Multiple Regression

1. Multiple Regression

2. Introduction • In this chapter we extend the simple linear regression model, and allow for any number of independent variables. • We expect to build a model that fits the data better than the simple linear regression model.

3. Introduction • We shall use computer printout to • Assess the model • How well it fits the data • Is it useful • Are any required conditions violated? • Employ the model • Interpreting the coefficients • Predictions using the prediction equation • Estimating the expected value of the dependent variable

4. Dependent variable Independent variables Model and Required Conditions • We allow for k independent variables to potentially be related to the dependent variable y = b0 + b1x1+ b2x2 + …+ bkxk + e Coefficients Random error variable

5. Multiple Regression for k = 2, Graphical Demonstration - I y The simple linear regression model allows for one independent variable, “x” y =b0 + b1x + e y = b0 + b1x y = b0 + b1x y = b0 + b1x y = b0 + b1x Note how the straight line becomes a plain, and... y = b0 + b1x1 + b2x2 y = b0 + b1x1 + b2x2 y = b0 + b1x1 + b2x2 y = b0 + b1x1 + b2x2 y = b0 + b1x1 + b2x2 X y = b0 + b1x1 + b2x2 1 y = b0 + b1x1 + b2x2 The multiple linear regression model allows for more than one independent variable. Y = b0 + b1x1 + b2x2 + e X2

6. Multiple Regression for k = 2, Graphical Demonstration - II y y= b0+ b1x2 Note how a parabola becomes a parabolic Surface. b0 X 1 y = b0 + b1x12 + b2x2 X2

7. Required conditions for the error variable • The error e is normally distributed. • The mean is equal to zero and the standard deviation is constant (se)for all values of y. • The errors are independent.

8. Estimating the Coefficients and Assessing the Model • The procedure used to perform regression analysis: • Obtain the model coefficients and statistics using a statistical software. • Diagnose violations of required conditions. Try to remedy problems when identified. • Assess the model fit using statistics obtained from the sample. • If the model assessment indicates good fit to the data, use it to interpret the coefficients and generate predictions.

9. Estimating the Coefficients and Assessing the Model, Example • Example 18.1 Where to locate a new motor inn? • La Quinta Motor Inns is planning an expansion. • Management wishes to predict which sites are likely to be profitable. • Several areas where predictors of profitability can be identified are: • Competition • Market awareness • Demand generators • Demographics • Physical quality

10. Physical Margin Profitability Competition Market awareness Customers Community Rooms Nearest Office space College enrollment Income Disttwn Median household income. Number of hotels/motels rooms within 3 miles from the site. Distance to the nearest La Quinta inn. Distance to downtown.

11. Physical Estimating the Coefficients and Assessing the Model, Example Operating Margin Profitability Market awareness Competition Customers Community Rooms Nearest Office space College enrollment Income Disttwn Median household income. Number of hotels/motels rooms within 3 miles from the site. Distance to the nearest La Quinta inn. Distance to downtown.

12. Estimating the Coefficients and Assessing the Model, Example • Data were collected from randomly selected 100 inns that belong to La Quinta, and ran for the following suggested model: Margin = b0 + b1Rooms + b2Nearest + b3Office + b4College + b5Income + b6Disttwn Xm18-01

13. Regression Analysis, Excel Output This is the sample regression equation (sometimes called the prediction equation) Margin = 38.14 - 0.0076Number +1.65Nearest + 0.020Office Space +0.21Enrollment + 0.41Income - 0.23Distance

14. Model Assessment • The model is assessed using three tools: • The standard error of estimate • The coefficient of determination • The F-test of the analysis of variance • The standard error of estimates participates in building the other tools.

15. Standard Error of Estimate • The standard deviation of the error is estimated by the Standard Error of Estimate: • The magnitude of seis judged by comparing it to

16. Standard Error of Estimate • From the printout, se = 5.51 • Calculating the mean value of y we have • It seems se is not particularly small. • Question:Can we conclude the model does not fit the data well?

17. Coefficient of Determination • The definition is • From the printout, R2 = 0.5251 • 52.51% of the variation in operating margin is explained by the six independent variables. 47.49% remains unexplained. • When adjusted for degrees of freedom, Adjusted R2 = 1-[SSE/(n-k-1)] / [SS(Total)/(n-1)] = = 49.44%

18. Testing the Validity of the Model • We pose the question: Is there at least one independent variable linearly related to the dependent variable? • To answer the question we test the hypothesis H0: b0 = b1 = b2 = … = bk H1: At least one bi is not equal to zero. • If at least one bi is not equal to zero, the model has some validity.

19. Testing the Validity of the La Quinta Inns Regression Model • The hypotheses are tested by an ANOVA procedure ( the Excel output) MSR/MSE k = n–k–1 = n-1 = SSR MSR=SSR/k MSE=SSE/(n-k-1) SSE

20. Testing the Validity of the La Quinta Inns Regression Model [Variation in y] = SSR + SSE. Large F results from a large SSR. Then, much of the variation in y is explained by the regression model; the model is useful, and thus, the null hypothesis should be rejected. Therefore, the rejection region is… • Rejection region • F>Fa,k,n-k-1

21. Testing the Validity of the La Quinta Inns Regression Model Conclusion: There is sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis. At least one of the bi is not equal to zero. Thus, at least one independent variable is linearly related to y. This linear regression model is valid Fa,k,n-k-1 = F0.05,6,100-6-1=2.17 F = 17.14 > 2.17 Also, the p-value (Significance F) = 0.0000 Reject the null hypothesis.

22. Interpreting the Coefficients • b0 = 38.14. This is the intercept, the value of y when all the variables take the value zero. Since the data range of all the independent variables do not cover the value zero, do not interpret the intercept. • b1 = – 0.0076. In this model, for each additional room within 3 mile of the La Quinta inn, the operating margin decreases on average by .0076% (assuming the other variables are held constant).

23. Interpreting the Coefficients • b2 = 1.65. In this model, for each additional mile that the nearest competitor is to a La Quinta inn, the operating margin increases on average by 1.65% when the other variables are held constant. • b3 = 0.020.For each additional 1000 sq-ft of office space, the operating margin will increase on average by .02% when the othervariables are held constant. • b4 = 0.21. For each additional thousand students the operating margin increases on average by .21% when the othervariables are held constant.

24. Interpreting the Coefficients • b5 = 0.41. For additional $1000 increase in median household income, the operating margin increases on average by .41%, when the other variables remain constant. • b6 = -0.23. For each additional mile to the downtown center, the operating margin decreases on average by .23% when the other variables are held constant.

25. Test statistic Testing the Coefficients • The hypothesis for each bi is • Excel printout H0: bi= 0 H1: bi¹ 0 d.f. = n - k -1

26. Using the Linear Regression Equation • The model can be used for making predictions by • Producing prediction interval estimate for the particular value of y, for a given values of xi. • Producing a confidence interval estimate for the expected value of y, for given values of xi. • The model can be used to learn about relationships between the independent variables xi, and the dependent variable y, by interpreting the coefficients bi

27. La Quinta Inns, Predictions • Predict the average operating margin of an inn at a site with the following characteristics: • 3815 rooms within 3 miles, • Closet competitor .9 miles away, • 476,000 sq-ft of office space, • 24,500 college students, • $35,000 median household income, • 11.2 miles distance to downtown center. MARGIN = 38.14 - 0.0076(3815)+1.65(.9) + 0.020(476) +0.21(24.5) + 0.41(35) - 0.23(11.2) = 37.1%

28. Assessment and Interpretation:MBA Program Admission Policy • The dean of a large university wants to raise the admission standards to the popular MBA program. • She plans to develop a method that can predict an applicant’s performance in the program. • She believes a student’s success can be predicted by: • Undergraduate GPA • Graduate Management Admission Test (GMAT) score • Number of years of work experience

29. MBA Program Admission Policy • A randomly selected sample of students who completed the MBA was selected. • Develop a plan to decide which applicant to admit.

30. MBA Program Admission Policy • Solution • The model to estimate is:y = b0 +b1x1+ b2x2+ b3x3+ey = MBA GPAx1 = undergraduate GPA [UnderGPA]x2 = GMAT score [GMAT]x3 = years of work experience [Work] • The estimated model:MBA GPA = b0 + b1UnderGPA + b2GMAT + b3Work

31. MBA Program Admission Policy – Model Diagnostics We estimate the regression model then we check: • Normality of errors

32. MBA Program Admission Policy – Model Diagnostics We estimate the regression model then we check: • The variance of the error variable

33. MBA Program Admission Policy – Model Diagnostics

34. 46.35% of the variation in MBA GPA is explained by the model. • The model is valid (p-value = 0.0000…) • GMAT score and years of work experience are linearly related to MBA GPA. • Insufficient evidence of linear relationship between undergraduate GPA and MBA GPA. MBA Program Admission Policy – Model Assessment

35. ^ Plot the residuals versus y Regression Diagnostics - II • The conditions required for the model assessment to apply must be checked. • Is the error variable normally distributed? • Is the error variance constant? • Are the errors independent? • Can we identify outlier? • Is multicolinearity (intercorrelation)a problem? Draw a histogram of the residuals Plot the residuals versus the time periods

36. Diagnostics: Multicolinearity • Example: Predicting house price (Xm18-02) • A real estate agent believes that a house selling price can be predicted using the house size, number of bedrooms, and lot size. • A random sample of 100 houses was drawn and data recorded. • Analyze the relationship among the four variables

37. Diagnostics: Multicolinearity • The proposed model isPRICE = b0 + b1BEDROOMS + b2H-SIZE +b3LOTSIZE + e The model is valid, but no variable is significantly related to the selling price ?!

38. Diagnostics: Multicolinearity • Multicolinearity is found to be a problem. • Multicolinearity causes two kinds of difficulties: • The t statistics appear to be too small. • The b coefficients cannot be interpreted as “slopes”.

39. Remedying Violations of the Required Conditions • Nonnormality or heteroscedasticity can be remedied using transformations on the y variable. • The transformations can improve the linear relationship between the dependent variable and the independent variables. • Many computer software systems allow us to make the transformations easily.