Marketing Research Ch. 12

Front 1

Data Analysis

Back 1

Describing the dataset by by computing a small number of statistics that characterize various aspects of the data.

Front 2

4 purposes of statistical analysis?

Back 2

1. Summarize data

2. Help us to understand

3. Show patterns

4. Interpret patterns

Front 3

5 types of statistical anaylsis

Back 3

1. Descriptive

2. Inferential

3. Differences

4. Associative

5. Predicted

(Progressively more complex and usually combined when used)

Front 4

Descriptive analysis

Back 4

Ex. Mean, median, mode, standard deviation, and range.

Portrays the "typical" respondent to reveal a general pattern of responses. Summary.

Used early and become the foundation for subsequent analysis.

Front 5

Inference analysis

Back 5

Ex. Standard error and null hypothesis.

Determines population parameters, test hypothesis, and estimates population values.

Generalize results of the target population.

Front 6

Difference analysis

Back 6

Ex. t-test of differences and analysis of differences.

Determines differences between two percentages or two or more means for groups in the sample.

Front 7

Association analysis

Back 7

Ex. Correlation and cross tabulation

Determines simple relationships, if they are related in a statistical way, and by how much.

Front 8

Predictive analysis

Back 8

Ex. Multiple regression

Finds complex relationships for variables, forecast future events, and determines how several independent variables influence a key dependent variable.

Front 9

2 sets of measures used to describe information used in a sample

Back 9

Central tendency- typical response, single piece of into.

Variability- measures describing how similar or dissimilar responses are from typical ones, set of values.

Front 10

2 key questions while using descriptive measures

Back 10

1. What one number best represents the variable?

2. How well does that one number represent the variable in question?

Front 11

3 measures of central tendency and define

Back 11

Mode- value that occurs most often.

Median- value whose occurrence lies in the middle of an ordered set of values; halfway point.

Mean- average, must be computed.

Front 12

3 measures included in variability

Back 12

1. Frequency distribution

2. Range

3. Standard deviation

(tells how close or apart measures are)

Front 13

Frequency (percentage) distribution

Back 13

Number or percentage of times a different value appears in a particular set of values (occurrence).

Front 14

Range

Back 14

Max and min values in a set of numbers. Does not tell us how often these values occur.

Front 15

Standard deviation

Back 15

Degree of variation or diversity in the values. (normal or bell shaped curve)

xi each individual observation

x_ mean

Front 16

With a bell shaped distribution _________% of the value lies within ____________ times the standard deviation away from the mean.

Back 16

95%

+- 1.96

Front 17

Why is the squaring operation in the standard deviation formual used?

Back 17

To avoid the cancellation effect.

Front 18

Variance

Back 18

Standard deviation squared.

Front 19

With the bell shaped curve, how will the curve look with a small standard deviation vs a large standard deviation?

Back 19

Small- greatly compressed or high peak

Large- flat because it is stretched out.

Front 20

Nominal scale

Back 20

"What is your gender?"

Mode

Frequency/percentage distribution

Front 21

Ordinal scale

Back 21

"Rank from 1st to 5th choice"

Median

Cumulative percentage distribution

Front 22

Interval scale

Back 22

"On a scale of 1 to 5, how much..."

Mean

Standard deviation/range

Front 23

Ratio scale

Back 23

"How many times did you..."

Mean

Standard deviation/range

Front 24

Average in descriptive measure

Back 24

Include. Place in column close to variable descriptions and arrange in an order.

Front 25

Median, mode in descriptive measure

Back 25

Don't include.

Front 26

Standard deviation in descriptive measure

Back 26

Typically include, if they are mostly equal don't include.

Front 27

Minimum, maximum in descriptive measure

Back 27

Include if data set has several diff variables, don't report if they are the same.

Front 28

Frequencies in descriptive measure

Back 28

Include if researcher wants to note something about the sample (very small with great affect).

Very close to variables.

Front 29

Percent in descriptive measures

Back 29

Include, very close to variables.

Front 30

Mode in descriptive measure

Back 30

Highlight, but if obvious do not report. Largest percentage group i usually readily apparent.

Front 31

Statistics

Back 31

Values computed from a sample. (p)

Front 32

Parameters

Back 32

Values computed from a complete census that are considered to be precise and valid measures of the population. (pie sign)

Front 33

Inference

Back 33

Form of logic in which you make a general statement about an entire class based on what you have observed about a small set of members of that class.

Front 34

Statistical inference

Back 34

Sample size and statistic are used to make an estimate of the corresponding population parameter (large samples are more accurate than small ones).

Front 35

Statistical inference is based on what to determine what?

Back 35

Based on sample size and variability to determine the amount of sampling error.

Front 36

2 types of statistical inferences

Back 36

Parameter estimation and hypothesis testing.

Front 37

Parameter estimate

Back 37

Used to approximate the population value (parameter) through the use of confidence intervals.

Front 38

Hypothesis testing

Back 38

Used to compare the sample statistic with what is believed (hypothesized) to be the population value prior to undertaking the survey.

Used to accept or reject hypothesis based on sample evidence.

Front 39

Parameter estimation

Back 39

Using sample info to compute an interval that describes the range of a parameter such as the population mean or percentage.

Front 40

3 values that parameter estimation uses

Back 40

1. Population mean/percentage

2. Standard error of the statistic

3. Desired level of confidence

Front 41

Standard error

Back 41

Measure of the variability in a sampling distribution based on what is believed to occur were we to take a multitude of independent samples from the same population.

Front 42

Does the formula for mean standard error differ from a percentage standard error?

Back 42

Yes.

Front 43

The standard error will be ___________with larger sample sizes and ___________ with smaller sample sizes.

Back 43

Smaller

Larger

Front 44

2 things the standard error takes into account

Back 44

Sample size and variability in sample.

Front 45

50-50 split

Back 45

Great variability.

Has a larger standard error than 90-10 split when sample size is the same.

Front 46

Population parameters are estimated with the use of _______________________.

Back 46

Confidence intervals

Front 47

Confidence intervals

Back 47

Degree of accuracy desired by the researcher and stipulated as a level of confidence in the form of a range with a lower and upper boundary.

Front 48

Most commonly used level of confidence

Back 48

95%, 1.96

Front 49

A 99% confidence interval is always ___________ than 95% confidence interval if all other factors are equal.

Back 49

Wider

Front 50

A sample statistic is usually a ____________ or a _____________.

Back 50

Mean

Percentage

Front 51

Hypothesis

Back 51

Expectation

Front 52

Hypothesized population parameter

Back 52

Value can be determined using either a percentage or a mean.

Front 53

Sampling distribution concept

Back 53

Our sample is one of many contributing to the bell shaped curve.

**Crux of statistical hypothesis testing.

Front 54

2 tests of a hypothesized population parameter value

Back 54

1. Test of a hypothesis about a percentage

2. Test of a hypothesis about a mean