1. BMGT 311: Chapter 12
Using Descriptive Analysis, Performing
Population Estimates, and Testing Hypotheses
2. Learning Objectives
• To learn about the concept of data analysis and the functions it provides
• To appreciate the five basic types of statistical analysis used in marketing
research
• To use measures of central tendency and dispersion customarily used in
describing data
• To understand the concept of statistical inference
• To learn how to estimate a population mean or percentage
• To test a hypothesis about a population mean or percentage
3.
4. Types of Statistical Analyses
Used in Marketing Research
• Descriptive analysis
• Inferential analysis
• Differences analysis
• Associative analysis
• Predictive analysis
5. Descriptive Analysis
• Used by marketing researchers
to describe the sample dataset
in such a way as to portray the
“typical” respondent and to
reveal the general pattern of
responses
6. Inference Analysis
• Used when marketing
researchers use statistical
procedures to generalize the
results of the sample to the
target population it represents
7. Difference Analysis
• Used to determine the degree
to which real and generalizable
differences exist in the
population to help the manager
make an enlightened decision
on which advertising theme to
use
9. Predictive Analysis
● Statistical procedures and
models to help make
forecasts about future
events
● Big data is making this
highly accurate
● This is the future of
marketing and research
10.
11. Understanding Data via
Descriptive Analysis
• Two sets of measures are used extensively to describe the information
obtained in a sample.
• Measures of central tendency or measures that describe the “typical”
respondent or response
• Measures of variability or measures that describe how similar (dissimilar)
respondents or responses are to (from) “typical” respondents or responses
12. Measures of Central Tendency: Summarizing the
“Typical” Respondent
• The basic data analysis goal involved in all measures of central tendency is to
report a single piece of information that describes the most typical response
to a question.
• Central tendency applies to any statistical measure used that somehow
reflects a typical or frequent response.
13. Measures of Central Tendency: Summarizing the
“Typical” Respondent
• Measures of central tendency:
• Mode: a descriptive analysis measure defined as that value in a string of
numbers that occurs most often
• Median: expresses that value whose occurrence lies in the middle of an
ordered set of values
• Mean (or average):
14. Measures of Variability: Visualizing the Diversity of
Respondents
• All measures of variability are concerned with depicting the “typical”
difference between the values in a set of values.
• There are three measures of variability:
• Frequency distribution
• Range
• Standard deviation
15. Measures of Variability: Visualizing the Diversity of
Respondents
• A frequency distribution is a tabulation of the number of times that each
different value appears in a particular set of values.
• The conversion is accomplished simply through a quick division of the
frequency for each value by the total number of observations for all values,
resulting in a percent, called a percentage distribution.
16. Measures of Variability: Visualizing the Diversity of
Respondents
• Range: identifies the distance between lowest value (minimum) and the
highest value (maximum) in an ordered set of values
• Standard deviation: indicates the degree of variation or diversity in the
values in such a way as to be translatable into a normal or bell-shaped curve
distribution
17.
18. Coding Data and the
Data Code Book
• Typical Question: How satisfied are you with the gas mileage in the Ford
Fiesta
Highly
Satisfied
Satisfied
Somewhat
Satisfied
Neither
Satisfied or
dissatisfied
Somewhat
Dissatisfied
Dissatisfied
Not Satisfied
at all
19. Coding Data and the
Data Code Book
• Once the items are coded - you can build a frequency distribution table
Highly
Satisfied
Satisfied
7
6
Satisfied
Neither
Satisfied or
dissatisfied
Somewhat
Dissatisfied
Dissatisfied
Not Satisfied
at all
5
4
3
2
1
20. Building the Frequency Distribution
Satisfaction Rating
Count
7
2
6
2
5
4
4
2
3
0
2
0
1
0
Total
10
Frequency: Number of times a number
(response) is in the data set
Frequency Distribution: Summary of
how many times each possible response
to a question appears in the data set
21. Building the Frequency Distribution
Satisfaction
Rating
Count
Sum
7
2
14
6
2
12
5
4
20
4
2
8
3
0
2
0
1
0
Total
10
54
Mean
5.4
Mean: Arithmetic Average of all
responses
!
(7+5+6+4++6+5+7+5+4+5) = 54
!
54/10 = 5.4
22. Building the Frequency Distribution
Satisfaction
Rating
Count
Sum
Percentage
7
2
14
20%
6
2
12
20%
5
4
20
40%
4
2
8
20%
3
0
0
2
0
0
1
0
0
Total
10
54
5.4
Percentage = Frequency/
total count
23. Building the Frequency Distribution
Satisfaction
Rating
Count
Sum
Percentage
Cumulative %
7
2
14
20%
20%
6
2
12
20%
40%
5
4
20
40%
80%
4
2
8
20%
100%
3
0
0
2
0
0
1
0
0
Total
10
54
5.4
Cumulative Percentage = Each
individual percentage added to the
previous to get a total
24. Building the Frequency Distribution
Satisfaction
Rating
Count
Sum
Percentage
Cumulative %
7
2
14
20%
20%
6
2
12
20%
40%
5
4
20
40%
80%
4
2
8
20%
100%
3
0
0
2
0
0
1
0
0
Total
10
54
5.4
Median = 5
Median: Descriptive statistic that
splits the data into a hierarchal
pattern where half the data is above
the median value and half is below
!
Look for 50% or what includes
50% in the cumulative %
25. Building the Frequency Distribution
Satisfaction
Rating
Count
Sum
Percentage
Cumulative %
7
2
14
20%
20%
6
2
12
20%
40%
5
4
20
40%
80%
4
2
8
20%
100%
3
0
0
2
0
0
1
0
0
Total
10
54
5.4
Mode = 5
Mode: Most Frequently occurring
response to a given set of questions
26. Building the Frequency Distribution
Satisfaction
Rating
Count
Sum
Percentage
Cumulative %
7
2
14
20%
20%
6
2
12
20%
40%
5
4
20
40%
80%
4
2
8
20%
100%
3
0
0
2
0
0
1
0
0
Total
10
54
5.4
Range = 7 - 4 = 3
Range: Statistic that represents the
spread of the data and the distance
between the largest and smallest
values of a frequency distribution
27. Descriptive Analysis: Building the Distribution Table
from a real life example
• Example Question from a Survey:
• Question: Overall, how satisfied are you with the Real World Experience
Adjunct Professors bring to the table here at Point Park University
Highly
Satisfied
7
Satisfied
Somewhat
Satisfied
Neither
Satisfied or
dissatisfied
Somewhat
Dissatisfied
Dissatisfied
Not Satisfied
at all
6
5
4
3
2
1
28. Step 1: Collect the Raw Data
Respondent Number
Satisfaction Rating
1
2
3
4
5
6
7
8
9
10
11
Highly
Satisfied
7
Satisfied
Somewhat
Satisfied
Neither
Satisfied or
dissatisfied
Somewhat
Dissatisfied
Dissatisfied
Not Satisfied
at all
6
5
4
3
2
1
29. Distribution Table: Fill in Data Sets
• Record the Data
Percentage
Cumulative %
0
0
0%
0%
6
0
0
0%
0%
0
0
0%
0%
4
0
0
0%
0%
3
0
0
0%
0%
2
0
0
0%
0%
1
• Median =
Sum
5
• Mode =
Count
7
• Mean =
Satisfaction
Rating
0
0
0%
0%
Total
11
0
Mean
0.00
• Range =
30. Class Work: Try to Develop a Distribution Table
from the following Data Sets
31. • Question: Overall, how satisfied are you with the cafe food at Point Park
University?
Respondent Number
1
4
3
2
4
1
5
3
6
1
7
2
8
7
3
2
Highly
Satisfied
Satisfaction Rating
2
Satisfied
Somewhat
Satisfied
Neither
Satisfied or
dissatisfied
Somewhat
Dissatisfied
Dissatisfied
Not Satisfied
at all
6
5
4
3
2
1
32. In Class Example #2
• What is the mean?
• What is the median?
• What is the mode?
• What was the range? What does this tell you?
• Overall, what do these results tell you? What would you recommend?
33. Hypothesis Tests
• Tests of an hypothesized population parameter value:
• Test of an hypothesis about a percent
• Test of an hypothesis about a mean
• The crux of statistical hypothesis testing is the sampling distribution
concept.
35. Hypothesis Tests: Example: Page 314 and 315
• Rex hypothesizes interns will make about $2,750 their first semester
• Sample Survey:
• n=100 (Total Students Surveyed)
• Sample Mean = $2,800
• Standard Deviation = $350
• Does his hypothesis support this?
36. Hypothesis Tests: Example: Page 314 and 315
• z = (x - u)/standard error of the mean
• z = (2,800 - 2,750)/350/Sq Root 100
• z = 50/35 = 1.43
• Is this Hypothesis Supported? Yes. Why?
