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# Bmgt 311 chapter_12

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bmgt 311 marketing research fall 2014 chris lovett

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### Transcript of "Bmgt 311 chapter_12"

1. 1. BMGT 311: Chapter 12 Using Descriptive Analysis, Performing Population Estimates, and Testing Hypotheses
2. 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. 3. Types of Statistical Analyses Used in Marketing Research • Descriptive analysis • Inferential analysis • Differences analysis • Associative analysis • Predictive analysis
4. 4. 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
5. 5. Inference Analysis • Used when marketing researchers use statistical procedures to generalize the results of the sample to the target population it represents
6. 6. 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
7. 7. Association Analysis • Investigates if and how two variables are related
8. 8. 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
9. 9. 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
10. 10. 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.
11. 11. 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):
12. 12. 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
13. 13. 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.
14. 14. 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
15. 15. 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
16. 16. Coding Data and the   Data Code Book • Once the items are coded - you can build a frequency distribution table Highly Satisfied Satisfied Satisfied Neither Satisfied or dissatisfied Somewhat Dissatisfied Dissatisfied Not Satisfied at all 7 6 5 4 3 2 1
17. 17. 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
18. 18. 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
19. 19. 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
20. 20. 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
21. 21. Building the Frequency Distribution 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 % Median = 5 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
22. 22. Building the Frequency Distribution Mode: Most Frequently occurring response to a given set of questions 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
23. 23. Building the Frequency Distribution Range: Statistic that represents the spread of the data and the distance between the largest and smallest values of a frequency distribution Range = 7 - 4 = 3 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
24. 24. 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 Satisfied Somewhat Satisfied Neither Satisfied or dissatisfied Somewhat Dissatisfied Dissatisfied Not Satisfied at all 7 6 5 4 3 2 1
25. 25. Step 1: Collect the Raw Data Respondent Number Satisfaction Rating 1 2 3 4 5 6 7 8 9 10 11 Highly Satisfied Satisfied Somewhat Satisfied Neither Satisfied or dissatisfied Somewhat Dissatisfied Dissatisfied Not Satisfied at all 7 6 5 4 3 2 1
26. 26. Distribution Table: Fill in Data Sets • Record the Data • Mean = • Mode = • Median = • Range = Satisfaction Rating Count Sum Percentage Cumulative % 7 0 0 0% 0% 6 0 0 0% 0% 5 0 0 0% 0% 4 0 0 0% 0% 3 0 0 0% 0% 2 0 0 0% 0% 1 0 0 0% 0% Total 11 0 Mean 0.00
27. 27. Class Work: Try to Develop a Distribution Table from the following Data Sets
28. 28. • Question: Overall, how satisfied are you with the cafe food at Point Park University? Respondent Number Satisfaction Rating 1 3 2 4 3 2 4 1 5 3 6 1 7 2 8 2 Highly Satisfied Satisfied Somewhat Satisfied Neither Satisfied or dissatisfied Somewhat Dissatisfied Dissatisfied Not Satisfied at all 7 6 5 4 3 2 1
29. 29. 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?
30. 30. 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.
31. 31. Hypothesis Tests
32. 32. 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?
33. 33. 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?
34. 34. Hypothesis Tests: Example: Page 314 and 315
35. 35. Hypothesis Tests: Example: Page 314 and 315
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