# Multiple Regression. Introduction In this chapter, we extend the simple linear regression model. Any number of independent variables is now allowed. We.

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Multiple Regression

Introduction In this chapter, we extend the simple linear regression model. Any number of independent variables is now allowed. We wish to build a model that fits the data better than the simple linear regression model.

Computer printout is used to help us: –Assess/Validate the model How well does it fit the data? Is it useful? Are any of the required conditions violated? –Apply the model Interpreting the coefficients Estimating the expected value of the dependent variable

Coefficients Dependent variableIndependent variables Random error variable Model and Required Conditions We allow for k independent variables to potentially be related to the dependent variable Y =  0 +  1 X 1 +  2 X 2 + …+  k X k + 

Multiple Regression for k = 2, Graphical Demonstration Y =  0 +  1 X X Y X2X2 1 The simple linear regression model allows for one independent variable, “X” Y =  0 +  1 X +  The multiple linear regression model allows for more than one independent variable. Y =  0 +  1 X 1 +  2 X 2 +  Note how the straight line becomes a plane Y =  0 +  1 X 1 +  2 X 2

The error  is normally distributed. The mean is equal to zero and the standard deviation is constant (    for all possible values of the X i s. All errors are independent. Required Conditions for the Error Variable

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

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, defined as having 50% or higher operating margin (net profit expressed as a percentage of total revenue). –Several potential predictors of profitability are: Competition (room supply) Market awareness (competing motel) Demand generators (office and college) Demographics (household income) Physical quality/location (distance to downtown)

Profitability Competition/ Supply Market Awareness Demand/ Customers Community Physical Operating Margin RoomsNearestOffice Space College Enrollment IncomeDisttwn Distance to downtown. Median household income. Distance to the nearest motel. Number of hotels/motels rooms within 3 miles from the site.

Data were collected from 100 randomly-selected inns that belong to La Quinta, and ran for the following suggested model: Margin =     Rooms   Nearest   Office    College +  5 Income +  6 Disttwn +  Model and Data Xm18-01

This is the sample regression equation (sometimes called the prediction equation) This is the sample regression equation (sometimes called the prediction equation) Excel Output Margin = 38.14 - 0.0076 Rooms +1.65 Nearest + 0.020 Office + 0.21 College + 0.41 Income - 0.23 Disttwn

Model Assessment The model is assessed using three measures: –The standard error of estimate –The coefficient of determination –The F-test of the analysis of variance The standard error of estimates is used in the calculations for the other measures.

The standard deviation of the error is estimated by the Standard Error of Estimate : (k+1 coefficients were estimated) The magnitude of s  is judged by comparing it to: Standard Error of Estimate

From the printout, s  = 5.51 The mean value of Y can be determined as: It seems that s  is not particularly small (relative to the mean of Y). Question: Can we conclude the model does not fit the data well? Not necessarily.

The definition is: From the printout, R 2 = 0.5251 52.51% of the variation in operating margin is explained by the six independent variables. 47.49% are unexplained. When adjusted for the impact of k relative to n (intended to flag potential problems with small sample size), we have: Adjusted R 2 = 1-[SSE/(n-k-1)] / [SS(Total)/(n-1)] = = 49.44% Coefficient of Determination

Consider the question: Is there at least one independent variable linearly related to the dependent variable? To answer this question, we test the hypothesis: H 0 :  1 =  2 = … =  k = 0 H 1 : At least one  i is not equal to zero. If at least one  i is not equal to zero, the model has some validity. The test is similar to an Analysis of Variance... Testing the Validity of the Model

The hypotheses can be tested by an ANOVA procedure. The Excel output is: MSE=SSE/(n-k-1) MSR=SSR/k MSR/MSE SSE SSR k = n–k–1 = n-1 = SSR: Sum of Squares for Regression SSE: Sum of Squares for Error

[Total Variation in Y] = SSR + SSE. Large F indicates a large SSR; that is, much of the variation in Y is explained by the regression model. Therefore, if F is large, the model is considered valid and hence the null hypothesis should be rejected. The Rejection Region: F>F ,k,n-k-1 As in analysis of variance, we have:

F ,k,n-k-1 = F 0.05,6,100-6-1 =2.17 F = 17.14 > 2.17 Also, the p-value (Significance F) = 0.0000 Reject the null hypothesis. Conclusion: There is sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis: at least one of the  i is not equal to zero. Thus, at least one independent variable is linearly related to Y. This linear regression model is valid

b 0 = 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. b 1 = – 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). Interpreting the Coefficients

b 2 = 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. b 3 = 0.020. For each additional 1000 sq-ft of office space, the operating margin will increase on average by.02%, when the other variables are held constant. b 4 = 0.21. For each additional thousand students, the operating margin increases on average by.21%, when the other variables are held constant.

b 5 = 0.41. For each increment of \$1000 in median household income, the operating margin would increase on average by.41%, when the other variables remain constant. b 6 = -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.

The hypothesis for each  i is: Excel output: H 0 :  i  0 H 1 :  i  0 d.f. = n - k -1 Test statistic Testing Individual Coefficients Insufficient Evidence Ignore

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% Xm18-01 La Quinta Inns, Point Estimate

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 (correlation between the X i ’s) a problem? Regression Diagnostics Draw a histogram of the residuals Plot the residuals versus the predicted values of Y Plot the residuals versus the time periods

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