Exam 1 Flashcards

(55 cards)

1
Q

strategic decisions

A
  • involve higher level issues concerned with the overall direction of the organization
  • define the organization’s overall goals and aspirations for the future
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2
Q

tactical decisions

A
  • concern how the organization should achieve the goals and objectives set by its strategy
  • are usually the responsibility of midlevel management
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3
Q

operational decisions

A
  • affect how the firm is run from day to day

- are the domain of first line managers who are closest to the customer

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4
Q

common approaches to decision making

A
  • tradition
  • intuition
  • rules of thumb
  • sacred cow
  • using the relevant data available
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5
Q

business analytics

A
  • scientific process of transforming data into insight for making better decisions
  • used for data-driven or fact-based decision making, which is often seen as more objective than other alternatives for decision making
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6
Q

descriptive analytics

A

-the use of data to understand past and current business performance and make informed decisions

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7
Q

predictive analytics

A

-predict the future by examining historical data, detecting patterns or relationships in these data, and then extrapolating these relationships forward in time

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8
Q

prescriptive analytics

A

-identify the best alternatives to minimize or maximize some objective

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9
Q

optimization models

A
  • part of prescriptive analytics

- models that give the best decision subject to constraints of the situation

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10
Q

decision analytics

A
  • part of prescriptive analytics
  • used to develop an optimal strategy when a decision maker is faced with several decision alternatives and an uncertain set of future events
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11
Q

big data

A

-any set of data that is too large or too complex to be handled by standard data-processing techniques and typical desktop software

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12
Q

cross-sectional data

A

data collected from several entities at the same, or approximately the same, point in time

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13
Q

time series data

A

data collected over several time periods

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14
Q

frequency distribution

A

a summary of data that shows the number (frequency) of observations in each of several non-overlapping classes (bins)

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15
Q

histogram

A

a common graphical presentation of quantitative data

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16
Q

arithmetic mean

A

average of a set of numerical values

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17
Q

skewness

A

an important numerical measure of the shape of a distribution

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18
Q

range

A

subtracting smallest value from the largest value

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19
Q

variance

A
  • a measure of variability that utilizes all the data

- based on the deviation of the observations from the mean

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20
Q

standard deviation

A

-the positive square root of the variance

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21
Q

coefficient of variation

A

descriptive statistic that indicates how large the standard deviation is relative to the mean

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22
Q

data visualization

A
  • first step in interpreting data
  • creating a summary table for data
  • generating charts to represent data
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23
Q

data ink ratio

A
  • remove unnecessary non-data ink
  • de-emphasize and regularize the remaining non-data ink
  • emphasize the most important data ink
24
Q

when to use tables

A
  • to look up and compare individual values

- data must be precise

25
when to use graphs
- to see patterns, trends, relationships, and exceptions, to make broader comparisons - to rapidly get a sense of the story
26
percentile
the value of a variable at which a specified (approximate) percentage of observations are below that value
27
quartile
division points when data is divided into four equal parts
28
IQR
interquartile range | -difference between third and first quartiles
29
z-score
measures the relative location of a value in the data set - helps to determine how far a particular value is from the mean relative to the data set's standard deviation - often called the standardized value
30
outliers
extreme values in a data set - may be incorrectly recorded - may be from an observation that doesn't belong to the population we are studying
31
box plot
- a graphical summary of the distribution of data | - developed from the quartiles for a data set
32
scatter chart
- useful graph for analyzing the relationship between two variables - also suggests a trend line could be used as an approximation for the relationship between variables
33
covariance
a descriptive measure of the linear association between two variables
34
correlation coefficient
-measures the linear relationship between two variables
35
parallel coordinates plot
- used for plotting multivariate, numerical data | - ideal for comparing many variables together and seeing the relationships between them
36
regression analysis
-a statistical tool that examines the relationship between two or more variables so that one may be predicted from the other(s)
37
simple linear regression model
-the equation that describes how y is related to x and an error term
38
experimental region
the range of values of the independent variables in the data used to estimate the simple linear regression model
39
extrapolation
prediction of the value of the dependent variable outside the experimental region
40
multiple regression model
the equation that describes how the dependent variable y is related to the independent variables x1,x2,x3..., and an error term
41
multicollinearity
-the correlation among the independent variables in multiple regression analysis
42
time series
a sequence of observations on a variable measured at successive points in time or over successive periods of time -objective of analysis is to uncover a pattern in the time series and then extrapolate the pattern into the future
43
horizontal time series pattern
exists when the data fluctuate randomly around a constant mean overtime
44
trend pattern
shows gradual shifts or movements to relatively higher or lower values over a longer period of time -usually a result of long-term factors
45
seasonal pattern
-recurring patterns over successive periods of time
46
cyclical pattern
exists if the time series plot shows an alternating sequence of points below and above the trend line that lasts for multiple years
47
forecast error
difference between the actual and the forecasted values for period t
48
MFE
mean forecast error | -mean or average of the errors
49
MAE
mean absolute error | -measure of forecast accuracy that avoids problem of positive and negative errors offsetting one another
50
MSE
measure that avoids the problem of positive and negative errors offsetting each other -computing the average of the squared forecast errors
51
MAPE
- mean absolute percentage error | - average of the absolute value of percentage forecast errors
52
moving averages
-use the average of the most recent k data values in the time series as the forecast for the next period
53
exponential smoothing
uses a weighted average of past time series values as a forecast
54
Four v’s of big data
Volume Velocity Veracity Variety
55
Veracity
How much uncertainty is in the data