Скачать презентацию TESTING THE STRENGTH OF THE MULTIPLE REGRESSION MODEL

f439ae6b45f672fe9df6f48c9502e7ce.ppt

• Количество слайдов: 25

TESTING THE STRENGTH OF THE MULTIPLE REGRESSION MODEL

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 HA: At least one of these ’s 0 =. 05

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) • If 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

“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)

“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

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

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 HA is true, i. e. at least one β ≠ 0.

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

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?

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

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

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 F – 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

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: H 0: 3 = 0 (x 3 is not significant in this model) model HA: 3 0 (x 3 is significant in this model) model

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

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

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

Does A Poor t-value Imply the Variable is not Useful in Predicting y? • NO • It 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.

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

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

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

Test 3 --What Proportion of the Overall Variability in y Is Due to Changes in the x’s? R 2 • 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.

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

Scatterplot This is not what is really going on

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? – R 2 – Adjusted R 2 takes into account the reduced degrees of freedom for the error term by including more terms in the model

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