Data Exploration

Also called 1) ex___y data analysis, it is the 2) di____y process of 3) lo___g for something 4) n___ and previously 5) u___n in the data

This is accomplished by looking for 6) p___ns, ou___s, or, more generally, for 7) ins____.

1) exploratory
2) discovery
3) looking
4) new
5) unknown
6) patterns, outliers
7) insights

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Define Insights

an 1) obse_____ that might 2) sig____ af___t a business’ 3) de___-m___g.

1) observation
2) significantly affect
3) decision-making.

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Remember, decisions are 1) n___ based on 2) d___.

Rather, decisions are informed by the 3) in___s generated from 4) d___

1) not
2) data
3) insights
4) data

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This process of generating insights is what 1) dis___ data analysis from the simple act of reporting 2) nu____.

While they might share some tools, it is essential to differentiate between data 3) e___on, int____ion, and rep___:

Exploration: 4) Di__ng in___s.

Interpretation: 5) Con____g and unde____ in___s.

Reporting: 6) Com___ ins___s

1) distinguishes
2) numbers
3) exploration, interpretation, and reporting
4) Discovering insights
5) Contextualizing and understanding insights
6) Communicating insights

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The Process of Data Exploration

What’s the 4 step process of Data Exploration?

Ide___g Q____s
Id____g D___ Rel___s
Exp___ D___a Rel____s
Gen___g In____s

Identifying Questions
Identifying Data Relationships
Exploring Data Relationships
Generating Insights

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The Process of Data Exploration
1. Identifying Questions

Data exploration helps answer accounting 1) que__s such as whether sales and profits are 2) im___g, which products deserve 3) inv____, if 4) b__d d___s are appropriately managed, and more.

An 5) an___ database should provide 6) an___s to both 7) an____d and un____d (un___ed) qu____s.

Once the question is 8) det___, such as the question about whether unit sales are improving in Illustration 7.1, then the underlying data 9) rela___s can be 10) id___

1) questions
2) improving
3) investment
4) bad debts
5) analytical
6) answers
7) anticipated and unanticipated (unplanned) questions
8) determined
9) relationships
10) identified

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The Process of Data Exploration
2. Identifying Data Relationships

Define Data Relationships

describes 1) h__ d__ el___s (or v__s) 2) rel___ to each 3) ot___

-But before aspects of data relationships can be analyzed, they have to be 4) id__

1) how data elements (or values)
2) relate
3) other
4) identified

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The Process of Data Exploration
2. Identifying Data Relationships

Stephen Few, an expert in data visualizations, differentiates eight foundational data relationships

No____ co___son
Di____n
Dev___
Ra___g
Pa__-to-wh___e
Cor____
T___e se___s
Geo___

The data relationship identified in Illustration 7.1 is a time series, which describes 1) ho__ something 2) cha__ o___r ti__ and helps to 3) ide___ pa___s of ch___.

A relationship that has been 4) ide___, whether it is a time series relationship or one of the relationships examined later in this chapter, is ready for 5) ex____n

Nominal comparison
Distribution
Deviation
Ranking
Part-to-whole
Correlation
Time series
Geospatial

1) how
2) changes over time
3) identify patterns of change
4) identified
5) exploration

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The Process of Data Exploration
3. Explore Data Relationships

While there are different approaches to exploring data relationships, 1) vis____ and s___s are the most common.

Exploration involves 2) sel___ the 3) vis__n or vis___s 4) b___t su__d for 5) ex___g the data 6) rel___s. In Illustration 7.1 a line chart visualizes the time series

1) visualization and statistics
2) selecting
3) visualization or visualizations
4) best suited
5) exploring
6) relationships

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The Process of Data Exploration
3. Explore Data Relationships

Keep in mind that 1) t___l-sp___c kno___ is required to create 2) vis___s.

For example, a time series analysis requires knowing how to create line charts.

Business intelligence software such as Excel, Power BI, and Tableau all have powerful tools that visualize data relationships for exploration

1) tool-specific knowledge
2) visualizations

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The Process of Data Exploration
4. Generate Insights

The line chart in Illustration 7.1 shows an upward trend of unit sales starting in 2023.

Exploring this insight further would include discovering the 1) s___e of that growth and if the upward trend can be explained by other 2) fa___.

In fact, data exploration is a 3) con___s p___s.

4) Ins___ generate n__w que___, which then generate even 5) m___e ins____. These observations are then 6) int____ and co____ed to stakeholders during the 7) l___t st__ of the data 8) a___s p___ss

1) source
2) factors
3) continuous process.
4) Insights generate new
5) more insights.
6) interpreted and communicated
7) last stage
8) analysis process

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Exploring Data with PivotTables

Data exploration 1) inv____s data from 2) di___t an___s to collect 3) in__s.

A widely-used tool for this is the 4) Ex__ Pi___T___e

1) investigates
2) different angles
3) insights
4) Excel PivotTable

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The five components used for data exploration with PivotTables are 1) fi___s, v___s, r___s, col___s, and fil___

1) fields, values, rows, columns, and filters

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Fields

The Fields area 1) li__ all the data 2) el___s available for 3) exp___n p___es. They can be dragged and dropped to other areas to build data 4) rel____ and fi__r the data.

In Illustration 7.3, the Model and UnitsSold fields are used for exploration.

1) lists
2) elements available
3) exploration purposes
4) relationships and filter

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Values
The Values area in Illustration 7.3 (B) represents the 1) n__r or nu__rs to be 2) an__d.

It can be used to 3) e___e data in different ways:
-Drag and drop any field into the Values area and apply mathematical operations such as average, count, or sum to it.

-Create calculated fields.

Examples of accounting-related values that could be analyzed include gross revenue, net revenue, taxes, cost, profit, and more. For HNA, the values in the UnitsSold field are summed, generating the total number of units sold during the 2021–2025 period.

1) number or numbers
2) analyzed
3) exploree

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Filters

Using filters further enhances data exploration with Excel PivotTables.

Filters let us determine 1) w___t d__a should be 2) con____ for analysis, and they can be 3) cr___d for any field

1) what data
2) considered
3) created

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7.2 How are Data Relationships Visualized for Exploration?

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There are two types of data exploration patterns.

Some patterns explore a 1) fou___ data re___p with a 2) s___le vis____n, while others explore data by 3) inte___ da__ re___ps

1) foundational data relationship
2) single visualization
3) integrating data relationships

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Data Exploration Pattern 1: Nominal Comparison

Define Nominal Comparison Data Relationship

A data 1) rel___p that 2) com___s the 3) va___s of a 4) cat___l va___le based on a 5) s___d, nu___ variable

1) relationship
2) compares
3) values
4) categorical variable
5) second, numeric

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Data Exploration Pattern 1: Nominal Comparison

Define Exploration Structure

A 1) vi___l that 2) d___es the 3) dif___t data 4) e__ts 5) u___d in data 6) exp___ and 7) h__w they are 8) r__d

1) visual
2) describes
3) different
4) elements
5) used
6) exploration
7) how
8) related

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Data Exploration Pattern 1: Nominal Comparison

For nominal comparisons, the exploration structure is a 1) n___al variable, which is 2) w___ is being 3) com___, and a 4) nu____ variable, 5) w___ is 6) h__w the values of the 7) n__l variable are 8) co__d

1) nominal
2) what
3) compared
4) numeric
5) which
6) how
7) nominal
8) compared

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Data Exploration Pattern 1: Nominal Comparison
Visualizations

Visualizations for nominal comparisons include 1) b___r charts, c___n charts, d___ plots, and loll___p charts.

1) bar charts, column charts, dot plots, and lollipop charts

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Data Exploration Pattern 1: Nominal Comparison
Exploration and Insights

A nominal comparison can 1) qu___y ev___e data and collect 2) in___ in__t, which is especially 3) u___l when 4) w____g with a 5) n___ data set.

It allows us to compare the 6) si___s of each category–which is the 7) bi___t, which is the 8) sm___t, if one category is 9) tw__e as big as another, and more

1) quickly evaluate
2) initial insights
3) useful
4) working
5) new
6) sizes
7) biggest
8) smallest
9) twice

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Data Exploration Pattern 1: Nominal Comparison Exploration and Insights -Any nominal variable, or 1) w___ is being a__d, can be 2) co___d using any numeric variable, or 3) h___w the nominal variable is being 4) a___d. -Filters can also be applied to both the 5) nom__l variable and the 6) n___c variable.

1) what is being analyzed 2) compared 3) how 4) analyzed 5) nominal 6) numeric

Data Exploration Pattern 2: Distribution Define Distribution Data Relationship A data relationship that shows 1) h__w the 2) va__s of a 3) n___c variable are 4) dist___, or sp___ out, by 5) pro___ the 6) dif___ values 7) pr__nt in the data

1) how 2) values 3) numeric 4) distributed, or spread out 5) providing 6) different 7) present

Data Exploration Pattern 2: Distribution A common scenario, shown in Illustration 7.11, is to create and compare multiple distributions of the same numeric variable based on the different values of a nominal variable. The dotted line indicates that such a variable is valuable for exploration, but optional when defining a distribution data relationship.

Data Exploration Pattern 2: Distribution Visualizations Several visualizations portray distributions, including 1) hist___, v__n plots, and b__-and-w___er charts (or 2) b__lot charts). Box-and-whisker charts are both 3) po___ and de___d, so they are used here to illustrate a visualization for this pattern

1) histograms, violin plots, and box-and-whisker charts 2) boxplot charts 3) powerful and detailed

Data Exploration Pattern 2: Distribution Exploration and Insights Using the exploration structure for a 1) dis___ data relationship, we can 2) q___y examine the distribution of any 3) nu___ field in a data set and use any nominal field in the data set to 4) co__e data distributions.

1) distribution 2) quickly 3) numeric 4) compare

Data Exploration Pattern 2: Distribution Exploration and Insights It might be necessary to 1) ex___e the data 2) m___ to 3) be___ u____and the sou__ of the variation.

1) explore 2) more 3) better understand the source

Data Exploration Pattern 2: Distribution Exploration and Insights Distribution analysis has many applications, including analyzing employee compensation. To understand how salaries are distributed, distribution analysis could be used to answer the following questions: -Are salaries right-skewed? -Are the higher salaries more spread out? -How do salaries compare across departments? -How does an organization’s compensation distribution compare to the distribution for other organizations?

Data Exploration Pattern 3: Deviation Define Deviation Data Relationship A data relationship that shows 1) h__w a___l values deviate from their 2) refe___ va__s, which are 3) bu___ or fore___ values

1) how actual values deviate 2) reference values 3) budgeted or forecasted

Data Exploration Pattern 3: Deviation Deviation relationships are everywhere in accounting. 1) Va__e an___s is a good example, such as the difference between actual and standard costs. The exploration structure for deviation relationships shown in Illustration 7.18 contains the variable being compared and the variables being used for comparison purposes–actual, target, and deviation.

1) Variance analysis

Data Exploration Pattern 3: Deviation Visualizations 1) Clu___ bar and 2) c___n charts (used in the following example), 3) ga___s, 4) b___t charts, and more can be used for 5) d___ion analysis.

1) Clustered 2) column 3) gauges 4) bullet 5) deviation

Data Exploration Pattern 3: Deviation Visualizations Unlike a column chart, a 1) clu___d column chart uses 2) m___e variables for 3) com___n purposes. While the clustered column chart can make comparisons 4) a___s models, it also provides a comparison 5) w__n a model

1) clustered 2) multiple 3) comparison 4) across 5) within

Data Exploration Pattern 3: Deviation Exploration and Insights There are also three nominal variables in this data set to choose from: 1) co___, ty___, or m____l

1) country, type, or model

Data Exploration Pattern 3: Deviation Exploration and Insights Different types of 1) in____s can be 2) col___d from 3) dev___ analysis, including when results are 4) h___r or more fa___e than exp____, lo___ or l___s fa___ble than expected, and if targets are 5) unre____. Once we 6) id___y these insights, the next step might be exploring possible 7) ca___, such as how to reverse 8) unf___e deviations. This is particularly useful when the variances are 9) si___nt.

1) insights 2) collected 3) deviation analysis 4) higher or more favorable than expected, lower or less favorable 5) unreasonable 6) identify 7) causes 8) unfavorable 9) significant

Data Exploration Pattern 3: Deviation Exploration and Insights Variances have 1) hi____ been 2) v___ble tools for accountants to indicate issues that should be addressed: -3) Ineff____ such as 3.1) ine____e in negotiating prices, ill-q____d employees, employees assigned the wrong task, use of l___-quality re___s, or in___e training. -4) Une____d ch____s such as changes in 5) pr___s, or a 6) dec___e in 7) de___d. -8) P___r b____ing

1) historically 2) valuable 3) Inefficiencies 3.1) inexperience in negotiating prices, ill-qualified employees, employees assigned the wrong task, use of low-quality resources, or inadequate training. 4) Unexpected changes 5) prices 6) decrease 7) demand 8) Poor budgeting

Data Exploration Pattern 4: Ranking Define Ranking Data Relationship A data relationship that 1). or__s the 2) va___s of a variable 3) se___lly based on the values of a 4) se___d variable Ranks are determined by some 5) qu___, such as 6) hig___, lo___t, f__st, sl___t, as defined by the 7) se___ variable. The ranking can be displayed in 8) a___ing or des___g order. It is also possible to calculate a 9) r___k exp__

1) orders 2) values 3) sequentially 4) second 5) quality 6) highest, lowest, fastest, slowest 7) second 8) ascending or descending 9) rank explicitly

Data Exploration Pattern 4: Ranking Visualizations There are 1) n__ s___ic ra__g visualizations, but many 2) inte___ ranking in____on, including 3) ta___s, b___ charts, and co___n chart

1) no specific ranking 2) integrate ranking information 3) tables, bar charts, and column chart

Data Exploration Pattern 5: Part-to-Whole Define Part-to-Whole Data Relationship A data relationship that 1) com___s pa___s to w__es and examines how the 2) dif___ p___s co___e to each other A part-to-whole visualization should define the 3) nu___er that is the 4) wh__ and 5) h___ the whole should be 6) br___n d__n into 7) pa___.

1) compares parts to wholes 2) different parts compare 3) number 4) whole 5) how 6) broken down 7) parts

Data Exploration Pattern 5: Part-to-Whole Visualizations Multiple visualizations can model part-to-whole relationships, including 1) p__ charts, do___ charts, sta___ b__r charts, stacked col___ charts, and tr___ps. While treemaps are considered 2) sup___r for portraying part-to-whole relationships, here the more commonly used 3) p__e chart is used

1) pie charts, donut charts, stacked bar charts, stacked column charts, and treemaps 2) superior 3) pie

Data Exploration Pattern 6: Correlation Define Correlation Data Relationship A data relationship that indicates the 1) de__e to which 2) t__o va___es m__e in the 3) s___e or the 4) op___e dir___n. For example, if the marketing expenses for a product increase, then it is likely that the sales for the same product also increase

1) degree 2) two variables move 3) same 4) opposite direction.

Data Exploration Pattern 6: Correlation What are the 2 key features to consider with correlation data relationships ? -Di___n -Str___ of the cor___on

-Direction -Strength of the correlation

Data Exploration Pattern 6: Correlation 2 key features to consider with correlation data relationships -Direction -The direction is positive if both variables move in the 1) s__e direction. -The direction is negative if both variables move in 2) op___e directions

1) same 2) opposite

Data Exploration Pattern 6: Correlation 2 key features to consider with correlation data relationships -Strength of the Correlation Strength indicates the degree of 1) co___on between the 2) t__o va__, ranging from 3) n__ correlation to 4) pe___ct correlation

1) correlation 2) tow variables 3) no 4) perfect

Data Exploration Pattern 6: Correlation The most common visualization for exploring correlation is the 1) sca__t, which is also referred to as a 2) s___er ch__t. It 3) pl__s the 4) co__es for 5) t___o var__s for each data point.

1) scatterplot 2) scatter chart 3) plots 4) coordinates 5) two variables

Data Exploration Pattern 7: Time Series Define Time Series Data Relationship A relationship that defines the values of a variable at 1) seq___ po___s in t___e. To display a time series, a visualization requires a 2) ti___ unit such as a 3) min___, h___r, d__y, or w__k, and a variable that 4) cha___s o___r t__e.

1) sequential points in time 2) time 3) minute, hour, day, or week 4) changes over time.

Data Exploration Pattern 7: Time Series Visualizations A 1) li___ ch___t is the most common visualization for time series, but 2) b__r charts, c___n charts, a__a charts, wa__ll charts, and sp___e charts can also be use

1) line chart 2) bar charts, column charts, area charts, waterfall charts, and sparkline charts

Data Exploration Pattern 7: Time Series Exploration and Insights Look for 1) tr___s, cy___s, and irr____s when exploring time series

1) trends, cycles, and irregularities

Data Exploration Pattern 7: Time Series Exploration and Insights Look for trends, cycles, and irregularities when exploring time series: -A trend indicates the 1) ge___ di___n (up___d or dow___) in which a variable moves 2) o__r t__e. -A cycle indicates a 3) pat___of ch___s, which can vary in 4) le___h and in___ o___r t___e. 5) Se___ty refers to 6) in___a-y___r cy___s with a 7) fixed nature. -Irregularities are 8) unsy___c, typically sh__t-t___m, flu___n

1) general direction (upward or downward) 2) over time 3) pattern of changes 4) length and intensity over time 5) Seasonality 6) intra-year cycles 7) fixed nature 8) unsystematic, typically short-term, fluctuation

Data Exploration Pattern 8: Geospatial Define Geospatial Data Relationship A data relationship in which 1) nu___c values are assigned to 2) lo___s and enc__ by c___r and the s__e of the b___s wi___ the visualization

1) numeric 2) locations and encoded by color and the size of the bubbles within

Data Exploration Pattern 8: Geospatial Visualizations Geospatial relationships are defined using maps -1) Ch___h Map -2) Pro__al Sy__l Map

1) Choropleth 2) Proportional Symbol

Data Exploration Pattern 8: Geospatial Visualizations Geospatial relationships are defined using maps -Choropleth Map uses 1) co__ intensity to represent data 2) v___s

1) color intensity 2) values

Data Exploration Pattern 8: Geospatial Visualizations Geospatial relationships are defined using maps -Proportional Symbol Map uses 1) s___ls—often 2) bu__s/ci__s—and the 3) s__e of the symbol represents the data 4) v__e. The 5) la__r the symbol, the 6) h___r the value. EX. A business could create a map that shows the total revenue (numeric variable) per city (location variable) for a specific state. The larger the bubble representing the city, the higher the revenue for that city.

1) symbols 2) bubbles/circles 3) size 4) value 5) larger 6) higher

7.3 How are Data Explored by Integrating Data Relationships?

1) Fo____l data relationship with 2) o___e visualization, data exploration often requires integrating 3) t__o or m___ data 4) rel___ 5) Int___d data relationships can be represented with a 6) s__le visualization or by using 7) di___t visualizations as part of a 8) rep___. 9) Co__e t__s and 10) P___o an___are two examples of 11) c___d data relationships that can be represented in 12) o__e chart

1) Foundational 2) one 3) two or more 4) relationships 5) Integrated 6) single 7) different visualizations as 8) report 9) Composite trends 10) Pareto analysis 11) combined 12) one

Data Exploration Pattern 9: Composite Trends Define Composite trend data relationship An 1) in____d data 2) re___p that shows the 3) changes in a 4) p__t-to-wh__ rel___p over 5) ti___, often 6) an___

1) integrated 2) relationship 3) changes 4) part-to-whole relationship 5) time 6) analyzed

Data Exploration Pattern 9: Composite Trends The exploration structure for composite trends has three variables and combines the exploration structures of the time series data relationship and part-to-whole data relationship

3 variables -nominal variable -numeric variable -time unit variable

Data Exploration Pattern 9: Composite Trends Exploration and Insights Composite trends can be explored for any 1) comb__ of m__s (2) nu___c v___le), 3) dim___s (4) no___al var__), and 5) t__e u__ts by dragging and dropping.

1) combination of measures 2) numeric variable 3) dimensions 4) nominal variable 5) time unit

Data Exploration Pattern 9: Composite Trends Exploration and Insights A composite trend pattern can also show how the 1) rel____ im___e of the different 2) ac___s in an 3) inc___ st___nt changed in a 4) sp___c period. This is a combination of 5) ve___l (6) p__t-t__-wh__e) and 7) ho___l (8) t__e s__s) 9) fin___ st___nt analysis.

1) relative importance 2) accounts 3) income statement 4) specific 5) vertical 6) part-to-whole 7) horizontal 8) time series 9) financial statement

Data Exploration Pattern 10: Pareto Analysis Define Pareto Analysis A 1) st___al t___ue that 2) vi___s the 3) im___e of different 4) ca___ies (which is the 5) no___l co__on), 6) ra___s them, and shows how each 7) c___ory con___s to the 8) cum__e p__ge (9) pa__ to w__)

1) statistical technique 2) visualizes 3) importance 4) categories 5) nominal comparison 6) ranks 7) category contributes 8) cumulative percentage 9) part to whole

Data Exploration Pattern 10: Pareto Analysis Visualizations A Pareto chart visualizes the combination of the relationships shown in the exploration structure. Some tools, like Excel, offer Pareto charts. They can also be created as 1) li___ and co___n charts, which are supported by most tools

1) line and column

Data Exploration Pattern 10: Pareto Analysis Exploration and Insights Pareto analysis has a range of applications in business and accounting: -Identifying the most significant customer complaints. If a small group of issues causes most complaints, removing these issues could positively impact customer satisfaction. -Indicating main cost categories for a specific project. -Identifying the employees that generate the most new clients. -Illustrating which group of holdings in a portfolio is responsible for most of its growth

Reports Using Multiple Visualizations Data relationships are often integrated using 1) m__e vis__s as part of a 2) re__t. Mostly 3) int___e, these visualizations provide 4) en___s ex___n op___es.

1) multiple visualizations 2) report 3) interactive 4) endless exploration opportunities.

Reports Using Multiple Visualizations A report that combines 1) pa__-t__-w__e and ti__e se__ relationships explores 2) tr__s for not only the 3) wh__, but also for each of the 4) in___l parts. A more advanced example, with multiple interactive visualizations representing 5) dif___ re___s, can illustrate this.

1) part-to-whole and time series relationships 2) trends 3) whole 4) individual 5) different relationships,

Data 1) exp___n patterns are 2) be___ practices for 3) p___ng analysis. They make us more 4) aw___ of da___ rel__s, how they can be 5) repre___ and exp___, the 6) in___ts that can be 7) ge___ed, and how they can be 8) inte___.

1) exploration 2) best 3) performing 4) aware of data relationships 5) represented and explored 6) insights 7) generated 8) integrated.

8.1 How Do We Draw Conclusions from Data Analysis?

Define Data Analysis Interpretation The process of 1) ev____ an analysis to 2) und____ and 3) ex___n its 4) me__ the 5) in___s g___d from data analysis interpretation 6) h___p us make 7) g___d bu___s de___s

1) evaluating 2) understand 3) explain 4) meaning 5) insights gained 6) help 7) good business decisions

Data Analysis Interpretation VS. Data Exploration The goal of data exploration is 1) u____g the d__a, whereas data interpretation involves 2) un___g the a____s. The 3) f___, however, is what differentiates exploration and interpretation.

1) understanding the data 2) understanding the analysis 3) focus

Data Analysis Interpretation VS. Data Exploration The first step is exploring the data to 1) un___ it

1) understand

Data Analysis Interpretation VS. Data Exploration Data analysis interpretation happens at the 1) e___ of the 2) d___a exp____ pr___s. We move from exploring for insights to 3) inte___g so we can make 4) in___d d___s.

1) end 2) data exploration process 3) interpreting 4) informed decisions

Data Analysis Interpretation Data analysis interpretation is the process of reviewing an analysis What are the 2 steps? Step 1: De___ if the a___s makes s___e Step 2: V___y that the r___s are v___d and rel___

Step 1: Determine if the analysis makes sense Step 2: Verify that the results are valid and reliable

Data Analysis Interpretation -specific questions for each step Step 1: Determine if the analysis makes sense Question 1: D__s the a__is a___r the in___d q___on and al___n with the or__al ob___e? Question 2: Were the c____t data and me____s used to p___m the a___is? Question 3: Are the re___s r___e, or are more an___s ne___ry?

Question 1: Does the analysis answer the intended question and align with the original objective? Question 2: Were the correct data and methods used to perform the analysis? Question 3: Are the results reasonable, or are more analyses necessary?

Data Analysis Interpretation -specific questions for each step Step 2: Verify that the results are valid and reliable Question 4: Does the a___is m___e what it was in___d to m___re? Question 5: Are the re___s a___e?

Question 4: Does the analysis measure what it was intended to measure? Question 5: Are the results accurate?

Data Analysis Interpretation Step 1: Determine if the analysis makes sense When interpreting our own analysis, the first two questions in Step 1 are answered during the 1) pl___g and a___g stages of the 2) M____C process: Question 1: Does the analysis answer the original question or objective of the analysis? 3) (Mo___n and Obj___) Question 2: Were the correct data and appropriate method used to perform the analysis? 4) (St___ and An___s)

1) planning and analyzing 2) MOSIAC 3) (Motivation and Objective) 4) (Strategy and Analysis)

Data Analysis Interpretation Step 2: Verify that the results are valid and reliable The remaining questions from both steps would then be answered during the 1) rep___ stage of the data analysis process 2) (Int___n). However, if someone else prepared the analysis, then it is necessary to address 3) e___h q___n for b___h st__s

1) reporting 2) (Interpretation) 3) each question for both steps

One of the most valued skills of accountants is their ability to be 1) i____ and sk___cal eva___rs of fi___ial info___n.

1) independent and skeptical evaluators of financial information

8.2 What is the Relationship Between Critical Thinking and Data Analysis Interpretation?

What are the 6 elements of critical thinking? 1. S___ 2. P____e 3. Alt____s 4. Ri___ 5. Kno___ 6. Se___-ref____ What's the acronym ?

1. Stakeholders 2. Purpose 3. Alternatives 4. Risks 5. Knowledge 6. Self-reflection SPARKS

Stakeholders: Understand the Context Before 1) int___g the analysis provided by the Celeritas marketing team, 2) co___ who the 3) st___rs are in this 4) de___n. 5) Id___ st___ers (Illustration 8.6) provides 6) i__t into the situation within which the data analysis was 7) cre___. This knowledge can help explain the 8) re___ts. 9) Fa___ to 10) ide___y the 11) sta___s means 12) po___y int____g the 13) re___s from the 14) wr__ perspective.

1) interpreting 2) consider 3) stakeholders 4) decision 5) Identifying stakeholders 6) insight 7) created 8) results 9) Failing 10) identify 11) stakeholders 12) potentially interpreting 13) results 14) wrong

Purpose: Define the “Why” of the Analysis In addition to recognizing relevant stakeholders, identify the 1) p___e of the analysis. It is easy to 2) fo____ this and immediately start 3) inte___. Be careful not to fall into this trap! We 3) c__t fully interpret an analysis until we know its 4) p___e 5) Ove____g the purpose could mean interpreting the analysis 6) inc___

1) purpose 2) forget 3) interpreting 3.1) cannot fully interpret 4) purpose 5) overlooking 6) incorrectly

Alternatives: Investigate Other Interpretations Always consider if there are 1) alt___ ways to view the 2) re___s of an 3) an___. Additionally, consider if there are 4) alt___ m__ds to 5) co___ct the analysis that were 6) n__t a__d. Finally, decide if 7) m__e a___is is 8) ne___ Thinking about these different alternatives helps 9) d___ne whether the most 10) ap___te analysis was used, which can 11) in__e con___e when interpreting the 12) r__ts

1) alternative ways 2) results 3) analysis 4) alternative methods 5) conduct 6) not addressed 7) more analysis 8) needed 9) determine 10) appropriate analysis 11) increase confidence 12) results

Risks: Consider Data, Analysis, and Bias Examine 1) al__ as__s of the analysis to identify 2) pot___l r__ks. This begins with the data and extends to potential 3) bi__s–b___h o__r o__n and those of 4) sta___s Asking certain 5) que___s can help evaluate these potential 6) ri___. Keep in mind that if you are the preparer of the analysis, then you will have 7) alr__y ad__d some of these 8) is___

1) all aspect 2) potential risks 3) biases–both our own 4) stakeholders 5) questions 6) risks 7) already addressed 8) issues

Knowledge: What We Need to Know To interpret any analysis, determine the 1) kn___ge necessary to 2) un____d it. We may not have the 3) co__ct ba___ or ex____e, so identifying the 4) re___d un___ng will reveal if we need to do additional 5) re___h

1) knowledge 2) understand 3) correct background or experience 4) required understanding 5) research

Knowledge: What We Need to Know It helps to ask these questions: -Is sp___c accounting k___ge nec____? -Would in___y kn___e be h___ul? -Is te___y kn___ge important to int___t the analysis? -Is ad__l re___h necessary, or should we s__k the h___p of an e__t?

-Is specific accounting knowledge necessary? -Would industry knowledge be helpful? -Is technology knowledge important to interpret the analysis? -Is additional research necessary, or should we seek the help of an expert?

Self-Reflection: Think About Lessons Learned Each data analysis project should include 1) ref__g on le___s learned from pr___s an___s pe___d or int___d. We can also learn from the 2) c__ent data analysis being 3) pe___d or int___d and apply that to 4) f__re data analysis projects. Reflecting on 5) pre__ e___nces helps us perform interpretations 6) qu___, th___ly, and ac____y

1) reflecting on lessons learned from previous analyses performed or interpreted 2) current 3) performed or interpreted 4) future 5) previous experiences 6) quickly, thoroughly, and accurately

8.3 How Do We Know the Analysis Makes Sense?

Recall that the first step in data interpretation is determining if the analysis makes 1) se___. This may seem like an obvious question, but it is one that is often 2) ove____. It is even more important when trying to 3) un____d an analysis of something 4) u____iar. In terms of data analysis, asking if the analysis “makes sense” means 5) conf____ the analysis has a 6) c___r mea___

1) sense 2) overlooked 3) understand 4) unfamiliar 5) confirming 6) clear meaning

Using 1) cri___l th___g sk___s, determine if the analysis answers the intended 2) qu____n and aligns with 3) ob___, if the 4) co___t d__a and me__ds were used, and if the results are both 5) r____le and su___nt for the project’s 6) pu___.

1) critical thinking skills 2) question 3) objective 4) correct data and methods 5) reasonable and sufficient 6) purpose.

Evaluate the Data and Methods Questions we can ask to determine if the analysis makes sense include thinking about 1) h___w we got the results: -Are the d___ used in the analysis re___le given the q___on/ob__e of the analysis? -Is the a___s me___d reasonable given the qu___/ob___e of the analysis?

1) how -Are the data used in the analysis reasonable given the question/objective of the analysis? -Is the analysis method reasonable given the question/objective of the analysis?

Examine the Results There are also questions that evaluate the reasonableness of the results themselves: 1. Are the re___s of the analysis re___e given what is k___n about the s__ct being a___d? 2. Are the imp___s of the analysis rea____e given what is known about the sub___ being a___d? 3. Does the analysis ad___s the n___s/co___s of the st___rs? If the answers to any of these questions is “no,” then it is likely that either m___ or a di___nt analysis is nec___y before the r___ts can be int___d.

1. Are the results of the analysis reasonable given what is known about the subject being analyzed? 2. Are the implications of the analysis reasonable given what is known about the subject being analyzed? 3. Does the analysis address the needs/concerns of the stakeholders? If the answers to any of these questions is “no,” then it is likely that either more or a different analysis is necessary before the results can be interpreted.

Determine if More Information or Analyses are Necessary Even if the 1) int____n makes 2) s__e, sometimes 3) m___e inf___on or ad___l ana___s are still 4) nec___y to thoroughly answer the 5) que___n. It is easy to believe we have the 6) inf____n ne___ry to decide on a 7) co__e of ac___n

1) interpretation 2) sense 3) more information or additional analyses 4) necessary 5) question 6) information 7) course of action

Determine if More Information or Analyses are Necessary This bias can be particularly 1) st___ng if the data analysis 2) su___s 3) prec___d id__s or conc___s about the 4) qu__n. Accountants must be 5) sk____al evaluators of 6) in___on, so we must 7) dil___ly review data analyses to be sure they provide 8) en__h in___n and su___ort to make a 9) w__l-inf___d dec___n.

1) strong 2) supports 3) preconceived ideas or conclusions 4) question 5) skeptical 6) information 7) diligently 8) enough information and support 9) well-informed decision

Determine if More Information or Analyses are Necessary What are 2 bias? -con___n bias -sel___n bias

-confirmation bias -selection bias

Determine if More Information or Analyses are Necessary Define Confirmation Bias The person 1) perf___g the 2) a__is wants to 3) p__e a 4) p___ned ass___n, so they look for 5) d___a that 6) su___rt their 7) ex___g be__f. The person 8) int___g the analysis can 9) a___o have this 10) b___s. Being 10) aw___ of the 11) pr___er’s potential biases, as well as our own, can help to 12) mit___e c___on bias.

1) performing 2) analysis 3) prove 4) predetermined assumption 5) data 6) support 7) existing belief 8) interpreting 9) also 10) bias 10.1) awareness 11) preparer's 12) mitigate confirmation

Determine if More Information or Analyses are Necessary Define Selection Bias This bias occurs when the data 1) u___d in the analysis are 2) s___ed sub___y. Selection bias is a 3) co___n if the analysis being 4) int____d is based on a 5) sa___e of d___a rather than the 6) en___e po___ion. An example is when an analysis of sales transactions is based on a sample of transactions rather than on all the transactions. If the sample is 7) n___ a g___d rep___n of the full population, then the results will be 8) bi___d

1) used 2) selected subjectively 3) concern 4) interpreted 5) sample of data 6) entire population 7) not a good representation 8) biased

8.4 How are Validity and Reliability Determined in Descriptive and Diagnostic Analyses?

Once it is clear that the analysis makes sense, the results can be assessed for 1) v___y and rel__lity. If the results are not 2) v___d, then it does 3) ___t matter how 4) “g__d” the analysis is. Sometimes it is easy to be fooled by data analyses because we take them at face value

1) validity and reliability 2) valid 3) not 4) "good"

Define Validity In the context of data analysis, it means that an analysis 1) mea___s what it is 2) su___d to 3) me___e and that it represents 4) re__

1) measures 2) supposed 3) measure 4) reality

Define Reliability data used are 1) depe___le and tru___y and the 2) m___es used in the 3) an___s are 4) co___nt and ac___e

1) dependable and trustworthy 2) measures 3) analysis 4) consistent and accurate

Define Accurate the measures used in the analysis are 1) co___t and wit____t m__es.

1) correct and without mistakes

Summary of Analyses by Analytics Area The most common types of analyses used in descriptive and diagnostic analytics, how to identify appropriate measures for each type, and how to determine if the results are valid

Descriptive analytics help to better 1) un____ the 2) d__a underlying the analysis being 3) inte___d. To assess 4) val___y and rel___y of the analyses, ensure the 5) c___ct method is used and that the data are 6) ac___. Illustration 8.13 is a summary of valid descriptive analyses based on the objective of the analysis. For an analysis to be 7) v___d, the method 8) m___t m___ch the obj___e. In a 9) re___e analysis, the measures used are 10) ac___e and consistent.

1) understand 2) data 3) interpreted 4) validity and reliability 5) correct method 6) accurate 7) valid 8) must match the objective 9) reliable 10) accurate

Descriptive Analytics Understand Categories of Data 1) Gro___g d__a into 2) ca__ies is sometimes 3) p__t of an analysis. If the analysis is based on 4) gr___ps or cat__s of data, a 5) v__d analysis would be a 6) fre___y d___on or a 7) cr__ ta__on analysis An example is when the goal is to understand sales by product.

1) Grouping data 2) categories 3) part 4) groups or categories 5) valid analysis 6) frequency distribution 7) cross tabulation analysis

Define Frequency Distribution reveals 1) h__w m__y ti__ so__ing has 2) occurred 3) wit__ a 4) gr__ or int__

1) how many times something 2) occurred 3) within 4) group or interval

Descriptive Analytics Summarize by Categories of Data Define Cross Tabulation Analysis An analysis that shows the 1) nu___er of ob___ons in a data set for 2) di___nt s___bcat___s of data

)1 number of observations 2) different subcategories

Since the objective is analyzing sales volume by model, then the fr____cy dist____n and the cr___s ta___n analyses are valid methods to use

frequency distribution and the cross tabulation

Descriptive Analytics Identify an Average Observation in the Data In addition to frequency and cross tabulation, 1) me____s of loc__n are used in descriptive analytics. When evaluating the 2) va___ty and reli____y of an analysis that includes measures of location, ensure that the 3) co___t m___e is being used

1) measures of location 2) validity and reliability 3) correct measure

Descriptive Analytics Identify an Average Observation in the Data Measures of location include the 1) me___, me___an, and m__de measures that reveal the 2) av___e or t___al obse____n in the data set

1) mean, median, and mode 2) average or typical observation

Descriptive Analytics Identify an Average Observation in the Data a mean can be affected by 1) e___e va___ in the data, whereas the median is 2) n__t af___d by these kinds of values, which are 3) ou___s.

1) extreme values 2) not affected 3) outliers

Descriptive Analytics Identify an Average Observation in the Data There are two methods to determine if there are outliers in a data set: -1) Se___h for a 2) la___ di___ce between the 3) m__n and me___an. -4) Pl___ the data to 5) vi___ly determine if there is an 6) ou___r. (which is a scatterplot)

1) Search 2) large difference 3) mean and median 4) Plot 5) visually 6) outlier

Define Scatterplot shows the 1) rel___p between 2) t___o n___cal v___es. Each 3) ob____n in the data set is 4) p___d as a point whose 5) co___tes relate to the values of the 6) t__o va___s for that observation.

1) relationship 2) two numerical variables 3) observation 4) plotted 5) coordinates 6) two variables

Descriptive Analytics Evaluate the Distribution of Data The final type of descriptive analyses discussed here are measures of 1) dis___n. 2) Dis___n refers to the 3) am___t of var___n in the data. Are the data 4) sp___d out or are they com___? In other words, how 5) f__r ap__t are all the 6) obs___s (data 7) po__s) from the 8) me__?

1) dispersion 2) dispersion 3) amount of variation 4) spread out or are they compact 5) far apart 6) observations 7) points 8) mean

Descriptive Analytics Evaluate the Distribution of Data The two most widely used measures of dispersion are 1) var___e and sta___d de__n.

1) variance and standard deviation.

Descriptive Analytics Evaluate the Distribution of Data Define Standard Deviation shows how 1) sp___d o___t the data are from the 2) m___n. It is in the 3) s___e units as the 4) m__n. Standard deviation can help determine 5) val___y and re___ty of an analysis

1) spread out 2) mean 3) same 4) mean 5) validity and reliability

Standard deviation can indicate the data points’ relationship to the mean: A low standard deviation indicates the data points tend to be 1) cl___e to the 2) me__. A high standard deviation indicates the data points are 3) sp___d o__t over a 4) lar__ range of values.

1) close 2) mean 3) spread out 4) large

Measures of location can show what the 1) av___e observation looks like or if there are 2) ou___ while measures of dispersion reveal the 3) dist___n of the data underlying the 4) a___s

1) average 2) outliers 3) distribution 4) analysis

Diagnostic Analytics If the objective of the analysis is to understand 1) w__y so____g h__s oc___d, then you will be interpreting diagnostic analytics

1) why something has occurred,

Diagnostic Analytics Assessing validity and reliability includes identifying any potential 1) an__s in the data

1) anomalies

Define Anamoly An 1) ob___n in the data that 2) dev___s from what is 3) no___al or ex___d

1) observation 2) deviatets 3) normal or expected

Key Differences between outlier and anomaly Outlier: This is a 1) le__i___e obs___n that lies an 2) a___al distance from other values in the data. Anomaly: This is an 3) il_____ observation. 3.1) Ille___e observations could be 4) mi___es or u___l occurrences that we 5) d__ n__t ex__ct to see again. These observations may be 6) ou___s that are determined to be 7) ill___e, hence an anomaly.

1) legitimate observation 2) abnormal 3) illegitimate observation 3.1) Illegitimate 4) mistakes or unusual 5) do not expect 6) outliers 7) illegitimate

Diagnostic Analytics Finding Anomalies Detecting anomalies could be the 1) pu___e of the analysis, such as when searching for unusually large transactions in an audit where fraud is suspected. However, detecting an anomaly could also be 2) unin__

1) purpose 2) unintentional

Diagnostic Analytics Examine Data Relationships Part of understanding data is knowing how the data are 1) re__d

1) related

Define Correlation Analysis shows 1) rela___s in the data by 2) me___g the 3) li___r relat___p between 4) t__o va__es

1) relationships 2) measuring 3) linear relationship 4) two variables

Diagnostic Analytics Examine Data Relationships If the goal of the analysis is to determine the 1) st____h of a rel___p between 2) ob___s of int___t (va___s), then a 3) co___n ana___s would be appropriate. Assessing 4) val___y and reli___y of a 5) co___on analysis requires knowing how to interpret the 6) co___n coe__t.

1) strength of a relationship 2) objects of interest (variables) 3) correlation analysis 4) validity and reliability 5) correlation 6) correlation coefficient.

Diagnostic Analytics Examine Data Relationships 1) Li___r correlation is measured by the 2) cor____n co___nt, also know has the 3) Pe___n pr___ct-m___nt cor___on coef___nt.

1) Linear 2) correlation coefficient 3) Pearson product-moment correlation coefficient

Diagnostic Analytics Examine Data Relationships This measure is a numerical value between 1) __ and __. The higher the absolute number, the 2) gre___r the s___h of the relationship. A positive correlation coefficient indicates that as one variable 3) inc___s, so does the 4) o___r va___le A negative correlation is an 5) inv___e relationship. As one variable 6) in___s, the other 7) de___s and vice versa

1) –1 and +1 2) greater the strength 3) increases 4) other variable 5) inverse 6) increases 7) decreases

Diagnostic Analytics Examine Data Relationships Correlation is a 1) v__id measure for examining 2) lin___ r___ips between variables in data, and the interpretation of the correlation should be based on the 3) cor___n c___nt. Correlation analysis is 4) rel___e if the correlation coefficients are 5) co___nt and ac___e rel___e to the data being 6) ana___d It is reliable if we believe the correlation coefficients are 7) co___t and acc___e.

1) valid 2) linear relationships 3) correlation coefficient 4) reliable 5) consistent and accurate relative 6) analyzed 7) consistent and accurate

Define Trend Analysis A statistical tool that uses 1) his___l d__a to identify 2) pa__s It can explain 3) w___y som___ is ha___g

1) historical data 2) patterns 3) why something is happening

Define Trendline In a trend analysis, it indicates the general 1) co___e or ten___y of the data and is created using 2) hi__l d__a to 3) es___e a li___.

1) course or tendency 2) historical data 3) estimate a line

Diagnostic Analytics Identify Patterns Examining trends helps detect 1) pat___s and re___ips, which can identify 2) opp___s or po___l th__ts to a bu___ss. The best way to identify 3) tre__s is to graph the 4) d__ over t__.

1) patterns and relationships 2) opportunities or potential threats to a business 3) trends 4) data over time

8.5 How are Validity and Reliability Assessed in Predictive and Prescriptive Analyses?

Predictive Analytics There are many types of predictive analytics, but they all have the goal of 1) pre___g a fu__e o__e

1) predicting a future outcome

Predictive Analytics In the accounting profession the most common predictive analysis is 1) li___r regre__n

1) linear regression

Define Linear Regression which is a tool for building 1) ma___l and st___al m__s to explain the 2) rel___p between a 3) dep__ v___le and one or more 4) ind___t v___es

1) mathematical and statistical models 2) relationship 3) dependent variable 4) independent variables

Predictive Analytics Modeling Relationships Predictive analytics build a model to help 1) pr___ or be__r un__d a phenomenon.

1) predict or better understand

Predictive Analytics Modeling Relationships Remember that an analysis is 1) va__d if it measures what it is 2) sup__d to me___e and if it also represents 3) re___y

1) valid 2) supposed to measure 3) reality

Predictive Analytics Modeling Relationships The next step is confirming the model is 1) rel___e. Recall that reliability means that the measures used in the analysis are 2) ac___e and cons____t and the data are 3) dep___e and t___y

1) reliable 2) accurate and consistent 3) dependable and trustworthy

Predictive Analytics Modeling Relationships Here, we focus on the key statistics and output that help evaluate and interpret regression model output. Keep in mind that if the model is not valid, then it does not matter if it is reliable. An accurate and consistent measure of the model does not mean the model represents reality. So, this first step of determining if the model variables are 1) lo___al is important

1) logical

Predictive Analytics Reliability of the Regression Model Define Adjusted R Square (R^2) Explains how well the regression 1) l__e fits the data. The adjusted R2 is a statistic that modifies the value of R2 by incorporating the sample size and the number of 2) inde____ var___. In general, use adjusted R2 to evaluate a multiple regression model. The closer the R2 is to 3) __, the 4) be__r the f__ of the regression 5) li___ to the data.

1) line fits 2) independent variables 3) 1 4) better the fit 5) line

Predictive Analytics Reliability of the Regression Model Define Standard Error Represents the 1) va__y of the observed 2) de___nt va__le values from the values that are predicted by the model. In other words, it 3) co__es the ac___l de___ va__le to the 4) pr___d v__e that the model provides. If the data are 5) clu___d cl__e to the regression line, then the 6) sta___d er___r will be 7) s__ll. If the data are more 8) sca___d, then the standard error will be 9) la__er. A 10) s__ll standard error is 11) op___l.

1) variability 2) dependent variable 3) compares the actual dependent variable 4) predicted value 5) clustered close 6) standard error 7) small 8) scattered 9) larger 10) small 11) optimal

The last section of the regression summary output provides information to create the 1) eq__n that 2) p___ts the 3) de___t variable. If the 4) ad___d __ s__e and 5) sta__d error are 6) ac___e and the model is 7) si___t, then we can interpret the equation of the model.

1) equation 2) predicts 3) dependent 4) adjusted R square 5) standard error 6) acceptable 7) significant

The intercept and coefficients of the model represent the 1) eq___n of the l__e that 2) b__t f__s the data. The key statistic to analyze in this section is the 3) __-v___e for each of the 4) ind___nt variables. Like the F statistic, the p-value provides a test of significance. It is a test as to whether the 5) ind___nt variable 6) im___s the ability of the model to 7) p___t the 8) de____t variable. A p-value of 9) __ or l___s is considered 10) sig___

1) equation of the line 2) best fits 3) p-value 4) independent 5) independent 6) improves 7) predict 8) dependent 9) 0.05 or less 10) significant

Prescriptive Analytics Prescriptive analytics prescribe 1) w__t sh__d h___en to ac__ve de___d r___s. The most common prescriptive analytics in accounting are 2) w___t-i___ a__es and op__n m___s

1) what should happen to achieve desired results 2) what-if analyses and optimization models

Prescriptive Analytics The analysis must be 1) v__id and r___le. Since a prescriptive model prescribes 2) ac___n, it is crucial to 3) ve__y that 4) i___ts and ou__s of the model are 5) va__ and re___e to avoid making 6) p___r bu__ess dec___s.

1) valid and reliable 2) action 3) verify 4) inputs and outputs 5) valid and reliable 6) poor business decisions

Define What-If Analysis A 1) sp___et model that evaluates how 2) ch___s to 3) va__s and as__ns affect an 4) o___e an 5) ea___ way to 6) c___e va___s in a spreadsheet and 7) rec___e the outputs.

1) spreadsheet 2) changes 3) values and assumptions 4) outcome 5) easy 6) change values 7) recalculate

9.1 How Do We Tell a Data Story?

Effectively 1) com___g these findings requires 2) d_a lite__y

1) communicating 2) data literacy

Define Data Literacy which is the ability to 1) und__d and co___ate d__a

1) understand and communicate data

Communicate Effectively The final step in the data analysis process is 1) sum___g the project’s 2) fin___s and co___ting them to the 3) in___d audience. This requires 4) expl___g the 5) me__g of the data by writing 6) me____s or rep__s or pre___g pre____s that 7) e___in the data analysis results 8) cle___ and co____ly. This is 9) n__t easy, and it takes practice

1) summarizing 2) findings and communicating 3) intended 4) explaining 5) meaning 6) memos or reports or preparing presentations 7) explain 8) clearly and concisely 9) not

Communicate Effectively Effectively 1) rep___g the results of data analysis requires being 2) aw__e of the aud__e, foc___g on the me___e, putting that me__e in c___xt, and making sure it is cle___y presented as an e__ng story

1) reporting 2) aware of the audience, focusing on the message, putting that message in context, and making sure it is clearly presented as an engaging story

Understand the Audience 1) Und___g to whom we are 2) cm___g is 3) cr___l. The 4) au___ce (or re__r) 5) m__t be pr__d with enough 6) b___nd and exp____n to follow the 7) pr_____n.

1) Understanding 2) communicating 3) critical 4) audience (or reader) 5) must be provided 6) background and explanation 7) presentation

Focus on the Message Accountants are comfortable reading and interpreting numbers, so it is easy to focus solely on the 1) nu___rs when communicating data analyses. However, an audience is likely more 2) int___d in the relationship between the 3) nu__s and the me__ge. For example, if the objective of the analysis is to identify expense trends, ensure the communication explains the trends and does not only focus on the amounts

1) numbers 2) interested 3) numbers and the message

Put it in Context The third suggestion for communicating effectively is to put the data in 1) co__t, or per___.

1) context, or perspective

Put it in Context There are two aspects to context when communicating data analyses results: 1. The context of the 1) ov__ll pur__e of the 2) a___is. Is the 3) pur__e to in___m or pe__de the 4) au___e? 2. The context of the 5) ind__al ana___s. Are the 6) ana__s based on all the 7) co___y data or just 8) o__e dep___t? Are the figures in whole dollars or in millions?

1) overall purpose 2) analysis 3) purpose to inform or persuade 4) audience 5) individual analyses 6) analyses 7) company data 8) one department?

Put it in Context When communicating data analysis results, give the audience the 1) in___n they need to 2) un___and the co___t of the analysis

1) information 2) understand the context

Strive for Clarity The fourth suggestion for effective data analysis communication is to make 1) su__ the co___ion is e__y to und___d.

1) sure the communication is easy to understand

Strive for Clarity Do this by 1) cle___y ex___g the d___a and the 2) res__s in the 3) nar___e of the 4) com____n and including 5) eff__e v___ons. The last suggestion for effective communication is to 6) en__e the audience with a memorable story

1) clearly explaining the data 2) results 3) narrative 4) communication 5) effective visualizations 6) engage

Data Story Elements There are three elements to a data story -d__a, nar___e, and vi___ls

data, narrative, and visuals

Data Story Elements The author of Effective Data Storytelling, Brent Dykes, describes how these elements combine to 1) ex___n, enli___n, and en__e the audience: The 2) inte___n of data and 3) nar___e explains the data 4) st__y. The story’s 5) nar___e provides the 6) co__t and com___ry needed to 7) und___d the 8) re___ts of the 9) an__is. It provides 10) str___e to the data and 11) gu___s the reader through the 12) me___g of the analysis

1) explain, enlighten, and engage the audience 2) intersection 3) narrative 4) story 5) narrative 6) context and commentary 7) understand 8) results 9) analysis 10) structure 11) guides 12) meaning

Data Story Elements Data also intersects with 1) vis__s to 2) en__n the reader with 3) ins___ts. 4) Visu___g the data reveals 5) pat___s or t__ds that may have gone 6) un__ed without the help of 7) vi___s. In fact, humans process visual images 60,000 times faster than text

1) visuals 2) enlighten 3) insights 4) Visualizing 5) patterns or trends 6) unnoticed 7) visualizations

Data Story Elements Finally, 1) co___g nar___e with 2) vi___s en___es the au___e in the 3) st__y. A good 4) st___ can hold the 5) at___n of the 6) re___ and 6) inc___s the likelihood of action

1) combining narrative 2) visuals engages the audience 3) story 4) story 5) attention 6) reader 6) increases the likelihood

Data Story Structure Freytag’s pyramid It is one of the most 1) t__ht dr___c structures in the world.

1) taught dramatic

Data Story Structure Exposition - introduce the problem or issue Rising Action - the subject of the analysis is explored at a deeper level Climax - the main finding or insight is shared. This is the “aha moment” of the story Falling Action - share the solution Resolution - conclude the story and provide next steps

9.2 What are the Steps for Creating Effective Data Visualizations?

Define Data Visualization The graphical 1) re___n of data and 2) in___n to provide 3) me___g and ins__s during the 4) da__a an___s pr__s A well-designed visualization conveys the results of an analysis 5) cle__y and conci__y.

1) representation 2) information 3) meaning and insights 4) data analysis process 5) clearly and concisely

Verify the Data The saying “garbage in, garbage out” is as relevant to data analysis communication as it is to performing data analyses. 1) Inc___ct d__a leads to 2) inc___t vis___ns. To avoid this, data should have the attributes of 3) ac__cy, com___s, co__ncy, fre___ss, and tim___s

1) Incorrect data 2) incorrect visualizations 3) accuracy, completeness, consistency, freshness, and timeliness

Accurate Data Accurate data are 1) f__e from er___s. They are 2) re___le and rep___ve of the 3) pro__ or is__e being 4) vis___d If the data are 5) con__d to be 6) fr__ of er___r, then they are also 7) rel___e

1) free from errors 2) reliable and representative 3) problem or issue 4) visualized 5) confirmed 6) free of error 7) reliable

Complete and Consistent Data Data are complete when there are 1) __o mi___g d__a.

1) no missing data.

Consider the Audience What are the 4 types of audiences? 1. No___e 2. Ma___ial 3. Exp___t 4. Exe___e

1. Novice 2. Managerial 3. Expert 4. Executive

Consider the Audience Novice Description -1) has n___r enc____d the info___on What They Want -2) en___h de___l to g__n und__ng

1) has never encountered the information 2) enough detail to gain understanding

Consider the Audience Managerial Description -1) has some kn___ge of the to__c What They Want -2) a___ble re___lts

1) has some knowledge of the topic 2) actionable results

Consider the Audience Expert Description -1) has d___p k___dge of the to__ic What They Want -2) inv___tion and dis___ry

1) has deep knowledge of the topic 2) investigation and discovery

Consider the Audience Executive Description -1) has a br___d, h__h-le__l kno___ge of the topic What They Want -2) only the m__t im__nt ins__ts

1) has a broad, high-level knowledge of the topic 2) only the most important insights

Novice Audiences A novice audience, which can be 1) int___al or ex___al to the organization, needs enough 2) ba__nd inf__ion to 3) un___nd the re__s. An analysis for an 4) e___nal client or one presented to an 5) int___l department 6) un___r with the topic would both have 9) no__e audiences.

1) internal or external 2) background information 3) understand the results 4) external 5) internal 6) unfamiliar 7) novice

Managerial Audiences A managerial audience generally has 1) s__e kno___e of the topic, so 2) det__d ba__d info__n may be 3) unn___. However, this audience is looking for 4) ac___le results, so the 5) vi___on should include 6) reco___ns for act__s based on the 7) r__s

1) some knowledge 2) detailed background information 3) unnecessary 4) actionable results 5) visualization 6) recommendations 7) results

Expert Audiences Because experts already have 1) d___p k__ge about the subject of the analysis, this type of audience 2) d__s n__t need 3) ba__ b___nd inf__ Instead, they are interested in the 4) inve___e as___t of the 5) st__y.

1) deep knowledge 2) does not 3) basic background information 4) investigate aspect 5) story

Executive Audiences An audience composed of executives will be interested in only the 1) m__t im__nt insi__s. Discuss the 2) im__nt in__hts fi__t, then discuss the 3) s__rt for those 4) in___t

1) most important insights 2) important insights first 3) support 4) insight

Define the Objective Once data are verified and the audience has been considered, the next step is to 1) und___d the o__ve of the analysis, or the 2) qu___n/is___e to be addressed in the 3) visu__n

1) understand the objective 2) question/issue 3) visualization

Define the Objective The 1) obj___e of the analysis helps determine the 2) t__es of visualizations that are 3) ap___e. It might be to show 4) comp__n, rel____ips, distr__s, tr__ds, or c___ons of the da__.

1) objective 2) types of visualizations 3) appropriate 4) composition, relationships, distributions, trends, or comparisons of the data

Visualization Objectives Composition Explanation -Show how part of the data compares to the whole Example -How much revenue has each region contributed to total revenue?

Visualization Objectives Relationship Explanation -Show how the data are related Example -Is there a relationship between machine hours and maintenance expense?

Visualization Objectives Distributions Explanation -Reveal how data are spread out or grouped Example -Are there transactions that might be considered outliers?

Visualization Objectives Trends Explanation -Display patterns in the data Example -Is there a seasonal pattern in revenue?

Visualization Objectives Comparisons Explanation -Compare values between groups of data Example -How does the revenue for 2024 compare to 2025 by product?

Visualization Decision Tree for Showing Composition, Relationships, and Distributions What is the objective of the analysis ? -Show co___on -Show r___hips -Show di___ons

-Show composition -Show relationships -Show distributions

Visualization Decision Tree for Showing Composition, Relationships, and Distributions What is the objective of the analysis ? Show composition -ar__ chart -p__ chart -st___d b__r chart

-area chart -pie chart -stacked bar chart

Visualization Decision Tree for Showing Composition, Relationships, and Distributions What is the objective of the analysis ? Show Relationships -bu___e chart -sca____t

-bubble chart -scatterplot

Visualization Decision Tree for Showing Composition, Relationships, and Distributions What is the objective of the analysis ? Show Distributions -his__m chart -li__ chart -sc___lot

-histogram chart -line chart -scatterplot

Visualization Decision Tree for Showing Trends or Comparisons What is the objective of the analysis ? -Ind__e t___ds -Com__s

-Indicate trends -Comparisons

Visualization Decision Tree for Showing Trends or Comparisons What is the objective of the analysis ? Indicate Trends -l__e chart -co__n chart

-line chart -column chart

Visualization Decision Tree for Showing Trends or Comparisons What is the objective of the analysis ? Comparisons -It___s -Ov___e

-Items -Overtime

Visualization Decision Tree for Showing Trends or Comparisons What is the objective of the analysis ? Comparisons -Items -b__r chart -c___n chart

-bar chart -column chart

Visualization Decision Tree for Showing Trends or Comparisons What is the objective of the analysis ? Comparisons -Overtime -l__e chart -co__n chart

-line chart -column chart

9.3 What Are the Characteristics of Effective Visualizations?

Use Principles of Visual Perception The human brain prefers 1) sim___y and order in 2) v___l im___s because it 3) pr__nts us from becoming 4) ove___d with 5) info___. We can process simple patterns 6) f___er than 7) co___x patterns. 8) Ge___t is an area of psychology that was the 9) fou___n for the modern study of perception

1) simplicity and order 2) visual images 3) prevents 4) overwhelmed 5) information 6) faster 7) complex patterns 8) Gestalt 9) foundation

Define Gestalt Principles of Visual Perception Principles that 1) de__e how 2) hu__ns g___n m__ng from the 3) st___li ar__d them

1) describe 2) humans gain meaning 3) stimuli around

Gestalt Principles of Visual Perception These principles address the 1) na___l n___d for hu___s to f__d or__r. We can create 2) ef___e vis____s by considering 3) re___t principles such as 4) con___y, si___y, pr___ity, and f__al p__nt

1) natural need for humans to find order 2) effective visualizations 3) relevant 4) continuity, similarity, proximity, and focal point

Continuity Define Law of Continuity The 1) Ge___t p___le of 2) visual perc___n which explains how 3) pe___e tend to 4) pe___e any l__e as 5) con___g in its 6) e___ed dir___n and that 7) o___ts ali__d with 8) e___h o___er are perceived as a 9) si__e p__h or sh___

1) Gestalt principle 2) visual perception 3) people 4) perceive any line 5) continuing 6) established direction 7) objects aligned 8) each other 9) single path or shape

Continuity We follow 1) li__s, c__es, or a se__ce of sh__s to determine if there is a 2) re___p between the 3) el___s 4) Eff___e d___a vis___ns will arrange 5) vi___l ob__ts in a 6) li__ to 7) sim__y gr___ng and com___n

1) lines, curves, or a sequence of shape 2) relationship 3) elements 4) Effective data visualizations 5) visual objects 6) line 7) simplify grouping and comparison

Similarity Define Law of Similarity The 1) Ge___t p__le of 2) vi__l per__n that describes how 3) si__r el__ts tend to be perceived as a 4) un__d gr__p

1) Gestalt principle 2) visual perception 3) similar elements 4) unified group

Similarity Items similar in 1) co__r, s__pe, s___e, or lo___n e__ke the perception that they belong to the 2) s__e g__p Using the law of similarity can help viewers 3) id___fy the gr__s to which the displayed data 4) be___g

1) color, shape, size, or location evoke 2) same group 3) identify the groups 4) belong

Proximity Define Law of Proximity The Gestalt principle of visual perception that states people will 1) pe___e vi___al el__nts based on how 2) clo___y they are pos___d to 3) o__e an___r

1) perceive visual elements 2) closely they are positioned 3) one another

Proximity The law of proximity helps a viewer make 1) se__e of a la__e s_t of data very qu___y.

1) sense of a large set of data very quickly.

Focal Point Define Law of Focal Point The Gestalt principle of visual perception that refers to how we are 1) m__e at___e to wh__r sta__ds o__t vi___ly.

1) more attentive to whatever stands out visually.

Consider Preattentive Attributes Define Preattentive attributes 1) Vi___l pr__s that people 2) no__e w__ut re___g it

1) Visual properties 2) notice without realizing it

Consider Preattentive Attributes Size -if 1) ele__ts of a 2) vis___n vary in 3) s__e, the audience 4) as___s those 5) s___e d___ces matt___er. In other words, 6) re___e s__e will be 7) inter___d as 8) rel___ imp___ce. This is true in 9) in__al vi__ons as well as in 10) das___s

1) elements 2) visualization 3) size 4) assumes 5) size differences matter 6) relative size 7) interpreted 8) relative importance 9) individual visualizations 10) dashboards

Consider Preattentive Attributes Size While a visualization’s relative 1) s__e indicates its 2) im___e, the size of the 3) t___t wi__in the 4) ill___ion, relative to other 5) t__t, will also indicate 6) im__ce.

1) size 2) importance 3) text within 4) illustration 5) text 6) importance

Consider Preattentive Attributes Color Color can make visualizations more 1) ef___e. There are three ways to use color in visualizations 1. Se___al 2. Di____g 3. Ca___al

1) effective 1. Sequential 2. Diverging 3. Categorical

Consider Preattentive Attributes Color 1. Sequential Color is ordered from l___ to h__

low to high

Consider Preattentive Attributes Color 2. Diverging There are 1) t__o se__tial c__rs with a 2) ne__al mi___t. This type of scale is useful for showing 3) ga__s and lo___s

1) two sequential colors 2) neutral midpoint 3) gains and losses

Consider Preattentive Attributes Color 3. Categorical There are 1) cont__g colors for 2) in___l co___on. This is a 3) co___on use for color when comparing 4) cat___s

1) contrasting 2) individual comparison 3) common 4) categories

Consider Preattentive Attributes Color Regardless of its tone, color should be used both consistently and sparingly: -If the visualization includes color, variables should be indicated by the 1) s__e co__r to avoid 2) co___on. -Interpreting 3) t__ m___y 3) co___rs can 4) ov___elm the audience, so only add color that makes it 5) e__er to inte___t the visualization.

1) same color 2) confusion 3) too many 3) colors 4) overwhelm the audience 5) easier to interpret

Consider Preattentive Attributes Color Finally, consider users with colorblindness when creating visualizations. Color blindness affects 8% of men and 0.5% of women. The most common color blindness is the inability to distinguish between red and green shades, so avoid using red and green in the same visualization.

Consider Preattentive Attributes Position Item 1) pla___t in 2) vis___s and das___ds matters. Most people begin viewing a visualization from the 3) t__p l__t c___er and then s___n in a 4) zi__-z___g mo___n through the entire visualization Therefore, be sure to put the most 5) imp___t inf___on at the 6) t__p le__t of the visualization.

1) placement 2) visualizations and dashboards 3) top left corner and then scan 4) zig-zag motion 5) important information 6) top left

Consider Preattentive Attributes Titles Finally, include a 1) fa___al and neu__l ti__e. Avoid using 2) un__ry des___e w__ds. The title should be a 3) noun that represents what was 4) mea__d and __en.

1) factual and neutral title 2) unnecessary descriptive words 3) noun 4) measured and when

Avoid Clutter After considering principles of perception and preattentive attributes, consider 1) vi__al clu__r in visualizations. 2) Cl___r is the 3) en__y of a 4) g__d vi__tion. The more 5) cl___d the visualization, the 6) har___r it is for the viewer to 7) un__nd the results. Remove any 8) n__n-d__a related details from the visualization and check for 9) red__t in___tion

1) visual clutter 2) Clutter 3) enemy 4) good visualization 5) cluttered 6) harder 7) understand the results 8) non-data 9) redundant information

Before and After Reducing Visualization Clutter Chart Cluttered Visualization -T___ many co__rs are n__t adding to the m__ng of the gr__h because the models are also dir__y la___d. Reduced Visualization -The vi___l is a si__e co__r.

Cluttered Visualization -Too many colors are not adding to the meaning of the graph because the models are also directly labeled. Reduced Visualization -The visual is a single color.

Before and After Reducing Visualization Clutter Chart Cluttered Visualization -A__s l__ls are provided, but the audience m__t g__s the ex__t a___nt. Reduced Visualization -La__l e__h b__r dir___y with the sales volume am___t and rem__ the a__s. -Sales volume is in the ti__e of the graph, so r___ve the a__s he__r.

Cluttered Visualization -Axis labels are provided, but the audience must guess the exact amount. Reduced Visualization -Label each bar directly with the sales volume amount and remove the axis. -Sales volume is in the title of the graph, so remove the axis header

Before and After Reducing Visualization Clutter Chart Cluttered Visualization -An___n t___t b___es identify the bes___g model Reduced Visualization -S__t the d__a from hi___st to lo___t

Cluttered Visualization -Annotation text boxes identify the bestselling model Reduced Visualization -Sort the data from highest to lowest

Before and After Reducing Visualization Clutter Charter Cluttered Visualization -Bac____d c___r of the graph is un___ary. Reduced Visualization -R___ve bac___d co__r. -Re___e g___es since we are rem___g the a__is and lab___ng the col__s directly.

Cluttered Visualization -Background color of the graph is unnecessary. Reduced Visualization -Remove background color. Remove gridlines since we are removing the axis and labeling the columns directly.

Before and After Reducing Visualization Clutter Charter Cluttered Visualization -Models are listed in alp___al order. There is n__ ind___on which model the audience should fo__s on. Reduced Visualization -The bestselling model is hig___d and the others are gr__d o__t to draw at__ion to it.

Cluttered Visualization -Models are listed in alphabetical order. There is no indication which model the audience should focus on. Reduced Visualization -The bestselling model is highlighted and the others are grayed out to draw attention to it.

Use Visualization-Specific Best Practices Area Chart -Do n__t use with more than fo___ cat___es to avoid con___n and clu__er. -Start the _-a__s at ze__o. -Put h___y va___e data on the t___p and data with l__w va__ty on the bo___m.

-Do not use with more than four categories to avoid confusion and clutter. -Start the y-axis at zero. -Put highly variable data on the top and data with low variability on the bottom.

Use Visualization-Specific Best Practices Bar and Column Chart -Use ho___l bars if there are more than __ categories or l___g category la___ls. -Use ho___al la__ls for better rea__ty. -Sp___e bars app__ely and con___ly. -Use co___r s___gly, or as an ac__nt. -Always have a z__o baseline (the y-a__s begins at ze___). -Compare __–__ categories with ve___l columns.

-Use horizontal bars if there are more than 7 categories or long category labels. -Use horizontal labels for better readability. -Space bars appropriately and consistently. -Use color sparingly, or as an accent. -Always have a zero baseline (the y-axis begins at zero). -Compare 2–7 categories with vertical columns.

Use Visualization-Specific Best Practices Column Chart -La__l bu___es and make sure they are vi___e. -Sc___e bubble s__e by a__a and not di__r. -Do n__t use bubbles if they are all si___r in si__e.

-Label bubbles and make sure they are visible. -Scale bubble size by area and not diameter. -Do not use bubbles if they are all similar in size.

Use Visualization-Specific Best Practices Histogram Chart -Use a z__o b___ne. Choose an ap___te n___er of bi__s: -Bins are nu___rs that represent the in__ls into which the data will be gr___ed. -Bins define the gro___s used for the freq___y dis___ution. -Generally, include between ___–__ bins.

-Use a zero baseline. Choose an appropriate number of bins: -Bins are numbers that represent the intervals into which the data will be grouped. -Bins define the groups used for the frequency distribution. Generally, include between 5–15 bins.

Use Visualization-Specific Best Practices Line Graph -Ti___ runs from l__t to r___ht. Be con___t when plotting t__e poi__s. -Use so__d li__s, not d__d. -Use a z__o ba___ne. -Do n__t p__t more than f__ur lines. Use mu__e ch___ts, instead.

-Time runs from left to right. Be consistent when plotting time points. -Use solid lines, not dotted.Use a zero baseline. -Do not plot more than four lines. Use multiple charts, instead.

Use Visualization-Specific Best Practices Pie Chart -Most im___ul with s__ll data sets. -Best to use when showing dif___s wit___n gr__s based on o__e variable. -Ensure the data adds to __%. L__it the chart to a ma___um of fi___ s___ts. -Start the fi__t segment at __ o’clock position

-Most impactful with small data sets. -Best to use when showing differences within groups based on one variable. -Ensure the data adds to 100%. Limit the chart to a maximum of five segments. -Start the first segment at 12 o’clock position

Use Visualization-Specific Best Practices Stacked Bar Chart -Can be v__al or ho___ntal. -Follow s__e b__t practices as b__r ch__ts. -Used to show c___ns of su___ents between cat___s. -Best used when there are n__t too m__y su__ents. -Consider using a __% stacked bar to make co___ons between the b__s and sub__nents easier

-Can be vertical or horizontal. -Follow same best practices as bar charts. -Used to show comparisons of subcomponents between categories. -Best used when there are not too many subcomponents. -Consider using a 100% stacked bar to make comparisons between the bars and subcomponents easier

Use Visualization-Specific Best Practices Scatter Chart -Data set should be in p__rs with an ind___nt variable (__-axis) and a de__t variable (__-axis). -Use if or__r is n__t re___t–otherwise use a l__e graph. -Do n__t u__e if there are only a f___w pi___s of data or if there is n__ cor___tion.

-Data set should be in pairs with an independent variable (x-axis) and a dependent variable (y-axis). -Use if order is not relevant–otherwise use a line graph. -Do not use if there are only a few pieces of data or if there is no correlation.

9.4 What Makes Data Visualizations Misleading?

Use Visualization-Specific Best Practices Tree Map -App___te when p__e com__ns are n__t important. -Use br__ht, co__g colors so that each box is easily de__ned. -L___el boxes with t__t or nu__rs.

-Appropriate when precise comparisons are not important. -Use bright, contrasting colors so that each box is easily defined. -Label boxes with text or numbers.

Using 1) b___t pr___s is the 2) f__t step for 3) mit___ing the r__k of creating a 4) mi__g data visualization. The second is 5) dev___g an aw__ess of how 6) vis__ns can 7) m___d to av___d making those m__kes

1) best practices 2) first step 3) mitigating the risk 4) misleading data visualization 5) developing an awareness 6) visualizations 7) mislead to avoid making those mistakes

1) Et__s and d__a vi__ion best practices are intertwined. Because visualizations can significantly 2) i___ce how data are used to make 3) decisions, there is an 4) et__cal obligation to not 5) m__ad the viewer

1) Ethics and data visualization 2) influence 3) decisions 4) ethical 5) mislead

Visualizations may mislead by -om___g the ba__ne, -mani___ng the __-axis -s___ly pi___ng the d__a -using the w__g type of g__ph -going ag__st co___ons.

-omitting the baseline, -manipulating the y-axis -selectively picking the data -using the wrong type of graph -going against conventions.

Omitting the Baseline Al___s gr__h data with a z__ro bas___e to avoid misl___g the audience.

Always graph data with a zero baseline to avoid misleading the audience.

Manipulating the Y-Axis Like omitting the baseline, 1) ma__ng the 2) sc__e on the 3) __-axis can 4) af___t how the data are 5) int__ed. 6) Exp___g or co___ng the 7) scale on the 8) __-axis can make 9) ch___es in the data seem 10) m__e or le__s sign__nt.

1) manipulating 2) scale 3) y-axis 4) affect 5) interpreted 6) Expanding or compressing 7) scale 8) y-axis 9) changes 10) more or less significant.

Going Against Conventions A general standard in data visualization is that 1) da__r colors indicate 2) hi__r numbers in a color scale

1) darker 2) higher

Selectively Choosing the Data Including only 1) s__e data po__s in a visualization may also create a 2) fa___e im___n of the data

1) some data points 2) false impression

Using the Wrong Type of Graph Sometimes choosing the 1) wr__g ty__e of gr__h can make it 2) di___lt to 3) inte___t the data and 4) res__s in 5) mi___ng the viewer. A visualization that is 6). n__t ap___ate for the 7) t___e of d__a or an___sis res___ts being re___ed makes it 8) dif___lt for the audience to 9) in___t the 10) me__e.

1) wrong type of graph 2) difficult 3) interpret 4) results 5) misleading 6) not appropriate 7) type of data or analysis results being reported 8) difficult 9) interpret 10) message

9.5 How are Data Used in Live Presentations?

Because accountants are often asked to present data 1) sto___s, 2) o___l com___tion ski___s are essential for a successful career. Results of data analyses are commonly 3) com__ed via a live 4) pr__on to the intended audience, either 5) i__-pe___n or in a vi__al meeting. These presentations often include 6) int___ve data visualizations

1) stories 2) oral communication skills 3) communicated 4) presentation 5) in-person or in a virtual 6) interactive

Best Practices for Live Presentations Whether presenting to a live audience or virtually, a presentation should be 1) cl___r and en___ng

1) clear and engaging

Best Practices for Live Presentations 1. E___re the audience can s__e the d__a. A vi___ion that looks f__e on a screen may be too s___ll for someone in the b__k of the room to see. 2. F__us on the po___ts the data illu___es by explaining the me___g of the data analysis. Stating f___ts without sh__g how they tell a st___y will leave the audience con__d. 3. Share o__e ma__r p__t on each ch___t to avoid ove___g with de__ls. Rather than showing se___al visu___ns, on___ sh___e those that support the data st__y. 4. La___l chart co___nts clearly. Re___w them and ask: “If I was seeing this for the fi___t time, would I un___d it?”

1. Ensure the audience can see the data. A visualization that looks fine on a screen may be too small for someone in the back of the room to see. 2. Focus on the points the data illustrates by explaining the meaning of the data analysis. Stating facts without showing how they tell a story will leave the audience confused. 3. Share one major point on each chart to avoid overwhelming with details. Rather than showing several visualizations, only share those that support the data story. 4. Label chart components clearly. Review them and ask: “If I was seeing this for the first time, would I understand it?”

Best Practices for Live Presentations 5. Vis__ly hi___ht the “__-__a” point, or the in___t or di___y, in the st__y. S__rt pr__ers explain the re___nce of the “___-__a” moment both or___ly and with a vi__al hig___t in the ch__t or gr__h. 6. Slide ti___es should rei___ce the data’s point. Av___d ge__ic ti___s and cho___e those the audience will no__e and rem___ber. 7. Pr___nt to the au___nce by lo___ng at them and not re___g from a sl__e pr___n. En__g the audience requires con__g with them, and the b__t way to do that is by foc__g on t__m rather than the sli__s.

5. Visually highlight the “a-ha” point, or the insight or discovery, in the story. Smart presenters explain the relevance of the “a-ha” moment both orally and with a visual highlight in the chart or graph. 6. Slide titles should reinforce the data’s point. Avoid generic titles and choose those the audience will notice and remember. 7. Present to the audience by looking at them and not reading from a slide presentation. Engaging the audience requires connecting with them, and the best way to do that is by focusing on them rather than the slides.

Best Practices for Live Presentations Following these seven best practices will help create a powerful presentation. However, the work does not stop there, as an effective oral presentation requires 1) pla___ng and pr__ice

1) planning and practice

Creating Interactive Data Visualizations A powerful way to 1) com__te data analysis results in a 2) l___e pres___n is by inviting the 3) au___ce to ex___re the 4) rep___tion of the data

1) communicate 2) live presentation 3) audience to explore 4) representation of the

Define Interactive Data Visualization A data visualization in which users can 1) ex___re, ma___te, and int___ct with gr___al repr__ions of da__a

1) explore, manipulate, and interact with graphical representations of data

Interactive Data Visualization They help 1) co__ct the 2) pre___er with the 3) au__e by drilling down into the 4) da__ based on the audience’s 5) n__ds. This allows the 6) presenter to 7) qui___ly an__er any que___s from the audience

1) connect 2) presenter 3) audience 4) data 5) needs 6) presenter 7) quickly answer any questions

Creating Interactive Data Visualizations Besides following best practices for creating data visualizations and telling a story with data, 1) int___ve da__ vi___ions should provide an 2) e___y and in___e way for the 3) u__r to in___ct with the data.

1) interactive data visualizations 2) easy and intuitive 3) user to interact with the data

Creating Interactive Data Visualizations The benefit of interactive data visualization is that it allows 1) eng___nt with the data. An interactive visualization helps users: -2) Qu__ly ide__fy tr__ds -3) Ef__y id__y rela___ips -4) Provide us__l data st___g -5) Si__y co__ex data

1) engagement 2) Quickly identify trends 3) Efficiently identify relationships 4) Provide useful data storytelling 5) Simplify complex data

Creating Interactive Data Visualizations Keep in mind that interactive data visualization is 1) n__t li__d to 2) l__e pres___ns. In general, the 3) m__e inte___ve the vi__al, the 4) mo__e en___ed the u__r will be regardless of the 5) com___on medium.

1) not limited 2) live presentations 3) more interactive the visual 4) more engaged 5) communication

EXAM 3 Flashcards

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