Problems 9.3 (a and b); 9.11. — These are the last two problems in the course where hand calculations are required ( except some exam problems). For the rest of the class and PUB 506 class SPSS will do all calculations for us, so we will be working only with SPSS output.Part B:1. SPSS output – pp. 239-240. Reproduce two SPSS tables and interpret them. Detailed instructions are provided on pages 239-240 and in the T Test SPSS Interpretation Example that is posted in the Module 9 files folder. SPSS Demonstration for Chapter 9 video and the text for the video are available in the Videos folder.ATTENTION: BOOK TYPO on page 240 sixth line from the bottom should read : The value of sig. ( in other words the value of the exact probability of the null hypotheses being true) is greater than our usual indicator of significance (alpha) of 0.05.2. Problem 9.17 p . 255 – 256.Part C:Part C should be done after the completion of SPSS demonstration part B.You are the researcher Project 1: Exploring the Gender GAP. Follow the directions given on pp. 256 – 258. Choose two (not four, as the book suggests) variables. Choose only interval-ratio variables as your dependent variables, not ordinal variables, as the book suggests.In any hypothesis testing [T test and F (ANOVA) test discussed in the next chapter], please follow the 5-step model of hypothesis testing and treat the SPSS output as the fourth step (calculations) of the analysis. The critical value for all tests should be looked up in the Appendixes tables. State your null and research hypotheses in words and using notations.Two-Sample Hypotheses Testing. Things to Remember:There is almost always a difference between sample means or between sample proportions.If this difference happened by chance, we say that there is no statistically significant difference between the sample means (or proportions). This indicates that the population means (or the population proportions) are the same.That is why there are two ways of presenting hypotheses:The first one is when we hypothesize about population means or proportions:For example: Research Hypothesis: The average number of children in the suburban families is smaller than the average number of children in the center city families. ( Note: we do not use the word significant because we are talking about the population)The second one: we hypothesize about the sample means or proportions (for respondents):Research Hypothesis: The average number of children (2.37) in the sample of the suburban families (N=42) is significantly smaller than the average number of children (2.78) in the sample of the center city families (N=38). If we find that the mean of the first sample is (statistically) significantly smaller than the mean of the second sample (2.37 – 2.78 =- 0.41), this will infer that this difference is also present in the population (between all suburban families and all center city families).The same logic applies to ANOVA hypothesis testing discussed in the next chapter.PROBLEMS ARE IN THE BACK OF THE CHAPTER
