Posts tagged with data analysis projects

Analysis of a Fitted Multiple Regression Model

If your “best model” selected in the previous chapter contains more than one x-variable, run that regression model in SAS. If it only has one x-variable, use the best model that had more than one x-variable. Answer the following questions.

  1. Analysis of Output
    (a) The t-tests
    (i) What is being tested?
    (ii) What are the results of the tests? (Careful, these are partial coefficients.)

(b) The F-test

(i) State the null and alternate hypotheses for your model. 
(ii) State the conclusion of the test and the grounds for the decision.

(c) The -equation

    (i) State the equation for your fitted model;
 (ii) Explain how is related to the E(YX).
  1. R-square
    (a) Report the value of R-square;
    (b) Show how it was computed from the software output;
    (c) Explain the meaning of your R-square using the approach found in Lecture Notes 5.
    (d) Give the naïve interpretation of your R-square, as discussed on LN5.
  2. Adjusted R-square
    (a) Report the value of the adjusted R-square;
    (b) Show how it was computed from the output;
    (b) What is the meaning of your adjusted R-square value?
  3. The Partial Regression Coefficients
    (a) Run the simple regression of y on one of your x-variables from your best model to show that this produces a sample slope coefficient that differs from the partial sample slope coefficient for the same x-variable when your other x-variables are in the model.
    (b) Describe the steps by which you can use a series of regressions to compute the above partial regression coefficient for the selected x-variable, above. This involves regressing y on those other x-variables, saving the residuals, and so on, until finally you regress one set of residuals on a second set of residuals.
    (c) Demonstrate that what you described in (b) actually works!
    (d) Output from SAS the partial R-square and the partial correlations coefficient for the same x-variable on which you analyzed the partial coefficient in your best model.
    (e) Using the same approach as in (c), show that the ordinary R-square for the regression of those residuals yields the partial R-square. Likewise for the partial correlation coefficient.
  4. Model Diagnostics
    (a) Explain what is shown by Cook’s distance diagnostic plot for your model.

Chapter 7
Run a ridge regression analysis of your data. Try in your own words to explain what that technique does and interpret the output. Supply your own Headers.