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Multiple Regression

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:

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Multiple Regression

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  1. Multiple Regression

  2. 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.

  3. 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

  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 Y The simple linear regression model allows for one independent variable, “X” Y = b0 + b1X + e Y = b0 + b1X Y = b0 + b1X Note how the straight line becomes a plane Y = b0 + b1X1 + b2X2 Y = b0 + b1X1 + b2X2 Y = b0 + b1X1 + b2X2 X 1 The multiple linear regression model allows for more than one independent variable. Y = b0 + b1X1 + b2X2 + e X2

  6. 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 possible values of the Xis. • All errors are independent.

  7. Estimating the Coefficients and Assessing the Model • The procedure used to perform regression analysis: • Obtain the model coefficients and statistics using Excel. • 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.

  8. 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)

  9. Physical Operating Margin Profitability Competition/ Supply Market Awareness Demand/ 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 motel. Distance to downtown.

  10. Model and Data • Data were collected from 100 randomly-selected inns that belong to La Quinta, and ran for the following suggested model: Margin = b0 + b1Rooms + b2Nearest + b3Office + b4College + b5 Income + b6 Disttwn + e Xm18-01

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

  12. 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.

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

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

  15. 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% are unexplained. • When adjusted for the impact of k relative to n (intended to flag potential problems with small sample size), we have: Adjusted R2 = 1-[SSE/(n-k-1)] / [SS(Total)/(n-1)] = = 49.44%

  16. Testing the Validity of the Model • Consider the question: Is there at least one independent variable linearly related to the dependent variable? • To answer this question, we test the hypothesis: H0: b1 = b2 = … = bk = 0 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. • The test is similar to an Analysis of Variance ...

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

  18. As in analysis of variance, we have: [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>Fa,k,n-k-1

  19. 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. 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

  20. 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).

  21. 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.

  22. b5 = 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. • 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.

  23. Test statistic Testing Individual Coefficients • The hypothesis for each bi is: • Excel output: H0: bi= 0 H1: bi¹ 0 d.f. = n - k -1 Insufficient Evidence Ignore Insufficient Evidence

  24. La Quinta Inns, Point Estimate Xm18-01 • 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%

  25. Plot the residuals versus the predicted values of Y Regression Diagnostics • 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 Xi’s) a problem? Draw a histogram of the residuals Plot the residuals versus the time periods

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