Quantitative data analysis

Question 1

What is statistics?

Answer 1

The science of collecting, analysing, interpreting and presenting and organising quantitative data

Question 2

Common applications of statistics in business

Answer 2

-Financial management
-Operational Process Improvement
-Strategic Planning
-Evaluating Performance

Question 3

What is statistical inferecne?

Answer 3

How we generalise from a sample to a population providing evidence based asnwers to research questions.

Question 4

What should quantitative data analysis be guided by?

Answer 4

Your research problem

Question 5

What is the quantitative data analysis process?

Answer 5

1. Data collection
2. Descpritive Statistics
3. Inferential Statistics
4. Interpretation & Application

Question 6

Methods of quantitative data collection

Answer 6

-Surveys
-Experiments
Secondary sources

Question 7

What needs to happen to qualitative data before it can be applied to statistics?

Answer 7

It needs to be transformed into quantitative data (e.g. coding, text analysis)

Question 8

How do we collect data on a population?

Answer 8

-Data on the population is rarely available.
- Instead, we obtain a sample to represent the population
-Statistical methods tell us what we can know about the population from the data

Question 9

What are variables?

Answer 9

Any quantity that can be measured

Question 10

What are descriptive statistics used for?

Answer 10

Descriptive statistics are used to organsie and summarise data and identify the eatures of a sample, often including visualisation/plots.

Question 11

What are the key measures of descriptive statistics?

Answer 11

• Central tendency
• Dispersion
  -Association

Question 12

Define Central tednecy?

Answer 12

What is the typical value of a variable?

Question 13

Define Dispersion

Answer 13

How far from the typical value are the individual observations of a variable?

Question 14

Define Association

Answer 14

How does a variable relate to another variable?

Question 15

What are Inferential Statistics?

Answer 15

Used to make predictions about parameters (characteristics) of the population based on two factors.

Question 16

Key concepts of Inferential Statistics

Answer 16

• Probability
• Sampling distributions
• Hypotheses testing

Question 17

Define Probability

Answer 17

What is the chance that a particular event will occur?

Question 18

Define sampling distributions

Answer 18

What is the probability that we obtain parameters observed in our sample?

Question 19

Define hypothesis testing

Answer 19

Does the data support our beliefs about the population

Question 20

Define Population

Answer 20

The entire set of people or obects that is the subject of investigation

Question 21

Define Sample

Answer 21

A subset of the population for which data has been collected

Question 22

Define Variable

Answer 22

Any number or quantity that can be counted

Question 23

Define Observation

Answer 23

Data collected for a specfific individual in the sample

Question 24

Define Statistic

Answer 24

A numer computed form the sample data to estimate a parameter

Question 25

Define Parameter

Answer 25

A quantifiable characteristic of a population, inferred from sample data

Question 26

Define Distribution

Answer 26

How the values of a variable are spread

Question 27

Define Probability

Answer 27

A measure of the liklihood that a particular event will occur

Question 28

Define Hypothesis

Answer 28

A specific, testable prediction about what you expect to happen in your study

Question 29

What does conclusive research require?

Answer 29

Requires quantitative measurement of numerical variables

Question 30

What is descripitve research?

Answer 30

Seeks to verify the nature/characteristics of a phenomenon

Question 31

What is causal research?

Answer 31

Seeks to test relationships and hypotheses (predictions) --> inferential statistics

Question 32

How to choose variables?

Answer 32

- Identify the variables to be measured - Determine the right measurement approach - Collect observations of these variables for each individual in our sample

Question 33

What do we use descriptive & inferential statistics for?

Answer 33

To summarise our sample data, then use inference to generalise about population parameters.

Question 34

What are the 4 levels of measurement?

Answer 34

-Nominal -Ordinal -Interval -Ratio

Question 35

Define Nominal measurement

Answer 35

Numbers indetify categories or binary outcomes

Question 36

Define Ordinal measurement

Answer 36

Numbers indicate categories in an ordered sequence (ranking)

Question 37

Define Interval measurement

Answer 37

Distances are concistent (scores are comparable but zero point is arbitrary)

Question 38

Define Ratio measurement

Answer 38

Zero point is non-arbitrary and represents a real measurment

Question 39

What does each level of measurement have?

Answer 39

-Different properties -The properties of each level determine the types of statisticsl analysis that can be applied

Question 40

What can data be?

Answer 40

- Data can be categorical or continuous. - Continous data can be grouped to examine frequencies

Question 41

What does visualisation help with?

Answer 41

Helps in understanding the distribution of values: Bar charts for categorical data & histograms for continuous data. These can also be presented in requency tables