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Linear Regression and Prediction Assignment [ADD IN YOUR CLAIM] Step 1. Collect Data Sets. FIND THE DATA SET(S) YOU NEED TO SUPPORT YOUR CLAIM. 2.
Linear Regression and Prediction Assignment [ADD IN YOUR CLAIM] Step 1. Collect Data Sets. FIND THE DATA SET(S) YOU NEED TO SUPPORT YOUR CLAIM. 2. Examine the data & run summary statistics. Discuss these BRIEFLY (review chapter 4 if necessary) 3. Determine relationships & format data set a) what is your dependent variable - format your data set appropriately (i.e. think \"wins\" or \"win %\" from basketball discussion b) what are your potential explanatory variables? Do you have (need) squares, interactions or binary variables? Format your data set appropriately. 4. Do some correlation analysis a) Do a scatter plot. b) Does the evidence suggest that there is a correlation c) Are there any 'weird' patterns? d) Drop any variables that seemingly lack in explanatory value 5. Run a regression on data set Look at R-squared & Adjusted R-squared. Comment on them. Do you have evidence of unnecessary variables? Do your explanatory variables seemingly capture much of the variation? Estimate of intercept - is it statistically different from zero. If not, could you justify a RTO (i.e. regression through origin)? Re-run if necessary. Coefficient estimates - rank by p-value. If there are very high p-values, consider dropping those explanatory variables and re-running the regression BEFORE continuing Do the signs of the coefficients make sense? What about magnitudes? Redo step 4 every time you re-run your regression. Stop when you feel like you have a 'good fit' 6. Fitted equation a) write out the estimated model b) Add the trend line to your scatter plot. Make sure that the fitted regression line equation is consistent with your equation in (a) 7. Plot the standard residuals vs. the fitted values. Discuss any issues that arise (i.e. heteroskedacity?, non-normal residuals? Non-constant variance?) 8. Predict THE NEXT ONE (OR MORE) VALUES USING YOUR REGRESSION MODEL.. Do your predictions hold up? Do they make sense? 9. COMMENT ON THE REGRESSION. What else might you do? Are you satisfied with the model? Does it lack something? If so, what might that be? Are there possible non-independence issues that you'd like to resolve? Etc
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