Yesica Adicondro, profile picture
Uploaded byYesica Adicondro
PPT, PDF637 views

Introduction to Multiple Regression

The document describes multiple regression analysis and its applications in business decision making. It explains that multiple regression allows examination of the linear relationship between one dependent variable and two or more independent variables. The chapter goals are to help readers apply and interpret multiple regression, perform residual analysis, and test significance of variables. An example of using price and advertising spending to predict pie sales is provided to illustrate multiple regression concepts.

Embed presentation

Downloaded 52 times
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-1 Chapter 13 Introduction to Multiple Regression Statistics for Managers Using Microsoft® Excel 4th Edition
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-2 Chapter Goals After completing this chapter, you should be able to:  apply multiple regression analysis to business decision-making situations  analyze and interpret the computer output for a multiple regression model  perform residual analysis for the multiple regression model  test the significance of the independent variables in a multiple regression model
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-3 Chapter Goals After completing this chapter, you should be able to:  use a coefficient of partial determination to test portions of the multiple regression model  incorporate qualitative variables into the regression model by using dummy variables  use interaction terms in regression models (continued)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-4 The Multiple Regression Model Idea: Examine the linear relationship between 1 dependent (Y) & 2 or more independent variables (Xi) εXβXβXββY kik2i21i10i +++++=  Multiple Regression Model with k Independent Variables: Y-intercept Population slopes Random Error
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-5 Multiple Regression Equation The coefficients of the multiple regression model are estimated using sample data kik2i21i10i XbXbXbbYˆ ++++=  Estimated (or predicted) value of Y Estimated slope coefficients Multiple regression equation with k independent variables: Estimated intercept In this chapter we will always use Excel to obtain the regression slope coefficients and other regression summary measures.
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-6 Two variable model Y X1 X2 22110 XbXbbYˆ ++= Slope for variable X 1 Slope for variable X2 Multiple Regression Equation (continued)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-7 Example: 2 Independent Variables  A distributor of frozen desert pies wants to evaluate factors thought to influence demand  Dependent variable: Pie sales (units per week)  Independent variables: Price (in $) Advertising ($100’s)  Data are collected for 15 weeks
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-8 Pie Sales Example Sales = b0 + b1 (Price) + b2 (Advertising) Week Pie Sales Price ($) Advertising ($100s) 1 350 5.50 3.3 2 460 7.50 3.3 3 350 8.00 3.0 4 430 8.00 4.5 5 350 6.80 3.0 6 380 7.50 4.0 7 430 4.50 3.0 8 470 6.40 3.7 9 450 7.00 3.5 10 490 5.00 4.0 11 340 7.20 3.5 12 300 7.90 3.2 13 440 5.90 4.0 14 450 5.00 3.5 15 300 7.00 2.7 Multiple regression equation:
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-9 Estimating a Multiple Linear Regression Equation  Excel will be used to generate the coefficients and measures of goodness of fit for multiple regression  Excel:  Tools / Data Analysis... / Regression  PHStat:  PHStat / Regression / Multiple Regression…
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-10 Multiple Regression Output Regression Statistics Multiple R 0.72213 R Square 0.52148 Adjusted R Square 0.44172 Standard Error 47.46341 Observations 15 ANOVA df SS MS F Significance F Regression 2 29460.027 14730.013 6.53861 0.01201 Residual 12 27033.306 2252.776 Total 14 56493.333 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 306.52619 114.25389 2.68285 0.01993 57.58835 555.46404 Price -24.97509 10.83213 -2.30565 0.03979 -48.57626 -1.37392 Advertising 74.13096 25.96732 2.85478 0.01449 17.55303 130.70888 ertising)74.131(Advce)24.975(Pri-306.526Sales +=
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-11 The Multiple Regression Equation ertising)74.131(Advce)24.975(Pri-306.526Sales += b1 = -24.975: sales will decrease, on average, by 24.975 pies per week for each $1 increase in selling price, net of the effects of changes due to advertising b2 = 74.131: sales will increase, on average, by 74.131 pies per week for each $100 increase in advertising, net of the effects of changes due to price where Sales is in number of pies per week Price is in $ Advertising is in $100’s.
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-12 Using The Equation to Make Predictions Predict sales for a week in which the selling price is $5.50 and advertising is $350: Predicted sales is 428.62 pies 428.62 (3.5)74.131(5.50)24.975-306.526 ertising)74.131(Advce)24.975(Pri-306.526Sales = += += Note that Advertising is in $100’s, so $350 means that X2 = 3.5
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-13 Predictions in PHStat  PHStat | regression | multiple regression … Check the “confidence and prediction interval estimates” box
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-14 Input values Predictions in PHStat (continued) Predicted Y value < Confidence interval for the mean Y value, given these X’s < Prediction interval for an individual Y value, given these X’s <
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-15 Coefficient of Multiple Determination  Reports the proportion of total variation in Y explained by all X variables taken together squaresofsumtotal squaresofsumregression SST SSR r2 k..12.Y ==
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-16 Regression Statistics Multiple R 0.72213 R Square 0.52148 Adjusted R Square 0.44172 Standard Error 47.46341 Observations 15 ANOVA df SS MS F Significance F Regression 2 29460.027 14730.013 6.53861 0.01201 Residual 12 27033.306 2252.776 Total 14 56493.333 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 306.52619 114.25389 2.68285 0.01993 57.58835 555.46404 Price -24.97509 10.83213 -2.30565 0.03979 -48.57626 -1.37392 Advertising 74.13096 25.96732 2.85478 0.01449 17.55303 130.70888 .52148 56493.3 29460.0 SST SSR r2 Y.12 === 52.1% of the variation in pie sales is explained by the variation in price and advertising Multiple Coefficient of Determination (continued)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-17 Adjusted r2  r2 never decreases when a new X variable is added to the model  This can be a disadvantage when comparing models  What is the net effect of adding a new variable?  We lose a degree of freedom when a new X variable is added  Did the new X variable add enough explanatory power to offset the loss of one degree of freedom?
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-18  Shows the proportion of variation in Y explained by all X variables adjusted for the number of X variables used (where n = sample size, k = number of independent variables)  Penalize excessive use of unimportant independent variables  Smaller than r2  Useful in comparing among models Adjusted r2 (continued)             −− − −−= 1kn 1n )r1(1r 2 k..12.Y 2 adj
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-19 Regression Statistics Multiple R 0.72213 R Square 0.52148 Adjusted R Square 0.44172 Standard Error 47.46341 Observations 15 ANOVA df SS MS F Significance F Regression 2 29460.027 14730.013 6.53861 0.01201 Residual 12 27033.306 2252.776 Total 14 56493.333 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 306.52619 114.25389 2.68285 0.01993 57.58835 555.46404 Price -24.97509 10.83213 -2.30565 0.03979 -48.57626 -1.37392 Advertising 74.13096 25.96732 2.85478 0.01449 17.55303 130.70888 .44172r2 adj = 44.2% of the variation in pie sales is explained by the variation in price and advertising, taking into account the sample size and number of independent variables (continued) Adjusted r2
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-20 Two variable model Y X1 X2 22110 XbXbbYˆ ++=Yi Yi < x2i x1i The best fit equation, Y , is found by minimizing the sum of squared errors, Σe2 < Sample observation Residuals in Multiple Regression Residual = ei = (Yi – Yi) <
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-21 Multiple Regression Assumptions Assumptions:  The errors are normally distributed  Errors have a constant variance  The model errors are independent ei = (Yi – Yi) < Errors (residuals) from the regression model:
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-22 Residual Plots Used in Multiple Regression  These residual plots are used in multiple regression:  Residuals vs. Yi  Residuals vs. X1i  Residuals vs. X2i  Residuals vs. time (if time series data) < Use the residual plots to check for violations of regression assumptions
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-23 Is the Model Significant?  F-Test for Overall Significance of the Model  Shows if there is a linear relationship between all of the X variables considered together and Y  Use F test statistic  Hypotheses: H0: β1 = β2 = … = βk = 0 (no linear relationship) H1: at least one βi ≠ 0 (at least one independent variable affects Y)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-24 F-Test for Overall Significance  Test statistic: where F has (numerator) = k and (denominator) = (n – k - 1) degrees of freedom 1kn SSE k SSR MSE MSR F −− ==
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-25 6.5386 2252.8 14730.0 MSE MSR F === Regression Statistics Multiple R 0.72213 R Square 0.52148 Adjusted R Square 0.44172 Standard Error 47.46341 Observations 15 ANOVA df SS MS F Significance F Regression 2 29460.027 14730.013 6.53861 0.01201 Residual 12 27033.306 2252.776 Total 14 56493.333 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 306.52619 114.25389 2.68285 0.01993 57.58835 555.46404 Price -24.97509 10.83213 -2.30565 0.03979 -48.57626 -1.37392 Advertising 74.13096 25.96732 2.85478 0.01449 17.55303 130.70888 (continued) F-Test for Overall Significance With 2 and 12 degrees of freedom P-value for the F-Test
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-26 H0: β1 = β2 = 0 H1: β1 and β2 not both zero α = .05 df1= 2 df2 = 12 Test Statistic: Decision: Conclusion: Since F test statistic is in the rejection region (p- value < .05), reject H0 There is evidence that at least one independent variable affects Y 0 α = .05 F.05 = 3.885 Reject H0Do not reject H0 6.5386 MSE MSR F == Critical Value: Fα = 3.885 F-Test for Overall Significance (continued) F
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-27 Are Individual Variables Significant?  Use t-tests of individual variable slopes  Shows if there is a linear relationship between the variable Xi and Y  Hypotheses:  H0: βi = 0 (no linear relationship)  H1: βi ≠ 0 (linear relationship does exist between Xi and Y)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-28 Are Individual Variables Significant? H0: βi = 0 (no linear relationship) H1: βi ≠ 0 (linear relationship does exist between xi and y) Test Statistic: (df = n – k – 1) ib i S b t 0− = (continued)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-29 Regression Statistics Multiple R 0.72213 R Square 0.52148 Adjusted R Square 0.44172 Standard Error 47.46341 Observations 15 ANOVA df SS MS F Significance F Regression 2 29460.027 14730.013 6.53861 0.01201 Residual 12 27033.306 2252.776 Total 14 56493.333 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 306.52619 114.25389 2.68285 0.01993 57.58835 555.46404 Price -24.97509 10.83213 -2.30565 0.03979 -48.57626 -1.37392 Advertising 74.13096 25.96732 2.85478 0.01449 17.55303 130.70888 t-value for Price is t = -2.306, with p-value .0398 t-value for Advertising is t = 2.855, with p-value .0145 (continued) Are Individual Variables Significant?
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-30 d.f. = 15-2-1 = 12 α = .05 tα/2 = 2.1788 Inferences about the Slope: t Test Example H0: βi = 0 H1: βi ≠ 0 The test statistic for each variable falls in the rejection region (p-values < .05) There is evidence that both Price and Advertising affect pie sales at α = .05 From Excel output: Reject H0 for each variable Coefficients Standard Error t Stat P-value Price -24.97509 10.83213 -2.30565 0.03979 Advertising 74.13096 25.96732 2.85478 0.01449 Decision: Conclusion: Reject H0Reject H0 α/2=.025 -tα/2 Do not reject H0 0 tα/2 α/2=.025 -2.1788 2.1788
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-31 Confidence Interval Estimate for the Slope Confidence interval for the population slope βi Example: Form a 95% confidence interval for the effect of changes in price (X1) on pie sales: -24.975 ± (2.1788)(10.832) So the interval is (-48.576 , -1.374) ib1kni Stb −−± Coefficients Standard Error Intercept 306.52619 114.25389 Price -24.97509 10.83213 Advertising 74.13096 25.96732 where t has (n – k – 1) d.f. Here, t has (15 – 2 – 1) = 12 d.f.
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-32 Confidence Interval Estimate for the Slope Confidence interval for the population slope βi Example: Excel output also reports these interval endpoints: Weekly sales are estimated to be reduced by between 1.37 to 48.58 pies for each increase of $1 in the selling price Coefficients Standard Error … Lower 95% Upper 95% Intercept 306.52619 114.25389 … 57.58835 555.46404 Price -24.97509 10.83213 … -48.57626 -1.37392 Advertising 74.13096 25.96732 … 17.55303 130.70888 (continued)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-33  Contribution of a Single Independent Variable Xj SSR(Xj | all variables except Xj) = SSR (all variables) – SSR(all variables except Xj)  Measures the contribution of Xj in explaining the total variation in Y (SST) Testing Portions of the Multiple Regression Model
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-34 Measures the contribution of X1 in explaining SST From ANOVA section of regression for From ANOVA section of regression for Testing Portions of the Multiple Regression Model Contribution of a Single Independent Variable Xj, assuming all other variables are already included (consider here a 3-variable model): SSR(X1 | X2 and X3) = SSR (all variables) – SSR(X2 and X3) (continued) 3322110 XbXbXbbYˆ +++= 33220 XbXbbYˆ ++=
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-35 The Partial F-Test Statistic  Consider the hypothesis test: H0: variable Xj does not significantly improve the model after all other variables are included H1: variable Xj significantly improves the model after all other variables are included  Test using the F-test statistic: (with 1 and n-k-1 d.f.) MSE j)exceptvariablesall|(XSSR F j =
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-36 Testing Portions of Model: Example Test at the α = .05 level to determine whether the price variable significantly improves the model given that advertising is included Example: Frozen desert pies
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-37 Testing Portions of Model: Example H0: X1 (price) does not improve the model with X2 (advertising) included H1: X1 does improve model α = .05, df = 1 and 12 F critical Value = 4.75 (For X1 and X2) (For X2 only) ANOVA df SS MS Regression 2 29460.02687 14730.01343 Residual 12 27033.30647 2252.775539 Total 14 56493.33333 ANOVA df SS Regression 1 17484.22249 Residual 13 39009.11085 Total 14 56493.33333 (continued)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-38 Testing Portions of Model: Example Conclusion: Reject H0; adding X1 does improve model 316.5 78.2252 22.484,1703.460,29 MSE(all) )X|(XSSR F 21 = − == (continued) (For X1 and X2) (For X2 only) ANOVA df SS MS Regression 2 29460.02687 14730.01343 Residual 12 27033.30647 2252.775539 Total 14 56493.33333 ANOVA df SS Regression 1 17484.22249 Residual 13 39009.11085 Total 14 56493.33333
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-39 Coefficient of Partial Determination for k variable model  Measures the proportion of variation in the dependent variable that is explained by Xj while controlling for (holding constant) the other explanatory variables j)exceptvariablesall|SSR(Xvariables)SSR(allSST j)exceptvariablesall|(XSSR r j j 2 j)exceptvariablesYj.(all +− =
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-40 Coefficient of Partial Determination in Excel  Coefficients of Partial Determination can be found using Excel:  PHStat | regression | multiple regression …  Check the “coefficient of partial determination” box
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-41 Using Dummy Variables  A dummy variable is a categorical explanatory variable with two levels:  yes or no, on or off, male or female  coded as 0 or 1  Regression intercepts are different if the variable is significant  Assumes equal slopes for other variables  If more than two levels, the number of dummy variables needed is (number of levels - 1)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-42 Dummy-Variable Example (with 2 Levels) Let: Y = pie sales X1 = price X2 = holiday (X2 = 1 if a holiday occurred during the week) (X2 = 0 if there was no holiday that week) 210 XbXbbYˆ 21 ++=
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-43 Same slope Dummy-Variable Example (with 2 Levels) (continued) X1 (Price) Y (sales) b0 + b2 b0 1010 12010 Xbb(0)bXbbYˆ Xb)b(b(1)bXbbYˆ 121 121 +=++= ++=++= Holiday No Holiday Different intercept Holiday (X2 = 1)No Holiday (X2 = 0) If H0: β2 = 0 is rejected, then “Holiday” has a significant effect on pie sales
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-44 Sales: number of pies sold per week Price: pie price in $ Holiday: Interpreting the Dummy Variable Coefficient (with 2 Levels) Example: 1 If a holiday occurred during the week 0 If no holiday occurred b2 = 15: on average, sales were 15 pies greater in weeks with a holiday than in weeks without a holiday, given the same price )15(Holiday30(Price)-300Sales +=
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-45 Dummy-Variable Models (more than 2 Levels)  The number of dummy variables is one less than the number of levels  Example: Y = house price ; X1 = square feet  If style of the house is also thought to matter: Style = ranch, split level, condo Three levels, so two dummy variables are needed
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-46 Dummy-Variable Models (more than 2 Levels)  Example: Let “condo” be the default category, and let X2 and X3 be used for the other two categories: Y = house price X1 = square feet X2 = 1 if ranch, 0 otherwise X3 = 1 if split level, 0 otherwise The multiple regression equation is: 3322110 XbXbXbbYˆ +++= (continued)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-47 18.840.045X20.43Yˆ 1 ++= 23.530.045X20.43Yˆ 1 ++= Interpreting the Dummy Variable Coefficients (with 3 Levels) With the same square feet, a ranch will have an estimated average price of 23.53 thousand dollars more than a condo With the same square feet, a split-level will have an estimated average price of 18.84 thousand dollars more than a condo. Consider the regression equation: 321 18.84X23.53X0.045X20.43Yˆ +++= 10.045X20.43Yˆ += For a condo: X2 = X3 = 0 For a ranch: X2 = 1; X3 = 0 For a split level: X2 = 0; X3 = 1
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-48 Interaction Between Explanatory Variables  Hypothesizes interaction between pairs of X variables  Response to one X variable may vary at different levels of another X variable  Contains two-way cross product terms  )X(XbXbXbb XbXbXbbYˆ 21322110 3322110 +++= +++=
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-49 Effect of Interaction  Given:  Without interaction term, effect of X1 on Y is measured by β1  With interaction term, effect of X1 on Y is measured by β1 + β3 X2  Effect changes as X2 changes εXXβXβXββY 21322110 ++++=
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-50 X2 = 1: Y = 1 + 2X1 + 3(1) + 4X1(1) = 4 + 6X1 X2 = 0: Y = 1 + 2X1 + 3(0) + 4X1(0) = 1 + 2X1 Interaction Example Slopes are different if the effect of X1 on Y depends on X2 value X1 44 88 1212 00 00 110.50.5 1.51.5 Y = 1 + 2X1 + 3X2 + 4X1X2 Suppose X2 is a dummy variable and the estimated regression equation is Yˆ
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-51 Significance of Interaction Term  Can perform a partial F-test for the contribution of a variable to see if the addition of an interaction term improves the model  Multiple interaction terms can be included  Use a partial F-test for the simultaneous contribution of multiple variables to the model
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-52 Simultaneous Contribution of Explanatory Variables  Use partial F-test for the simultaneous contribution of multiple variables to the model  Let m variables be an additional set of variables added simultaneously  To test the hypothesis that the set of m variables improves the model: MSE(all) m/]variables)mofsetnewexcept(allSSR[SSR(all) F − = (where F has m and n-k-1 d.f.)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-53 Chapter Summary  Developed the multiple regression model  Tested the significance of the multiple regression model  Discussed adjusted r2  Discussed using residual plots to check model assumptions  Tested individual regression coefficients  Tested portions of the regression model  Used dummy variables  Evaluated interaction effects

More Related Content

PPT
multiple regression model building
byYesica Adicondro
 
PPT
Insight on multiple regression
byVijay Somu
 
PPT
Chap13 intro to multiple regression
byUni Azza Aunillah
 
PPT
Chap06 normal distributions & continous
byUni Azza Aunillah
 
PPT
Chap14 multiple regression model building
byUni Azza Aunillah
 
PPTX
Chap12 multiple regression
byJudianto Nugroho
 
PPT
04 regression
byFiras Husseini
 
PPT
Chap17 statistical applications on management
byUni Azza Aunillah
 
multiple regression model building
byYesica Adicondro
 
Insight on multiple regression
byVijay Somu
 
Chap13 intro to multiple regression
byUni Azza Aunillah
 
Chap06 normal distributions & continous
byUni Azza Aunillah
 
Chap14 multiple regression model building
byUni Azza Aunillah
 
Chap12 multiple regression
byJudianto Nugroho
 
04 regression
byFiras Husseini
 
Chap17 statistical applications on management
byUni Azza Aunillah
 

What's hot

PPTX
Linear Regression Analysis | Linear Regression in Python | Machine Learning A...
bySimplilearn
 
PPT
Chap11 chie square & non parametrics
byUni Azza Aunillah
 
PPT
Chap10 anova
byUni Azza Aunillah
 
PDF
Linear regression
byLeonardo Auslender
 
PPT
Chap01 intro & data collection
byUni Azza Aunillah
 
PPT
Simple Regression
byKhawaja Naveed
 
PPT
Chap03 numerical descriptive measures
byUni Azza Aunillah
 
PPT
Chap02 presenting data in chart & tables
byUni Azza Aunillah
 
PPT
Chap15 time series forecasting & index number
byUni Azza Aunillah
 
PPT
Simple Linier Regression
bydessybudiyanti
 
PPT
Chapter 14
byTuul Tuul
 
PDF
Linear regression
byVani011
 
PPT
Chap16 decision making
byUni Azza Aunillah
 
PPT
Chap08 fundamentals of hypothesis
byUni Azza Aunillah
 
PPT
Chapter 4 - multiple regression
byTauseef khan
 
PPTX
Regression analysis by akanksha Bali
byAkanksha Bali
 
PPTX
Regression analysis on SPSS
byAntimDevMishraMTechE
 
PPTX
Linear regression
byzekeLabs Technologies
 
PPTX
Basics of Regression analysis
byMahak Vijayvargiya
 
PPTX
Bivariate linear regression
byMenaal Kaushal
 
Linear Regression Analysis | Linear Regression in Python | Machine Learning A...
bySimplilearn
 
Chap11 chie square & non parametrics
byUni Azza Aunillah
 
Chap10 anova
byUni Azza Aunillah
 
Linear regression
byLeonardo Auslender
 
Chap01 intro & data collection
byUni Azza Aunillah
 
Simple Regression
byKhawaja Naveed
 
Chap03 numerical descriptive measures
byUni Azza Aunillah
 
Chap02 presenting data in chart & tables
byUni Azza Aunillah
 
Chap15 time series forecasting & index number
byUni Azza Aunillah
 
Simple Linier Regression
bydessybudiyanti
 
Chapter 14
byTuul Tuul
 
Linear regression
byVani011
 
Chap16 decision making
byUni Azza Aunillah
 
Chap08 fundamentals of hypothesis
byUni Azza Aunillah
 
Chapter 4 - multiple regression
byTauseef khan
 
Regression analysis by akanksha Bali
byAkanksha Bali
 
Regression analysis on SPSS
byAntimDevMishraMTechE
 
Linear regression
byzekeLabs Technologies
 
Basics of Regression analysis
byMahak Vijayvargiya
 
Bivariate linear regression
byMenaal Kaushal
 

Similar to Introduction to Multiple Regression

PDF
Bbs11 ppt ch14
byTuul Tuul
 
PPT
ch13 multiple regression ppt: introduction to multiple regression
byHannahIsabelleDinopo
 
PPTX
chap12.pptx
bySoujanyaLk1
 
PPT
Simple Linear Regression
byYesica Adicondro
 
PPT
Multiple Regression
byKhawaja Naveed
 
PPT
Chap12 simple regression
byUni Azza Aunillah
 
PPT
Newbold_chap13.ppt
bycfisicaster
 
PPTX
Forecasting Using the Predictive Analytics
byPRPrasad1
 
PPTX
4. regression analysis1
byKaran Kukreja
 
DOCX
© 2008 Prentice-Hall, Inc.Regression ModelsChapter 4.docx
byLynellBull52
 
PDF
Lecture slides stats1.13.l07.air
byatutor_te
 
PPT
Multiple Regression.ppt
byTanyaWadhwani4
 
PPT
Chapter14
byrwmiller
 
PPT
Chapter 4 power point presentation Regression models
byJustinXerri
 
DOCX
Convenience shoppingSTAT-S301Fall 2019Question Set 1.docx
bybobbywlane695641
 
PDF
14 ch ken black solution
byKrunal Shah
 
PDF
Bbs11 ppt ch13
byTuul Tuul
 
DOCX
DBM380 v14Create a DatabaseDBM380 v14Page 2 of 2Create a D.docx
byedwardmarivel
 
PPTX
An Introduction to Regression Models: Linear and Logistic approaches
byBhanu Yadav
 
PDF
Chapter6
byVu Vo
 
Bbs11 ppt ch14
byTuul Tuul
 
ch13 multiple regression ppt: introduction to multiple regression
byHannahIsabelleDinopo
 
chap12.pptx
bySoujanyaLk1
 
Simple Linear Regression
byYesica Adicondro
 
Multiple Regression
byKhawaja Naveed
 
Chap12 simple regression
byUni Azza Aunillah
 
Newbold_chap13.ppt
bycfisicaster
 
Forecasting Using the Predictive Analytics
byPRPrasad1
 
4. regression analysis1
byKaran Kukreja
 
© 2008 Prentice-Hall, Inc.Regression ModelsChapter 4.docx
byLynellBull52
 
Lecture slides stats1.13.l07.air
byatutor_te
 
Multiple Regression.ppt
byTanyaWadhwani4
 
Chapter14
byrwmiller
 
Chapter 4 power point presentation Regression models
byJustinXerri
 
Convenience shoppingSTAT-S301Fall 2019Question Set 1.docx
bybobbywlane695641
 
14 ch ken black solution
byKrunal Shah
 
Bbs11 ppt ch13
byTuul Tuul
 
DBM380 v14Create a DatabaseDBM380 v14Page 2 of 2Create a D.docx
byedwardmarivel
 
An Introduction to Regression Models: Linear and Logistic approaches
byBhanu Yadav
 
Chapter6
byVu Vo
 

More from Yesica Adicondro

PPTX
Strategi Tata Letak
byYesica Adicondro
 
PPT
Konsep Balanced Score Card
byYesica Adicondro
 
PPTX
Makalah kelompok Analisis Taksi Bakri
byYesica Adicondro
 
DOCX
Makalah kelompok Analisis Taksi Bakri
byYesica Adicondro
 
PPTX
Makalah Analisis PT Kereta API Indonesia
byYesica Adicondro
 
DOC
Makalah Analisis PT Kereta API Indonesia
byYesica Adicondro
 
PPTX
Makalah kelompok 3 gudang garam
byYesica Adicondro
 
DOC
Makalah Perusahaan Gudang Garam
byYesica Adicondro
 
PPTX
Makalah kelompok 2 garuda citilink PPT
byYesica Adicondro
 
DOCX
Makalah kelompok 2 garuda citilink
byYesica Adicondro
 
PDF
Dmfi leaflet indonesian
byYesica Adicondro
 
PDF
Dmfi booklet indonesian
byYesica Adicondro
 
PPTX
Makalah kinerja operasi Indonesia PPT
byYesica Adicondro
 
DOCX
Makalah kinerja operasi Indonesia
byYesica Adicondro
 
PPTX
Business process reengineering PPT
byYesica Adicondro
 
DOCX
Business process reengineering Makalah
byYesica Adicondro
 
PPTX
PPT Balanced Scorecard
byYesica Adicondro
 
DOCX
Makalah Balanced Scorecard
byYesica Adicondro
 
DOCX
Analisis Manajemen strategik PT garuda citilink
byYesica Adicondro
 
PPTX
analisis PPT PT Japfa
byYesica Adicondro
 
Strategi Tata Letak
byYesica Adicondro
 
Konsep Balanced Score Card
byYesica Adicondro
 
Makalah kelompok Analisis Taksi Bakri
byYesica Adicondro
 
Makalah kelompok Analisis Taksi Bakri
byYesica Adicondro
 
Makalah Analisis PT Kereta API Indonesia
byYesica Adicondro
 
Makalah Analisis PT Kereta API Indonesia
byYesica Adicondro
 
Makalah kelompok 3 gudang garam
byYesica Adicondro
 
Makalah Perusahaan Gudang Garam
byYesica Adicondro
 
Makalah kelompok 2 garuda citilink PPT
byYesica Adicondro
 
Makalah kelompok 2 garuda citilink
byYesica Adicondro
 
Dmfi leaflet indonesian
byYesica Adicondro
 
Dmfi booklet indonesian
byYesica Adicondro
 
Makalah kinerja operasi Indonesia PPT
byYesica Adicondro
 
Makalah kinerja operasi Indonesia
byYesica Adicondro
 
Business process reengineering PPT
byYesica Adicondro
 
Business process reengineering Makalah
byYesica Adicondro
 
PPT Balanced Scorecard
byYesica Adicondro
 
Makalah Balanced Scorecard
byYesica Adicondro
 
Analisis Manajemen strategik PT garuda citilink
byYesica Adicondro
 
analisis PPT PT Japfa
byYesica Adicondro
 

Recently uploaded

PDF
the-courtroom-color.pdf murdoku puzzless
byEduardoPortugal14
 
PDF
apidays Paris 2025 | Optimize your architecture for the next wave of AI-infus...
byapidays
 
PPTX
B2B_12345678876Marketing_5th_Lecture.pptx
byvandana1003
 
PDF
15th International Conference on Mobile & Wireless Networks (MoWiN 2026)
byijmnct
 
PPTX
Cybercrime-in-Academic-Institutions-in cyber world
byNishantKumar175068
 
PDF
AI 1.pdf AI in Business Training - Revolutionizing Business Operations
byCA Suvidha Chaplot
 
PPTX
project physics.pptx for class 12th students for therir schhol and enough
bykhantdevanshi12
 
PPTX
ppt for civil sustainable 1 st year enginnering.pptx
by9c2purnachandra
 
PPTX
LinkedIn Lead Generation expert for B2B Lead Generation
byMdBelalSharif1
 
PPT
The pathway of generic medecine, fulll analysis
bysangam862008
 
DOCX
MONITORING-ON-THE-RESUMPTION-OF-CLASSES-2026.docx
byMichaelMadelo1
 
PDF
AI 21.pdf Learn How AI Tools Optimize Decision Making, Marketing, HR, and More
byCA Suvidha Chaplot
 
PPTX
Scrum Master V5.2 - Trainer PPT new doc.pptx
byjaladi1231
 
PDF
5x9_five-nine-housingNNVRINFVURVRHFN.pdf
byboceb42621
 
PPTX
TISSUE EMBEDDING SYSTEM machine machine machine
byanantcyrix
 
PPTX
LONG QUIZ - TRENDS (Powerpoint Presentation)
bycharliepedrosa1
 
DOCX
An Educational Overview, Historical Context, and Key Lessons for Crypto Users...
bytopsellerit 4543
 
PPTX
Vector Space Modelin Information Storage and Retrieval .pptx
byhermelamisganew
 
PPTX
Free app market analytics for growth and competition insights - Appark
byAppark
 
PDF
Introduction to Neural Networks.pptx.pdf
byjatt66016
 
the-courtroom-color.pdf murdoku puzzless
byEduardoPortugal14
 
apidays Paris 2025 | Optimize your architecture for the next wave of AI-infus...
byapidays
 
B2B_12345678876Marketing_5th_Lecture.pptx
byvandana1003
 
15th International Conference on Mobile & Wireless Networks (MoWiN 2026)
byijmnct
 
Cybercrime-in-Academic-Institutions-in cyber world
byNishantKumar175068
 
AI 1.pdf AI in Business Training - Revolutionizing Business Operations
byCA Suvidha Chaplot
 
project physics.pptx for class 12th students for therir schhol and enough
bykhantdevanshi12
 
ppt for civil sustainable 1 st year enginnering.pptx
by9c2purnachandra
 
LinkedIn Lead Generation expert for B2B Lead Generation
byMdBelalSharif1
 
The pathway of generic medecine, fulll analysis
bysangam862008
 
MONITORING-ON-THE-RESUMPTION-OF-CLASSES-2026.docx
byMichaelMadelo1
 
AI 21.pdf Learn How AI Tools Optimize Decision Making, Marketing, HR, and More
byCA Suvidha Chaplot
 
Scrum Master V5.2 - Trainer PPT new doc.pptx
byjaladi1231
 
5x9_five-nine-housingNNVRINFVURVRHFN.pdf
byboceb42621
 
TISSUE EMBEDDING SYSTEM machine machine machine
byanantcyrix
 
LONG QUIZ - TRENDS (Powerpoint Presentation)
bycharliepedrosa1
 
An Educational Overview, Historical Context, and Key Lessons for Crypto Users...
bytopsellerit 4543
 
Vector Space Modelin Information Storage and Retrieval .pptx
byhermelamisganew
 
Free app market analytics for growth and competition insights - Appark
byAppark
 
Introduction to Neural Networks.pptx.pdf
byjatt66016
 

Introduction to Multiple Regression

  • 1.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-1 Chapter 13 Introduction to Multiple Regression Statistics for Managers Using Microsoft® Excel 4th Edition
  • 2.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-2 Chapter Goals After completing this chapter, you should be able to:  apply multiple regression analysis to business decision-making situations  analyze and interpret the computer output for a multiple regression model  perform residual analysis for the multiple regression model  test the significance of the independent variables in a multiple regression model
  • 3.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-3 Chapter Goals After completing this chapter, you should be able to:  use a coefficient of partial determination to test portions of the multiple regression model  incorporate qualitative variables into the regression model by using dummy variables  use interaction terms in regression models (continued)
  • 4.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-4 The Multiple Regression Model Idea: Examine the linear relationship between 1 dependent (Y) & 2 or more independent variables (Xi) εXβXβXββY kik2i21i10i +++++=  Multiple Regression Model with k Independent Variables: Y-intercept Population slopes Random Error
  • 5.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-5 Multiple Regression Equation The coefficients of the multiple regression model are estimated using sample data kik2i21i10i XbXbXbbYˆ ++++=  Estimated (or predicted) value of Y Estimated slope coefficients Multiple regression equation with k independent variables: Estimated intercept In this chapter we will always use Excel to obtain the regression slope coefficients and other regression summary measures.
  • 6.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-6 Two variable model Y X1 X2 22110 XbXbbYˆ ++= Slope for variable X 1 Slope for variable X2 Multiple Regression Equation (continued)
  • 7.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-7 Example: 2 Independent Variables  A distributor of frozen desert pies wants to evaluate factors thought to influence demand  Dependent variable: Pie sales (units per week)  Independent variables: Price (in $) Advertising ($100’s)  Data are collected for 15 weeks
  • 8.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-8 Pie Sales Example Sales = b0 + b1 (Price) + b2 (Advertising) Week Pie Sales Price ($) Advertising ($100s) 1 350 5.50 3.3 2 460 7.50 3.3 3 350 8.00 3.0 4 430 8.00 4.5 5 350 6.80 3.0 6 380 7.50 4.0 7 430 4.50 3.0 8 470 6.40 3.7 9 450 7.00 3.5 10 490 5.00 4.0 11 340 7.20 3.5 12 300 7.90 3.2 13 440 5.90 4.0 14 450 5.00 3.5 15 300 7.00 2.7 Multiple regression equation:
  • 9.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-9 Estimating a Multiple Linear Regression Equation  Excel will be used to generate the coefficients and measures of goodness of fit for multiple regression  Excel:  Tools / Data Analysis... / Regression  PHStat:  PHStat / Regression / Multiple Regression…
  • 10.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-10 Multiple Regression Output Regression Statistics Multiple R 0.72213 R Square 0.52148 Adjusted R Square 0.44172 Standard Error 47.46341 Observations 15 ANOVA df SS MS F Significance F Regression 2 29460.027 14730.013 6.53861 0.01201 Residual 12 27033.306 2252.776 Total 14 56493.333 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 306.52619 114.25389 2.68285 0.01993 57.58835 555.46404 Price -24.97509 10.83213 -2.30565 0.03979 -48.57626 -1.37392 Advertising 74.13096 25.96732 2.85478 0.01449 17.55303 130.70888 ertising)74.131(Advce)24.975(Pri-306.526Sales +=
  • 11.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-11 The Multiple Regression Equation ertising)74.131(Advce)24.975(Pri-306.526Sales += b1 = -24.975: sales will decrease, on average, by 24.975 pies per week for each $1 increase in selling price, net of the effects of changes due to advertising b2 = 74.131: sales will increase, on average, by 74.131 pies per week for each $100 increase in advertising, net of the effects of changes due to price where Sales is in number of pies per week Price is in $ Advertising is in $100’s.
  • 12.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-12 Using The Equation to Make Predictions Predict sales for a week in which the selling price is $5.50 and advertising is $350: Predicted sales is 428.62 pies 428.62 (3.5)74.131(5.50)24.975-306.526 ertising)74.131(Advce)24.975(Pri-306.526Sales = += += Note that Advertising is in $100’s, so $350 means that X2 = 3.5
  • 13.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-13 Predictions in PHStat  PHStat | regression | multiple regression … Check the “confidence and prediction interval estimates” box
  • 14.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-14 Input values Predictions in PHStat (continued) Predicted Y value < Confidence interval for the mean Y value, given these X’s < Prediction interval for an individual Y value, given these X’s <
  • 15.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-15 Coefficient of Multiple Determination  Reports the proportion of total variation in Y explained by all X variables taken together squaresofsumtotal squaresofsumregression SST SSR r2 k..12.Y ==
  • 16.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-16 Regression Statistics Multiple R 0.72213 R Square 0.52148 Adjusted R Square 0.44172 Standard Error 47.46341 Observations 15 ANOVA df SS MS F Significance F Regression 2 29460.027 14730.013 6.53861 0.01201 Residual 12 27033.306 2252.776 Total 14 56493.333 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 306.52619 114.25389 2.68285 0.01993 57.58835 555.46404 Price -24.97509 10.83213 -2.30565 0.03979 -48.57626 -1.37392 Advertising 74.13096 25.96732 2.85478 0.01449 17.55303 130.70888 .52148 56493.3 29460.0 SST SSR r2 Y.12 === 52.1% of the variation in pie sales is explained by the variation in price and advertising Multiple Coefficient of Determination (continued)
  • 17.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-17 Adjusted r2  r2 never decreases when a new X variable is added to the model  This can be a disadvantage when comparing models  What is the net effect of adding a new variable?  We lose a degree of freedom when a new X variable is added  Did the new X variable add enough explanatory power to offset the loss of one degree of freedom?
  • 18.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-18  Shows the proportion of variation in Y explained by all X variables adjusted for the number of X variables used (where n = sample size, k = number of independent variables)  Penalize excessive use of unimportant independent variables  Smaller than r2  Useful in comparing among models Adjusted r2 (continued)             −− − −−= 1kn 1n )r1(1r 2 k..12.Y 2 adj
  • 19.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-19 Regression Statistics Multiple R 0.72213 R Square 0.52148 Adjusted R Square 0.44172 Standard Error 47.46341 Observations 15 ANOVA df SS MS F Significance F Regression 2 29460.027 14730.013 6.53861 0.01201 Residual 12 27033.306 2252.776 Total 14 56493.333 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 306.52619 114.25389 2.68285 0.01993 57.58835 555.46404 Price -24.97509 10.83213 -2.30565 0.03979 -48.57626 -1.37392 Advertising 74.13096 25.96732 2.85478 0.01449 17.55303 130.70888 .44172r2 adj = 44.2% of the variation in pie sales is explained by the variation in price and advertising, taking into account the sample size and number of independent variables (continued) Adjusted r2
  • 20.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-20 Two variable model Y X1 X2 22110 XbXbbYˆ ++=Yi Yi < x2i x1i The best fit equation, Y , is found by minimizing the sum of squared errors, Σe2 < Sample observation Residuals in Multiple Regression Residual = ei = (Yi – Yi) <
  • 21.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-21 Multiple Regression Assumptions Assumptions:  The errors are normally distributed  Errors have a constant variance  The model errors are independent ei = (Yi – Yi) < Errors (residuals) from the regression model:
  • 22.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-22 Residual Plots Used in Multiple Regression  These residual plots are used in multiple regression:  Residuals vs. Yi  Residuals vs. X1i  Residuals vs. X2i  Residuals vs. time (if time series data) < Use the residual plots to check for violations of regression assumptions
  • 23.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-23 Is the Model Significant?  F-Test for Overall Significance of the Model  Shows if there is a linear relationship between all of the X variables considered together and Y  Use F test statistic  Hypotheses: H0: β1 = β2 = … = βk = 0 (no linear relationship) H1: at least one βi ≠ 0 (at least one independent variable affects Y)
  • 24.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-24 F-Test for Overall Significance  Test statistic: where F has (numerator) = k and (denominator) = (n – k - 1) degrees of freedom 1kn SSE k SSR MSE MSR F −− ==
  • 25.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-25 6.5386 2252.8 14730.0 MSE MSR F === Regression Statistics Multiple R 0.72213 R Square 0.52148 Adjusted R Square 0.44172 Standard Error 47.46341 Observations 15 ANOVA df SS MS F Significance F Regression 2 29460.027 14730.013 6.53861 0.01201 Residual 12 27033.306 2252.776 Total 14 56493.333 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 306.52619 114.25389 2.68285 0.01993 57.58835 555.46404 Price -24.97509 10.83213 -2.30565 0.03979 -48.57626 -1.37392 Advertising 74.13096 25.96732 2.85478 0.01449 17.55303 130.70888 (continued) F-Test for Overall Significance With 2 and 12 degrees of freedom P-value for the F-Test
  • 26.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-26 H0: β1 = β2 = 0 H1: β1 and β2 not both zero α = .05 df1= 2 df2 = 12 Test Statistic: Decision: Conclusion: Since F test statistic is in the rejection region (p- value < .05), reject H0 There is evidence that at least one independent variable affects Y 0 α = .05 F.05 = 3.885 Reject H0Do not reject H0 6.5386 MSE MSR F == Critical Value: Fα = 3.885 F-Test for Overall Significance (continued) F
  • 27.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-27 Are Individual Variables Significant?  Use t-tests of individual variable slopes  Shows if there is a linear relationship between the variable Xi and Y  Hypotheses:  H0: βi = 0 (no linear relationship)  H1: βi ≠ 0 (linear relationship does exist between Xi and Y)
  • 28.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-28 Are Individual Variables Significant? H0: βi = 0 (no linear relationship) H1: βi ≠ 0 (linear relationship does exist between xi and y) Test Statistic: (df = n – k – 1) ib i S b t 0− = (continued)
  • 29.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-29 Regression Statistics Multiple R 0.72213 R Square 0.52148 Adjusted R Square 0.44172 Standard Error 47.46341 Observations 15 ANOVA df SS MS F Significance F Regression 2 29460.027 14730.013 6.53861 0.01201 Residual 12 27033.306 2252.776 Total 14 56493.333 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 306.52619 114.25389 2.68285 0.01993 57.58835 555.46404 Price -24.97509 10.83213 -2.30565 0.03979 -48.57626 -1.37392 Advertising 74.13096 25.96732 2.85478 0.01449 17.55303 130.70888 t-value for Price is t = -2.306, with p-value .0398 t-value for Advertising is t = 2.855, with p-value .0145 (continued) Are Individual Variables Significant?
  • 30.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-30 d.f. = 15-2-1 = 12 α = .05 tα/2 = 2.1788 Inferences about the Slope: t Test Example H0: βi = 0 H1: βi ≠ 0 The test statistic for each variable falls in the rejection region (p-values < .05) There is evidence that both Price and Advertising affect pie sales at α = .05 From Excel output: Reject H0 for each variable Coefficients Standard Error t Stat P-value Price -24.97509 10.83213 -2.30565 0.03979 Advertising 74.13096 25.96732 2.85478 0.01449 Decision: Conclusion: Reject H0Reject H0 α/2=.025 -tα/2 Do not reject H0 0 tα/2 α/2=.025 -2.1788 2.1788
  • 31.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-31 Confidence Interval Estimate for the Slope Confidence interval for the population slope βi Example: Form a 95% confidence interval for the effect of changes in price (X1) on pie sales: -24.975 ± (2.1788)(10.832) So the interval is (-48.576 , -1.374) ib1kni Stb −−± Coefficients Standard Error Intercept 306.52619 114.25389 Price -24.97509 10.83213 Advertising 74.13096 25.96732 where t has (n – k – 1) d.f. Here, t has (15 – 2 – 1) = 12 d.f.
  • 32.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-32 Confidence Interval Estimate for the Slope Confidence interval for the population slope βi Example: Excel output also reports these interval endpoints: Weekly sales are estimated to be reduced by between 1.37 to 48.58 pies for each increase of $1 in the selling price Coefficients Standard Error … Lower 95% Upper 95% Intercept 306.52619 114.25389 … 57.58835 555.46404 Price -24.97509 10.83213 … -48.57626 -1.37392 Advertising 74.13096 25.96732 … 17.55303 130.70888 (continued)
  • 33.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-33  Contribution of a Single Independent Variable Xj SSR(Xj | all variables except Xj) = SSR (all variables) – SSR(all variables except Xj)  Measures the contribution of Xj in explaining the total variation in Y (SST) Testing Portions of the Multiple Regression Model
  • 34.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-34 Measures the contribution of X1 in explaining SST From ANOVA section of regression for From ANOVA section of regression for Testing Portions of the Multiple Regression Model Contribution of a Single Independent Variable Xj, assuming all other variables are already included (consider here a 3-variable model): SSR(X1 | X2 and X3) = SSR (all variables) – SSR(X2 and X3) (continued) 3322110 XbXbXbbYˆ +++= 33220 XbXbbYˆ ++=
  • 35.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-35 The Partial F-Test Statistic  Consider the hypothesis test: H0: variable Xj does not significantly improve the model after all other variables are included H1: variable Xj significantly improves the model after all other variables are included  Test using the F-test statistic: (with 1 and n-k-1 d.f.) MSE j)exceptvariablesall|(XSSR F j =
  • 36.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-36 Testing Portions of Model: Example Test at the α = .05 level to determine whether the price variable significantly improves the model given that advertising is included Example: Frozen desert pies
  • 37.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-37 Testing Portions of Model: Example H0: X1 (price) does not improve the model with X2 (advertising) included H1: X1 does improve model α = .05, df = 1 and 12 F critical Value = 4.75 (For X1 and X2) (For X2 only) ANOVA df SS MS Regression 2 29460.02687 14730.01343 Residual 12 27033.30647 2252.775539 Total 14 56493.33333 ANOVA df SS Regression 1 17484.22249 Residual 13 39009.11085 Total 14 56493.33333 (continued)
  • 38.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-38 Testing Portions of Model: Example Conclusion: Reject H0; adding X1 does improve model 316.5 78.2252 22.484,1703.460,29 MSE(all) )X|(XSSR F 21 = − == (continued) (For X1 and X2) (For X2 only) ANOVA df SS MS Regression 2 29460.02687 14730.01343 Residual 12 27033.30647 2252.775539 Total 14 56493.33333 ANOVA df SS Regression 1 17484.22249 Residual 13 39009.11085 Total 14 56493.33333
  • 39.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-39 Coefficient of Partial Determination for k variable model  Measures the proportion of variation in the dependent variable that is explained by Xj while controlling for (holding constant) the other explanatory variables j)exceptvariablesall|SSR(Xvariables)SSR(allSST j)exceptvariablesall|(XSSR r j j 2 j)exceptvariablesYj.(all +− =
  • 40.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-40 Coefficient of Partial Determination in Excel  Coefficients of Partial Determination can be found using Excel:  PHStat | regression | multiple regression …  Check the “coefficient of partial determination” box
  • 41.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-41 Using Dummy Variables  A dummy variable is a categorical explanatory variable with two levels:  yes or no, on or off, male or female  coded as 0 or 1  Regression intercepts are different if the variable is significant  Assumes equal slopes for other variables  If more than two levels, the number of dummy variables needed is (number of levels - 1)
  • 42.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-42 Dummy-Variable Example (with 2 Levels) Let: Y = pie sales X1 = price X2 = holiday (X2 = 1 if a holiday occurred during the week) (X2 = 0 if there was no holiday that week) 210 XbXbbYˆ 21 ++=
  • 43.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-43 Same slope Dummy-Variable Example (with 2 Levels) (continued) X1 (Price) Y (sales) b0 + b2 b0 1010 12010 Xbb(0)bXbbYˆ Xb)b(b(1)bXbbYˆ 121 121 +=++= ++=++= Holiday No Holiday Different intercept Holiday (X2 = 1)No Holiday (X2 = 0) If H0: β2 = 0 is rejected, then “Holiday” has a significant effect on pie sales
  • 44.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-44 Sales: number of pies sold per week Price: pie price in $ Holiday: Interpreting the Dummy Variable Coefficient (with 2 Levels) Example: 1 If a holiday occurred during the week 0 If no holiday occurred b2 = 15: on average, sales were 15 pies greater in weeks with a holiday than in weeks without a holiday, given the same price )15(Holiday30(Price)-300Sales +=
  • 45.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-45 Dummy-Variable Models (more than 2 Levels)  The number of dummy variables is one less than the number of levels  Example: Y = house price ; X1 = square feet  If style of the house is also thought to matter: Style = ranch, split level, condo Three levels, so two dummy variables are needed
  • 46.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-46 Dummy-Variable Models (more than 2 Levels)  Example: Let “condo” be the default category, and let X2 and X3 be used for the other two categories: Y = house price X1 = square feet X2 = 1 if ranch, 0 otherwise X3 = 1 if split level, 0 otherwise The multiple regression equation is: 3322110 XbXbXbbYˆ +++= (continued)
  • 47.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-47 18.840.045X20.43Yˆ 1 ++= 23.530.045X20.43Yˆ 1 ++= Interpreting the Dummy Variable Coefficients (with 3 Levels) With the same square feet, a ranch will have an estimated average price of 23.53 thousand dollars more than a condo With the same square feet, a split-level will have an estimated average price of 18.84 thousand dollars more than a condo. Consider the regression equation: 321 18.84X23.53X0.045X20.43Yˆ +++= 10.045X20.43Yˆ += For a condo: X2 = X3 = 0 For a ranch: X2 = 1; X3 = 0 For a split level: X2 = 0; X3 = 1
  • 48.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-48 Interaction Between Explanatory Variables  Hypothesizes interaction between pairs of X variables  Response to one X variable may vary at different levels of another X variable  Contains two-way cross product terms  )X(XbXbXbb XbXbXbbYˆ 21322110 3322110 +++= +++=
  • 49.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-49 Effect of Interaction  Given:  Without interaction term, effect of X1 on Y is measured by β1  With interaction term, effect of X1 on Y is measured by β1 + β3 X2  Effect changes as X2 changes εXXβXβXββY 21322110 ++++=
  • 50.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-50 X2 = 1: Y = 1 + 2X1 + 3(1) + 4X1(1) = 4 + 6X1 X2 = 0: Y = 1 + 2X1 + 3(0) + 4X1(0) = 1 + 2X1 Interaction Example Slopes are different if the effect of X1 on Y depends on X2 value X1 44 88 1212 00 00 110.50.5 1.51.5 Y = 1 + 2X1 + 3X2 + 4X1X2 Suppose X2 is a dummy variable and the estimated regression equation is Yˆ
  • 51.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-51 Significance of Interaction Term  Can perform a partial F-test for the contribution of a variable to see if the addition of an interaction term improves the model  Multiple interaction terms can be included  Use a partial F-test for the simultaneous contribution of multiple variables to the model
  • 52.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-52 Simultaneous Contribution of Explanatory Variables  Use partial F-test for the simultaneous contribution of multiple variables to the model  Let m variables be an additional set of variables added simultaneously  To test the hypothesis that the set of m variables improves the model: MSE(all) m/]variables)mofsetnewexcept(allSSR[SSR(all) F − = (where F has m and n-k-1 d.f.)
  • 53.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 13-53 Chapter Summary  Developed the multiple regression model  Tested the significance of the multiple regression model  Discussed adjusted r2  Discussed using residual plots to check model assumptions  Tested individual regression coefficients  Tested portions of the regression model  Used dummy variables  Evaluated interaction effects