2. QM 2113 - Spring 2002 Business Statistics SPSS: A Summary & Review

3. Agenda  Homework – Return bivariate analysis (Excel) Questions about either Excel exercise Comments about bivariate exercise – Collect SPSS exercises  SPSS – Reminder: delete extraneous output – Filtering data – Copy/paste into Word or other applications – CrossTabs Define categories Example comparing Excel and SPSS – Inference (population mean) Hypothesis test Estimation

4. Manipulating Data  Refer to exercises due 4/5/2002 – Salary analysis Men versus women Excel: filter, copy, paste, analyze – Now, let’s look at using SPSS  Filtering – Data | Select Cases | If... – Now do analysis – Unfilter: Data | Select Cases | All Cases...

5. Analyzing Qualitative Data  Recall – Two types of data Qualitative (Gender and Computer Usage) Quantitative (Salary, Age,... ) – Ordinal data can be treated as either quantitative or qualitative; categories with numerical order; e.g., Education and Job Classification  Analyzing relationship between two quantitative variables: regression  Analyzing relationship between two qualitative variables: crosstabs

6. Consider Gender versus Job Classification  Does “job level” depend upon gender?  Simple frequency tables – Doesn’t tell us about how these variables are related – Need to go further: crosstabulation  Review of crosstabs – Joint frequency: basis for developing the other three – Joint relative frequency (% of total) – Analyzing relationships Multiplication rule If independent, joint % = product of margin % values

7. Using Excel’s PivotTable Feature for Crosstabs  Select the data, including headings  Click on Data | PivotTable  Click twice on Next  Click on Layout – Drag Gender to row – Drag Job to column – Drag either to data – Double click on data button Select Count, then click on Options In Show Data As, select % of Total Click on OK – Click on OK  Click on Finish

8. Using SPSS Crosstabs  Analyze | Descriptives | Crosstabs  Select row and column variables  Click on Cells button – Leave Observed checked for Counts – Check Total for Percentages  Resulting table corresponds to Excel PivotTable  Analyze – P(Level | Gender) = P(Level)? – P(Level & Gender) = P(Level) x P(Gender)?

9. Inference: A Quick Review Population or Process Sample Parameter Statistic Inferences

10. Estimation & Hypothesis Testing  Hypothesis testing – Start with an assumed population (or process) parameter – Gather data and see if the statistic is likely, given the assumption  Estimation – Start with a sample statistic – Use that statistic to create an interval estimate  The situation dictates which is appropriate (sometimes either is)

11. Using SPSS for Statistical Inference  Univariate analysis – Inference about averages, not proportions – Hypothesis testing: First, setup test (H 0 & H A, , sketch, decision rule) Then: Analyze | Compare Means | One-Sample t Test – Estimation: Analyze | Descriptive Statistics | Explore  Relationship between two variables – Both quantitative: Analyze | Regression – Both qualitative: Analyze | Descriptive Statistics | Crosstabs – Quantitative dependent & qualitative independent: Analyze | Compare Means | One-Way ANOVA

12. Homework  CrossTabs exercise – Job Level vs Education – Use Excel SPSS  Inference exercises with SPSS – Hypothesis test – Confidence interval estimates – Sample size determination