MKF2121 Week 10 moodle new.pdf - Lecture10 JM1 Lecture 10 Correlation and Regression Dr Junzhao Ma MKF2121 Marketing Research Methods Todays agenda

# MKF2121 Week 10 moodle new.pdf - Lecture10 JM1 Lecture 10...

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Lecture 10 MKF2121 1 Lecture 10 Correlation and Regression Department of Marketing Dr. Junzhao Ma MKF2121 Marketing Research Methods JM1 Today’s agenda 2 Quick review of statistical tests covered to date Different tests and what they are for What more can you learn from SPSS output (things are not specifically stated in your hypotheses) More statistical tests for hypothesis testing – Correlation – Regression Taxonomy of different statistical tests 5/8/2017 3 Hypothesis Testing Test of Association (relational RQ) Test of Differences (comparative RQ) Means Proportions Different forms of t test Cross tabulation Correlation Multiple regression
Lecture 10 MKF2121 2 Association btw 2 categorical variables Test of association Mean diff. btw 1 metric var. and constant Something = 5 Mean diff. of 1 metric variable btw. 2 groups Weight of males vs Weight of females Mean diff. btw 2 metric variables Attitude before vs Attitude after (same ind.) Mean diff. of 1 metric var. among >2 groups Prices of brands A,B and C One sample T-test Independent samples T-test Paired samples T-test One-way ANOVA Which test should you choose? – It depends on your hypothesis Cross-tabulation (Chi-squared test) Today’s agenda 5 Quick review of statistical tests covered to date Different tests and what they are for Interpreting SPSS output More statistical tests for hypothesis testing – Correlation – Regression Interpreting SPSS results - what is the outcome of your test? If the p value (Sig.) < 0.05 If the p value (Sig.) 0.05 reject H 0 , go to step 2 do not reject H 0 . State H 0 in conclusion Step 1 Step 2 clearly state the relationship between variables or the differences between groups
Lecture 10 MKF2121 3 Cross-tabs example: soft drink preference by age group (week 8) 7 190 customers are surveyed for their preference of Coca-Cola and Pepsi (in paired comparison) They also report their own age, which is re-coded into two categories (“above 40” and “40 or under”) Null Hypothesis: “Brand preference” has no relationship with “Age” SPSS output 8 P value Strength of the relationship – this matters only if P < 0.05 SPSS output (continued) 9 0 = Coca Cola 1 = Pepsi Conclusion – younger adults are more likely to prefer Coca Cola over Pepsi than older adults. 45.9% of the young adults prefer Coke, compared to 30.4% of older adults only when p < 0.05