1. What statistical tests have we learned so far? Descriptive statistics (chp. 12) –Mean, median, mode –Frequency of each response (frequencies), range, standard deviation –Analyze > descriptive statistics > frequencies > statistics Estimate Population Parameters (chp. 12) –Allow us to make estimates of the actual population parameters by calculating Confidence Intervals. This gives us a range, within which, the actual population value should lie with a certain degree of confidence. –Analyze > compare means > one sample t-test (means only, proportions by hand) Testing for Associations – Chi Sq. (chp. 14) –Are two nominally scaled variables associated with each other. –Analyze > descriptive statistics > cross-tabs > statistics > chi sq –Asymp. Sig. of t statistic <.05 then null hypothesis is not supported, meaning there is a statistically significant relationship between the variables. Ch 171

2. Implementing Basic Differences Tests 13

3. Ch 173 Why Differences are Important Market segmentation holds that within a market, there are different types of consumers who have different requirements, and these differences can be the bases of marketing strategies.

4. Ch 174 Why Differences are Important Some differences are obvious – differences between teens’ and baby boomers’ music preferences. Other differences are not so obvious and marketers who “discover” these subtle differences may take advantage of huge gains in the marketplace.

5. Ch 175 Why Differences are Important Market Segmentation Differences must be statistically significant –Statistical significance of differences: the differences in the sample(s) may be assumed to exist in the population(s) from which the random samples are drawn

6. Ch 176 An Example: Testing the Difference Between Two Percentages (p. 495) Last year a Harris Poll showed 40% of surveyed companies were coming to college campuses to hire seniors (n=400 companies surveyed). This year, the Harris Poll reported the percentage is 65% (n=100 companies surveyed). Is this a significant difference?

7. Ch 177 Why Differences are Important Market Segmentation Differences must be meaningful –Meaningful difference: one that the marketing manager can potentially use as a basis for marketing decisions –Large sample sizes can yield deceiving statistical differences, just because it is statistical, does not mean it is meaningful.

8. Ch 178 Determining Statistical Significance: The P value Statistical tests generate some critical value usually identified by some letter; i.e., z, t or F. Associated with the value will be a p value which stands for probability of supporting the null hypothesis (no difference or no association). If the probability of supporting the null hypothesis is low, say 0.05 or less, we have significance!

9. Ch 179 Determining Statistical Significance: The P value P values are often identified in SPSS with abbreviations such as “Sig.” or “Prob.” P values range from 0 to 1.0. P-values ≤.05 indicate statistically significant difference.

10. Ch 1710 Some Example P Values and Their Meaning First, we MUST determine the amount of sampling error we are willing to accept and still say the results are significant. Convention is 5% (0.05), and this is known as the “alpha error.”

11. Ch 1711 Some Example P Values and Their Meaning P=0.05… P=0.01… P=0.10… P=0.051… P=0.99… significant not significant

12. Ch 1712 Testing Differences: Percentages or Means? There are statistical tests for when a researcher wants to compare the means or percentages of two different groups or samples. –i.e. freshmen vs. seniors Percentages are calculated for questions with nominal or ordinal level of measurement. (not via SPSS) –20% of our respondents were freshmen –40% of our respondents were seniors Means are calculated for questions with interval or ratio (metric level of measurement.) –On average, freshmen respondent incomes were $5,000 –On average, senior respondent incomes were $8,500

13. Ch 1713 Testing the Difference Between Two Percentages Null hypothesis: no difference between the means being compared –P-values >.05 –So, freshman and seniors have basically the same opinion, etc. Alternative hypothesis: a true difference between the compared means –P-values ≤.05 –So, freshman and seniors have a statistically different opinion, etc.

14. Ch 1714 Using SPSS to Test the Difference Between Two Percentages SPSS does not perform tests of significance of the difference between the percentages of two groups (nominally scaled question), but you can use SPSS to generate the relevant information and perform a hand calculation.

15. Ch 1715 Using SPSS to Test Differences Between Two Group Means The t-test is used to compare differences between two means – interval or ratio scale (remember: “t for two”). But the types of t-test depends upon whether the two groups upon which the means are calculated are independent (separate groups) or paired (the same group).

16. Ch 1716 Using SPSS to Test Differences Between Two Group Means If the two groups are different, i.e., males vs. females, you would use INDEPENDENT SAMPLES t-test. If the two groups are from the same sample, you would use PAIRED SAMPLES t-test – will come back to this later.

17. Ch 1717 An Example Is there a difference between subscribers vs. non-subscribers to City Magazine on “likely patronage”? –Since “likely patronage” is an interval scale, we can calculate a mean score. –There are two independent groups: subscribers vs. non-subscribers.

18. Ch 1718 An Example –To determine if subscribers’ mean score on “likely” is different from non-subscribers’ mean on “likely,” we should use SPSS: ANALYZE, COMPARE MEANS, INDEPENDENT SAMPLES T-TEST (See p. 500.)

19. Ch 1719

20. Ch 1720

21. Let’s do an example Survey pg. 320 Hobbit’s Choice Dataset on Blackboard Are people who are married more likely to patronize the restaurant than people who are not married? –Two groups: married vs. not married –Question being analyzed: (3) patronage Ch 1721

22. Ch 1722 Testing for Significant Differences Among More than Two Groups ANOVA –Analysis of variance (ANOVA): used when comparing the means of three or more groups –ANOVA will “flag” when at least one pair of means has a statistically significant difference, but it does not tell which pair.

23. Ch 1723 Testing for Significant Differences Among More than Two Groups –When the F values “Sig.” is less than or equal to 0.05, ANOVA is telling you that “at least one pair of means is significantly different.” –To determine which pair(s) are different, you must rerun the test and select a POST HOC test (Duncan).

24. Ch 1724 Testing for Significant Differences Among More than Two Groups –Assume that we wish to know if the mean score on “likelihood of patronizing an upscale restaurant” differs across sections of newspaper read most. –ANALYZE, COMPARE MEANS, ONE-WAY ANOVA –“Likely” goes in Dependent list; “section of newspaper” goes in factor

25. Ch 1725 Testing for Significant Differences Among More than Two Groups –Output shows Sig. Is 0.000 meaning at least one pair of means is different. –Now rerun the ANOVA but select Duncan under the POST HOC button.

26. Ch 1726

27. Ch 1727

28. Ch 1728 In Summary: Test of Differences Among More than Two Groups The basic logic –ANOVA (Analysis of Variance). –Test all pairs of averages simultaneously

29. Ch 1729 In Summary: Test of Differences Among More than Two Groups –If no pair is different at the 95% level of confidence, stop the analysis and say all pairs are “Equal.” –If at least one pair is different at the 95% level of confidence, make a table to show what pairs are “Equal” or “Unequal” by running post hoc test.

30. Let’s do an example Survey pg. 320 Hobbit’s Choice Dataset on Moodle Does marital status influence whether or not people are likely to patronize the restaurant? –Groups: married vs. not married vs. other –Question being analyzed: (3) patronage Ch 1730

31. Homework Case 13.1, p. 349 Hobbit’s Choice Dataset Questions: 1 and 5 Ch 1731