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TESTING THE STRENGTH OF THE MULTIPLE REGRESSION MODEL

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Test 1: Are Any of the x’s Useful in Predicting y? We are asking: Can we conclude at least one of the ’s (other than 0 ) 0? H 0 : 1 = 2 = 3 = 4 = 0 H A : At least one of these ’s 0 =.05

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Idea of the Test Measure the overall “average variability” due to changes in the x’s Measure the overall “average variability” that is due to randomness (error) IS A LOT LARGERIf the overall “average variability” due to changes in the x’s IS A LOT LARGER than “average variability” due to error, we conclude at least is non-zero, i.e. at least one factor (x) is useful in predicting y

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“Total Variability” Just like with simple linear regression we have total sum of squares due to regression SSR, and total sum of squares due to error, SSE, which are printed on the EXCEL output. –The formulas are a more complicated (they involve matrix operations)

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“Average Variability” “Average variability” (Mean variability) for a group is defined as the Total Variability divided by the degrees of freedom associated with that group: Mean Squares Due to Regression MSR = SSR/DFR Mean Squares Due to Error MSE = SSE/DFE

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Degrees of Freedom Total number of degrees of freedom DF(Total) always = n-1 Degrees of freedom for regression (DFR) = the number of factors in the regression (i.e. the number of x’s in the linear regression) Degrees of freedom for error (DFE) = difference between the two = DF(Total) -DFR

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The F-Statistic The F-statistic is defined as the ratio of two measures of variability. Here, Recall we are saying if MSR is “large” compared to MSE, at least one β ≠ 0. Thus if F is “large”, we draw the conclusion is that H A is true, i.e. at least one β ≠ 0.

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The F-test “Large” compared to what? F-tables give critical values for given values of TEST: REJECT H 0 (Accept H A ) if: F = MSR/MSE > F ,DFR,DFE

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RESULTS If we do not get a large F statistic –We cannot conclude that any of the variables in this model are significant in predicting y. If we do get a large F statistic –We can conclude at least one of the variables is significant for predicting y. –NATURAL QUESTION -- WHICH ONES?

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DFR = #x’s DFE = Total DF- DFR Total DF = n-1 SSR SSE Total SS = (y i - ) 2

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MSR = SSR/DFR MSE = SSE/DFE F = MSR/MSE P-value for the F test

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Results We see that the F statistic is 20.89762 This would be compared to F.05,3,34 –From the F.05 Table, the value of F.05,3,34 is not given. –But F.05,3,30 = 2.92 and F.05,3,40 = 2.84. –And 20.89762 > either of these numbers. –The actual value of F.05,3,34 can be calculated by Excel by FINV(.05,3,34) = 2.882601 USE SIGNIFICANCE FUSE SIGNIFICANCE F p-value –This is the p-value for the F-Test –Significance F = 7.46 x 10 -8 =.0000000746 <.05 –Can conclude that at least one x is useful in predicting y

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Test 2: Which Variables Are Significant IN THIS MODEL? The question we are asking is, “taking all the other factors (x’s) into consideration, does a change in a particular x (x 3, say) value significantly affect y. This is another hypothesis test (a t-test). To test if the age of the house is significant: in this model H 0 : 3 = 0 (x 3 is not significant in this model) in this model H A : 3 0 (x 3 is significant in this model)

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The t-test for a particular factor IN THIS MODEL Reject H 0 (Accept H A ) if:

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t-value for test of 3 = 0p-value for test of 3 = 0

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Reading Printout for the t-test Simply look at the p-value –p-value for 3 = 0 is.02194 <.05 in this modelThus the age of the house is significant in this model The other variables –p-value for 1 = 0 is.0000839 <.05 in this modelThus square feet is significant in this model –p-value for 2 = 0 is.15503 >.05 in this modelThus the land (acres) is not significant in this model

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Does A Poor t-value Imply the Variable is not Useful in Predicting y? NO IN THIS MODELIt says the variable is not significant IN THIS MODEL when we consider all the other factors. In this model – land is not significant when included with square footage and age. But if we would have run this model without square footage we would have gotten the output on the next slide.

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p-value for land is.00000717. In this model Land is significant.

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Can it even happen that F says at least one variable is significant, but none of the t’s indicate a useful variable? YES EXAMPLES IN WHICH THIS MIGHT HAPPEN: –Miles per gallon vs. horsepower and engine size –Salary vs. GPA and GPA in major –Income vs. age and experience –HOUSE PRICE vs. SQUARE FOOTAGE OF HOUSE AND LAND There is a relation between the x’s – –Multicollinearity

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Approaches That Could Be Used When Multicollinearity Is Detected Eliminate some variables and run again Stepwise regression This is discussed in a future module.

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Test 3 --What Proportion of the Overall Variability in y Is Due to Changes in the x’s? R2R2 R 2 =.442197 Overall 44% of the total variation in sales price is explained by changes in square footage, land, and age of the house.

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What is Adjusted R 2 ? Adjusted R 2 adjusts R 2 to take into account degrees of freedom. By assuming a higher order equation for y, we can force the curve to fit this one set of data points in the model – eliminating much of the variability (See next slide). But this is not what is going on! R 2 might be higher – but adjusted R 2 might be much lower Adjusted R 2 takes this into account Adjusted R 2 = 1-MSE/SST

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Scatterplot This is not what is really going on

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Review Are any of the x’s useful in predicting y IN THIS MODEL –Look at p-value for F-test – Significance F –F = MSR/MSE would be compared to F ,DFR,DFE Which variables are significant in this model? –Look at p-values for the individual t-tests What proportion of the total variance in y can be explained by changes in the x’s? –R2–R2 –Adjusted R 2 takes into account the reduced degrees of freedom for the error term by including more terms in the model

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1-regression equation 3- p-values for t-tests Which variables are significant in this model? 4- R 2 What proportion of y can be explained by changes in x? 4 Places to Look on Excel Printout 2- Significance F Are any variables useful?

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Correlation and Linear Regression Chapter 13 Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.

Correlation and Linear Regression Chapter 13 Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. McGraw-Hill/Irwin.

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