1 / 15

Business Statistics

Business Statistics. Bivariate Analyses for Qualitative Data. Student Objectives. Summarize regression analysis Interpret regression statistics Incorporate into report Address questions concerning homework Discuss why regression won’t work with qualitative data

emerson-ball
Download Presentation

Business Statistics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Business Statistics Bivariate Analyses for Qualitative Data

  2. Student Objectives • Summarize regression analysis • Interpret regression statistics • Incorporate into report • Address questions concerning homework • Discuss why regression won’t work with qualitative data • Use crosstab approach for joint frequency distributions • Use PivotTable feature of Excel for creating crosstabs

  3. Let’s Wrap Up Regression • Complete example from previous class • Review interpretations of regression statistics • Describe the relationship • Assess the validity • Summary of notation & terminology • Address questions concerning the homework • Expectations • Mechanics (e.g., copy/paste) • Other . . . ?

  4. Results of Analysis of TV Time versus Age • Note: using complete data set • Results b0 = 5.581 hours/week b1 = 0.522 hours per year of age R2 = 56% Syx = 6.924 hours/week • Correlation (r): a single, multipurpose measure • Square root of R • Same sign as b1 • R = +0.75 • Summarizes the estimated strength of the relationship

  5. Interpreting Regression Analyses (a) • Describing the relationship • Intercept (b0): • Base value for Y • If it were possible for X to be 0, this is what Y would be • Slope (b1): • How much Y changes when X changes 1 unit • The sensitivity of Y to changes in X (sometimes, the marginal value of X)

  6. Interpreting Regression Analyses (b) • Validity • R-Square (R2): we know Y varies, but how much (i.e., what percentage) is attributable to the variation in X? • Standard error (Syx): if we used the regression equation to predict Y, how much, on the average, should we expect to be wrong?

  7. Questions About the Homework? • Which data: • kivzdata.xls • All households, not just Ch.7 • What analyses • Univariate • Include: histogram and descriptive stats • Variables: TV Time, Income • Bivariate • Scatterplot (properly labeled) • Regression statistics (the basic 4) • The report • Integrate charts with text • Nontechnical language • Other questions . . . ?

  8. Regression, What Not to Do • Typical modeling errors • Reverse Y and X • Treat qualitative variables as quantitative • Use Excel shortcuts to create inflexible worksheets • Data analysis tool • Plot trend line

  9. Now, Recall Analysis Depends on Data Type • Univariate: • Quanitative data: histograms, averages, etc. • Qualitative data: bar charts, proportions • Bivariate: • Both variables quantitative • Scatterplots • Regression analysis • Either or both variables qualitative • Contingency tables, aka: • PivotTables (Excel) • Crosstabulations • Chi-square analysis (beyond our scope)

  10. Let’s Look at the Website Analytics Case • Pilot sample of major eCommerce sites • Note Internet business models • Virtual storefront (e.g., Amazon) • Content provider (e.g., WSJ) • Auction (e.g., eBay) • Several others, but these are the top three • Major decision common in business • Make vs buy • Apply to site development • What’s the research question here?

  11. Examining the Question • Does “make vs buy” depend upon type of business model? • Start with simple frequency tables • Doesn’t tell us about how these variables are related • Need to go further: crosstab

  12. Crosstabs:Many Flavors • Joint frequency: basis for developing the other three • Joint relative frequency (% of total) • Joint percentages • Margin percentages (same as univariate %) • Analyzing relationships • Row percentage • Column percentage

  13. Crosstabs: Relationships • Relationship? • If so, % of observations in given category of primary variable should differ substantially across categories of explanatory variable • That is, depending upon type of table, • Row % values differ down a given column, or • Column % values across a given row • Easier to analyze • With practice • Using basic probability concepts

  14. Using Excel’s PivotTable Feature for Crosstabs • Select the data, including headings • Click on Data | PivotTable • Click twice on Next • Click on Layout • Drag Development to row • Drag Model 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

  15. Homework • Complete the KIVZ analysis/report • Development vs Model for WA case • Try to create crosstabulation • Think about whether a relationship exists

More Related