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This chapter discusses simple linear regression analysis. It explains the simple linear regression model and how to obtain and interpret the linear regression equation for a set of data. It also discusses evaluating regression residuals to assess model fit, assumptions of regression analysis, and interpreting regression coefficients and using the model to make predictions. An example using house price and square footage data is analyzed using Excel to demonstrate simple linear regression.

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Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-1 Chapter 12 Simple Linear Regression Statistics for Managers Using Microsoft® Excel 4th Edition
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-2 Chapter Goals After completing this chapter, you should be able to:  Explain the simple linear regression model  Obtain and interpret the simple linear regression equation for a set of data  Evaluate regression residuals for aptness of the fitted model  Understand the assumptions behind regression analysis  Explain measures of variation and determine whether the independent variable is significant
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-3 Chapter Goals After completing this chapter, you should be able to:  Calculate and interpret confidence intervals for the regression coefficients  Use the Durbin-Watson statistic to check for autocorrelation  Form confidence and prediction intervals around an estimated Y value for a given X  Recognize some potential problems if regression analysis is used incorrectly (continued)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-4 Correlation vs. Regression  A scatter plot (or scatter diagram) can be used to show the relationship between two variables  Correlation analysis is used to measure strength of the association (linear relationship) between two variables  Correlation is only concerned with strength of the relationship  No causal effect is implied with correlation  Correlation was first presented in Chapter 3
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-5 Regression Analysis  Regression analysis is used to:  Predict the value of a dependent variable based on the value of at least one independent variable  Explain the impact of changes in an independent variable on the dependent variable Dependent variable: the variable we wish to explain Independent variable: the variable used to explain the dependent variable
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-6 Simple Linear Regression Model  Only one independent variable, X  Relationship between X and Y is described by a linear function  Changes in Y are assumed to be caused by changes in X
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-7 Types of Relationships Y X Y X Y Y X X Linear relationships Curvilinear relationships
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-8 Types of Relationships Y X Y X Y Y X X Strong relationships Weak relationships (continued)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-9 Types of Relationships Y X Y X No relationship (continued)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-10 i i 1 0 i ε X β β Y    Linear component Population Linear Regression Equation The population regression model: Population Y intercept Population Slope Coefficient Random Error term, or residual Dependent Variable Independent Variable Random Error component
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-11 i 1 0 i X b b Ŷ   The simple linear regression equation provides an estimate of the population regression line Sample Linear Regression Equation Estimate of the regression intercept Estimate of the regression slope Estimated (or predicted) Y value for observation i Value of X for observation i The individual random error terms ei have a mean of zero
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-12 Finding the Least Squares Equation  The coefficients b0 and b1 , and other regression results in this chapter, will be found using Excel Formulas are shown in the text at the end of the chapter for those who are interested
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-13 Simple Linear Regression Example  A real estate agent wishes to examine the relationship between the selling price of a home and its size (measured in square feet)  A random sample of 10 houses is selected  Dependent variable (Y) = house price in $1000s  Independent variable (X) = square feet
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-14 Sample Data for House Price Model House Price in $1000s (Y) Square Feet (X) 245 1400 312 1600 279 1700 308 1875 199 1100 219 1550 405 2350 324 2450 319 1425 255 1700
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-15 0 50 100 150 200 250 300 350 400 450 800 1300 1800 2300 2800 Square Feet House Price ($1000s) Graphical Presentation  House price model: scatter plot
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-16 Regression Using Excel  Tools / Data Analysis / Regression
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-17 Excel Output Regression Statistics Multiple R 0.76211 R Square 0.58082 Adjusted R Square 0.52842 Standard Error 41.33032 Observations 10 ANOVA df SS MS F Significance F Regression 1 18934.9348 18934.9348 11.0848 0.01039 Residual 8 13665.5652 1708.1957 Total 9 32600.5000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386 Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 The regression equation is: feet) (square 0.10977 98.24833 price house  
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-18 0 50 100 150 200 250 300 350 400 450 0 500 1000 1500 2000 2500 3000 Square Feet House Price ($1000s) Graphical Presentation  House price model: scatter plot and regression line feet) (square 0.10977 98.24833 price house   Slope = 0.10977 Intercept = 98.248
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-19 Interpretation of the Intercept, b0  b0 is the estimated average value of Y when the value of X is zero (if X = 0 is in the range of observed X values)  Here, no houses had 0 square feet, so b0 = 98.24833 just indicates that, for houses within the range of sizes observed, $98,248.33 is the portion of the house price not explained by square feet feet) (square 0.10977 98.24833 price house  
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-20  b1 measures the estimated change in the average value of Y as a result of a one-unit change in X  Here, b1 = .10977 tells us that the average value of a house increases by .10977($1000) = $109.77, on average, for each additional one square foot of size Interpretation of the Slope Coefficient, b1 feet) (square 0.10977 98.24833 price house  
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-21 317.85 0) 0.1098(200 98.25 (sq.ft.) 0.1098 98.25 price house      Predict the price for a house with 2000 square feet: The predicted price for a house with 2000 square feet is 317.85($1,000s) = $317,850 Predictions using Regression Analysis
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-22 0 50 100 150 200 250 300 350 400 450 0 500 1000 1500 2000 2500 3000 Square Feet House Price ($1000s) Interpolation vs. Extrapolation  When using a regression model for prediction, only predict within the relevant range of data Relevant range for interpolation Do not try to extrapolate beyond the range of observed X’s
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-23  The coefficient of determination is the portion of the total variation in the dependent variable that is explained by variation in the independent variable  The coefficient of determination is also called r-squared and is denoted as r2 Coefficient of Determination, r2 1 r 0 2   note: squares of sum total squares of sum regression SST SSR r2  
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-24 Excel Output Regression Statistics Multiple R 0.76211 R Square 0.58082 Adjusted R Square 0.52842 Standard Error 41.33032 Observations 10 ANOVA df SS MS F Significance F Regression 1 18934.9348 18934.9348 11.0848 0.01039 Residual 8 13665.5652 1708.1957 Total 9 32600.5000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386 Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 58.08% of the variation in house prices is explained by variation in square feet 0.58082 32600.5000 18934.9348 SST SSR r2   
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-25 Standard Error of Estimate  The standard deviation of the variation of observations around the regression line is estimated by 2 n ) Ŷ Y ( 2 n SSE S n 1 i 2 i i YX        Where SSE = error sum of squares n = sample size
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-26 Excel Output Regression Statistics Multiple R 0.76211 R Square 0.58082 Adjusted R Square 0.52842 Standard Error 41.33032 Observations 10 ANOVA df SS MS F Significance F Regression 1 18934.9348 18934.9348 11.0848 0.01039 Residual 8 13665.5652 1708.1957 Total 9 32600.5000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386 Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 41.33032 SYX 
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-27 Comparing Standard Errors Y Y X X YX s small YX s large SYX is a measure of the variation of observed Y values from the regression line The magnitude of SYX should always be judged relative to the size of the Y values in the sample data i.e., SYX = $41.33K is moderately small relative to house prices in the $200 - $300K range
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-28 Assumptions of Regression  Normality of Error  Error values (ε) are normally distributed for any given value of X  Homoscedasticity  The probability distribution of the errors has constant variance  Independence of Errors  Error values are statistically independent
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-29 Residual Analysis for Linearity Not Linear Linear  x residuals x Y x Y x residuals
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-30 Residual Analysis for Homoscedasticity Non-constant variance  Constant variance x x Y x x Y residuals residuals
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-31 Residual Analysis for Independence Not Independent Independent X X residuals residuals X residuals 
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-32 House Price Model Residual Plot -60 -40 -20 0 20 40 60 80 0 1000 2000 3000 Square Feet Residuals Excel Residual Output RESIDUAL OUTPUT Predicted House Price Residuals 1 251.92316 -6.923162 2 273.87671 38.12329 3 284.85348 -5.853484 4 304.06284 3.937162 5 218.99284 -19.99284 6 268.38832 -49.38832 7 356.20251 48.79749 8 367.17929 -43.17929 9 254.6674 64.33264 10 284.85348 -29.85348 Does not appear to violate any regression assumptions
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-33  Used when data is collected over time to detect if autocorrelation is present  Autocorrelation exists if residuals in one time period are related to residuals in another period Measuring Autocorrelation: The Durbin-Watson Statistic
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-34 Autocorrelation  Autocorrelation is correlation of the errors (residuals) over time  Violates the regression assumption that residuals are random and independent Time (t) Residual Plot -15 -10 -5 0 5 10 15 0 2 4 6 8 Time (t) Residuals  Here, residuals show a cyclic pattern, not random
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-35 The Durbin-Watson Statistic        n 1 i 2 i n 2 i 2 1 i i e ) e e ( D  The possible range is 0 ≤ D ≤ 4  D should be close to 2 if H0 is true  D less than 2 may signal positive autocorrelation, D greater than 2 may signal negative autocorrelation  The Durbin-Watson statistic is used to test for autocorrelation H0: residuals are not correlated H1: autocorrelation is present
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-36 Testing for Positive Autocorrelation  Calculate the Durbin-Watson test statistic = D (The Durbin-Watson Statistic can be found using PHStat) Decision rule: reject H0 if D < dL H0: positive autocorrelation does not exist H1: positive autocorrelation is present 0 dU 2 dL Reject H0 Do not reject H0  Find the value dL from the Durbin-Watson table (for sample size n and number of independent variables k) Inconclusive
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-37  Example with n = 25: Durbin-Watson Calculations Sum of Squared Difference of Residuals 3296.18 Sum of Squared Residuals 3279.98 Durbin-Watson Statistic 1.00494 y = 30.65 + 4.7038x R 2 = 0.8976 0 20 40 60 80 1 00 1 20 1 40 1 60 0 5 1 0 1 5 20 25 30 Time Sales Testing for Positive Autocorrelation (continued) Excel/PHStat output: 1.00494 3279.98 3296.18 e ) e (e D n 1 i 2 i n 2 i 2 1 i i         
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-38  Here, n = 25 and there is k = 1 one independent variable  Using the Durbin-Watson table, dL = 1.29  D = 1.00494 < dL = 1.29, so reject H0 and conclude that significant positive autocorrelation exists  Therefore the linear model is not the appropriate model to forecast sales Testing for Positive Autocorrelation (continued) Decision: reject H0 since D = 1.00494 < dL 0 dU=1.45 2 dL=1.29 Reject H0 Do not reject H0 Inconclusive
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-39 Inference about the Slope: t Test  t test for a population slope  Is there a linear relationship between X and Y?  Null and alternative hypotheses H0: β1 = 0 (no linear relationship) H1: β1  0 (linear relationship does exist)  Test statistic 1 b 1 1 S β b t   2 n d.f.   where: b1 = regression slope coefficient β1 = hypothesized slope Sb1 = standard error of the slope
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-40 House Price in $1000s (y) Square Feet (x) 245 1400 312 1600 279 1700 308 1875 199 1100 219 1550 405 2350 324 2450 319 1425 255 1700 (sq.ft.) 0.1098 98.2483 price house   Estimated Regression Equation: The slope of this model is 0.1098 Does square footage of the house affect its sales price? Inference about the Slope: t Test (continued)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-41 Inferences about the Slope: t Test Example H0: β1 = 0 H1: β1  0 From Excel output: Coefficients Standard Error t Stat P-value Intercept 98.24833 58.03348 1.69296 0.12892 Square Feet 0.10977 0.03297 3.32938 0.01039 b1 tails 2 8 2 n df 32938 . 3 03297 . 0 0 10977 . 0 S β b t 1 b 1 1         010393985 . ) 2 , 8 , 32938 . 3 TDIST( 
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-42 Inferences about the Slope: t Test Example H0: β1 = 0 H1: β1  0 Test Statistic: t = 3.329 There is sufficient evidence that square footage affects house price From Excel output: Reject H0 at =.05 Coefficients Standard Error t Stat P-value Intercept 98.24833 58.03348 1.69296 0.12892 Square Feet 0.10977 0.03297 3.32938 0.01039 p b1 Decision: Conclusion: Reject H0 Reject H0 /2=.025 Do not reject H0 0 /2=.025 3.329 d.f. = 10-2 = 8 (continued)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-43 Confidence Interval Estimate for the Slope Confidence Interval Estimate of the Slope: Excel Printout for House Prices: At 95% level of confidence, the confidence interval for the slope is (0.0337, 0.1858) 1 b 2 n 1 S t b   Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386 Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 d.f. = n - 2
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-44 Since the units of the house price variable is $1000s, we are 95% confident that the average impact on sales price is between $33.70 and $185.80 per square foot of house size Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386 Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 This 95% confidence interval does not include 0. Conclusion: There is a significant relationship between house price and square feet at the .05 level of significance Confidence Interval Estimate for the Slope (continued)
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-45 Confidence Interval for the Average Y, Given X Confidence interval estimate for the mean value of Y given a particular Xi Size of interval varies according to distance away from mean, X i YX 2 n X X | Y h S t Ŷ : μ for interval Confidence i            2 i 2 i 2 i i ) X (X ) X (X n 1 SSX ) X (X n 1 h
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-46 Prediction Interval for an Individual Y, Given X Confidence interval estimate for an Individual value of Y given a particular Xi This extra term adds to the interval width to reflect the added uncertainty for an individual case i YX 2 n X X h 1 S t Ŷ : Y for interval Confidence i    
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-47 Estimation of Mean Values: Example Find the 95% confidence interval for the mean price of 2,000 square-foot houses Predicted Price Yi = 317.85 ($1,000s)  Confidence Interval Estimate for μY|X=Xi 37.12 317.85 ) X (X ) X (X n 1 S t Ŷ 2 i 2 i YX 2 - n        The confidence interval endpoints are 280.66 -- 354.90, or from $280,660 -- $354,900
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-48 Estimation of Individual Values: Example Find the 95% confidence interval for an individual house with 2,000 square feet Predicted Price Yi = 317.85 ($1,000s)  Prediction Interval Estimate for YX=Xi 102.28 317.85 ) X (X ) X (X n 1 1 S t Ŷ 2 i 2 i YX 1 - n         The prediction interval endpoints are 215.50 -- 420.07, or from $215,500 -- $420,070
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-49 Finding Confidence and Prediction Intervals in Excel  In Excel, use PHStat | regression | simple linear regression …  Check the “confidence and prediction interval for X=” box and enter the X-value and confidence level desired
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-50 Input values Finding Confidence and Prediction Intervals in Excel (continued) Confidence Interval Estimate for μY|X=Xi Prediction Interval Estimate for YX=Xi Y 
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-51 Pitfalls of Regression Analysis  Lacking an awareness of the assumptions underlying least-squares regression  Not knowing how to evaluate the assumptions  Not knowing the alternatives to least-squares regression if a particular assumption is violated  Using a regression model without knowledge of the subject matter  Extrapolating outside the relevant range
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-52 Chapter Summary  Introduced types of regression models  Reviewed assumptions of regression and correlation  Discussed determining the simple linear regression equation  Described measures of variation  Discussed residual analysis  Addressed measuring autocorrelation
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-53 Chapter Summary  Described inference about the slope  Addressed estimation of mean values and prediction of individual values  Discussed possible pitfalls in regression and recommended strategies to avoid them (continued)

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chap12.pptx

  • 1.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-1 Chapter 12 Simple Linear Regression Statistics for Managers Using Microsoft® Excel 4th Edition
  • 2.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-2 Chapter Goals After completing this chapter, you should be able to:  Explain the simple linear regression model  Obtain and interpret the simple linear regression equation for a set of data  Evaluate regression residuals for aptness of the fitted model  Understand the assumptions behind regression analysis  Explain measures of variation and determine whether the independent variable is significant
  • 3.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-3 Chapter Goals After completing this chapter, you should be able to:  Calculate and interpret confidence intervals for the regression coefficients  Use the Durbin-Watson statistic to check for autocorrelation  Form confidence and prediction intervals around an estimated Y value for a given X  Recognize some potential problems if regression analysis is used incorrectly (continued)
  • 4.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-4 Correlation vs. Regression  A scatter plot (or scatter diagram) can be used to show the relationship between two variables  Correlation analysis is used to measure strength of the association (linear relationship) between two variables  Correlation is only concerned with strength of the relationship  No causal effect is implied with correlation  Correlation was first presented in Chapter 3
  • 5.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-5 Regression Analysis  Regression analysis is used to:  Predict the value of a dependent variable based on the value of at least one independent variable  Explain the impact of changes in an independent variable on the dependent variable Dependent variable: the variable we wish to explain Independent variable: the variable used to explain the dependent variable
  • 6.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-6 Simple Linear Regression Model  Only one independent variable, X  Relationship between X and Y is described by a linear function  Changes in Y are assumed to be caused by changes in X
  • 7.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-7 Types of Relationships Y X Y X Y Y X X Linear relationships Curvilinear relationships
  • 8.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-8 Types of Relationships Y X Y X Y Y X X Strong relationships Weak relationships (continued)
  • 9.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-9 Types of Relationships Y X Y X No relationship (continued)
  • 10.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-10 i i 1 0 i ε X β β Y    Linear component Population Linear Regression Equation The population regression model: Population Y intercept Population Slope Coefficient Random Error term, or residual Dependent Variable Independent Variable Random Error component
  • 11.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-11 i 1 0 i X b b Ŷ   The simple linear regression equation provides an estimate of the population regression line Sample Linear Regression Equation Estimate of the regression intercept Estimate of the regression slope Estimated (or predicted) Y value for observation i Value of X for observation i The individual random error terms ei have a mean of zero
  • 12.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-12 Finding the Least Squares Equation  The coefficients b0 and b1 , and other regression results in this chapter, will be found using Excel Formulas are shown in the text at the end of the chapter for those who are interested
  • 13.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-13 Simple Linear Regression Example  A real estate agent wishes to examine the relationship between the selling price of a home and its size (measured in square feet)  A random sample of 10 houses is selected  Dependent variable (Y) = house price in $1000s  Independent variable (X) = square feet
  • 14.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-14 Sample Data for House Price Model House Price in $1000s (Y) Square Feet (X) 245 1400 312 1600 279 1700 308 1875 199 1100 219 1550 405 2350 324 2450 319 1425 255 1700
  • 15.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-15 0 50 100 150 200 250 300 350 400 450 800 1300 1800 2300 2800 Square Feet House Price ($1000s) Graphical Presentation  House price model: scatter plot
  • 16.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-16 Regression Using Excel  Tools / Data Analysis / Regression
  • 17.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-17 Excel Output Regression Statistics Multiple R 0.76211 R Square 0.58082 Adjusted R Square 0.52842 Standard Error 41.33032 Observations 10 ANOVA df SS MS F Significance F Regression 1 18934.9348 18934.9348 11.0848 0.01039 Residual 8 13665.5652 1708.1957 Total 9 32600.5000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386 Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 The regression equation is: feet) (square 0.10977 98.24833 price house  
  • 18.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-18 0 50 100 150 200 250 300 350 400 450 0 500 1000 1500 2000 2500 3000 Square Feet House Price ($1000s) Graphical Presentation  House price model: scatter plot and regression line feet) (square 0.10977 98.24833 price house   Slope = 0.10977 Intercept = 98.248
  • 19.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-19 Interpretation of the Intercept, b0  b0 is the estimated average value of Y when the value of X is zero (if X = 0 is in the range of observed X values)  Here, no houses had 0 square feet, so b0 = 98.24833 just indicates that, for houses within the range of sizes observed, $98,248.33 is the portion of the house price not explained by square feet feet) (square 0.10977 98.24833 price house  
  • 20.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-20  b1 measures the estimated change in the average value of Y as a result of a one-unit change in X  Here, b1 = .10977 tells us that the average value of a house increases by .10977($1000) = $109.77, on average, for each additional one square foot of size Interpretation of the Slope Coefficient, b1 feet) (square 0.10977 98.24833 price house  
  • 21.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-21 317.85 0) 0.1098(200 98.25 (sq.ft.) 0.1098 98.25 price house      Predict the price for a house with 2000 square feet: The predicted price for a house with 2000 square feet is 317.85($1,000s) = $317,850 Predictions using Regression Analysis
  • 22.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-22 0 50 100 150 200 250 300 350 400 450 0 500 1000 1500 2000 2500 3000 Square Feet House Price ($1000s) Interpolation vs. Extrapolation  When using a regression model for prediction, only predict within the relevant range of data Relevant range for interpolation Do not try to extrapolate beyond the range of observed X’s
  • 23.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-23  The coefficient of determination is the portion of the total variation in the dependent variable that is explained by variation in the independent variable  The coefficient of determination is also called r-squared and is denoted as r2 Coefficient of Determination, r2 1 r 0 2   note: squares of sum total squares of sum regression SST SSR r2  
  • 24.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-24 Excel Output Regression Statistics Multiple R 0.76211 R Square 0.58082 Adjusted R Square 0.52842 Standard Error 41.33032 Observations 10 ANOVA df SS MS F Significance F Regression 1 18934.9348 18934.9348 11.0848 0.01039 Residual 8 13665.5652 1708.1957 Total 9 32600.5000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386 Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 58.08% of the variation in house prices is explained by variation in square feet 0.58082 32600.5000 18934.9348 SST SSR r2   
  • 25.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-25 Standard Error of Estimate  The standard deviation of the variation of observations around the regression line is estimated by 2 n ) Ŷ Y ( 2 n SSE S n 1 i 2 i i YX        Where SSE = error sum of squares n = sample size
  • 26.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-26 Excel Output Regression Statistics Multiple R 0.76211 R Square 0.58082 Adjusted R Square 0.52842 Standard Error 41.33032 Observations 10 ANOVA df SS MS F Significance F Regression 1 18934.9348 18934.9348 11.0848 0.01039 Residual 8 13665.5652 1708.1957 Total 9 32600.5000 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386 Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 41.33032 SYX 
  • 27.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-27 Comparing Standard Errors Y Y X X YX s small YX s large SYX is a measure of the variation of observed Y values from the regression line The magnitude of SYX should always be judged relative to the size of the Y values in the sample data i.e., SYX = $41.33K is moderately small relative to house prices in the $200 - $300K range
  • 28.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-28 Assumptions of Regression  Normality of Error  Error values (ε) are normally distributed for any given value of X  Homoscedasticity  The probability distribution of the errors has constant variance  Independence of Errors  Error values are statistically independent
  • 29.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-29 Residual Analysis for Linearity Not Linear Linear  x residuals x Y x Y x residuals
  • 30.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-30 Residual Analysis for Homoscedasticity Non-constant variance  Constant variance x x Y x x Y residuals residuals
  • 31.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-31 Residual Analysis for Independence Not Independent Independent X X residuals residuals X residuals 
  • 32.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-32 House Price Model Residual Plot -60 -40 -20 0 20 40 60 80 0 1000 2000 3000 Square Feet Residuals Excel Residual Output RESIDUAL OUTPUT Predicted House Price Residuals 1 251.92316 -6.923162 2 273.87671 38.12329 3 284.85348 -5.853484 4 304.06284 3.937162 5 218.99284 -19.99284 6 268.38832 -49.38832 7 356.20251 48.79749 8 367.17929 -43.17929 9 254.6674 64.33264 10 284.85348 -29.85348 Does not appear to violate any regression assumptions
  • 33.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-33  Used when data is collected over time to detect if autocorrelation is present  Autocorrelation exists if residuals in one time period are related to residuals in another period Measuring Autocorrelation: The Durbin-Watson Statistic
  • 34.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-34 Autocorrelation  Autocorrelation is correlation of the errors (residuals) over time  Violates the regression assumption that residuals are random and independent Time (t) Residual Plot -15 -10 -5 0 5 10 15 0 2 4 6 8 Time (t) Residuals  Here, residuals show a cyclic pattern, not random
  • 35.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-35 The Durbin-Watson Statistic        n 1 i 2 i n 2 i 2 1 i i e ) e e ( D  The possible range is 0 ≤ D ≤ 4  D should be close to 2 if H0 is true  D less than 2 may signal positive autocorrelation, D greater than 2 may signal negative autocorrelation  The Durbin-Watson statistic is used to test for autocorrelation H0: residuals are not correlated H1: autocorrelation is present
  • 36.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-36 Testing for Positive Autocorrelation  Calculate the Durbin-Watson test statistic = D (The Durbin-Watson Statistic can be found using PHStat) Decision rule: reject H0 if D < dL H0: positive autocorrelation does not exist H1: positive autocorrelation is present 0 dU 2 dL Reject H0 Do not reject H0  Find the value dL from the Durbin-Watson table (for sample size n and number of independent variables k) Inconclusive
  • 37.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-37  Example with n = 25: Durbin-Watson Calculations Sum of Squared Difference of Residuals 3296.18 Sum of Squared Residuals 3279.98 Durbin-Watson Statistic 1.00494 y = 30.65 + 4.7038x R 2 = 0.8976 0 20 40 60 80 1 00 1 20 1 40 1 60 0 5 1 0 1 5 20 25 30 Time Sales Testing for Positive Autocorrelation (continued) Excel/PHStat output: 1.00494 3279.98 3296.18 e ) e (e D n 1 i 2 i n 2 i 2 1 i i         
  • 38.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-38  Here, n = 25 and there is k = 1 one independent variable  Using the Durbin-Watson table, dL = 1.29  D = 1.00494 < dL = 1.29, so reject H0 and conclude that significant positive autocorrelation exists  Therefore the linear model is not the appropriate model to forecast sales Testing for Positive Autocorrelation (continued) Decision: reject H0 since D = 1.00494 < dL 0 dU=1.45 2 dL=1.29 Reject H0 Do not reject H0 Inconclusive
  • 39.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-39 Inference about the Slope: t Test  t test for a population slope  Is there a linear relationship between X and Y?  Null and alternative hypotheses H0: β1 = 0 (no linear relationship) H1: β1  0 (linear relationship does exist)  Test statistic 1 b 1 1 S β b t   2 n d.f.   where: b1 = regression slope coefficient β1 = hypothesized slope Sb1 = standard error of the slope
  • 40.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-40 House Price in $1000s (y) Square Feet (x) 245 1400 312 1600 279 1700 308 1875 199 1100 219 1550 405 2350 324 2450 319 1425 255 1700 (sq.ft.) 0.1098 98.2483 price house   Estimated Regression Equation: The slope of this model is 0.1098 Does square footage of the house affect its sales price? Inference about the Slope: t Test (continued)
  • 41.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-41 Inferences about the Slope: t Test Example H0: β1 = 0 H1: β1  0 From Excel output: Coefficients Standard Error t Stat P-value Intercept 98.24833 58.03348 1.69296 0.12892 Square Feet 0.10977 0.03297 3.32938 0.01039 b1 tails 2 8 2 n df 32938 . 3 03297 . 0 0 10977 . 0 S β b t 1 b 1 1         010393985 . ) 2 , 8 , 32938 . 3 TDIST( 
  • 42.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-42 Inferences about the Slope: t Test Example H0: β1 = 0 H1: β1  0 Test Statistic: t = 3.329 There is sufficient evidence that square footage affects house price From Excel output: Reject H0 at =.05 Coefficients Standard Error t Stat P-value Intercept 98.24833 58.03348 1.69296 0.12892 Square Feet 0.10977 0.03297 3.32938 0.01039 p b1 Decision: Conclusion: Reject H0 Reject H0 /2=.025 Do not reject H0 0 /2=.025 3.329 d.f. = 10-2 = 8 (continued)
  • 43.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-43 Confidence Interval Estimate for the Slope Confidence Interval Estimate of the Slope: Excel Printout for House Prices: At 95% level of confidence, the confidence interval for the slope is (0.0337, 0.1858) 1 b 2 n 1 S t b   Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386 Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 d.f. = n - 2
  • 44.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-44 Since the units of the house price variable is $1000s, we are 95% confident that the average impact on sales price is between $33.70 and $185.80 per square foot of house size Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386 Square Feet 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 This 95% confidence interval does not include 0. Conclusion: There is a significant relationship between house price and square feet at the .05 level of significance Confidence Interval Estimate for the Slope (continued)
  • 45.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-45 Confidence Interval for the Average Y, Given X Confidence interval estimate for the mean value of Y given a particular Xi Size of interval varies according to distance away from mean, X i YX 2 n X X | Y h S t Ŷ : μ for interval Confidence i            2 i 2 i 2 i i ) X (X ) X (X n 1 SSX ) X (X n 1 h
  • 46.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-46 Prediction Interval for an Individual Y, Given X Confidence interval estimate for an Individual value of Y given a particular Xi This extra term adds to the interval width to reflect the added uncertainty for an individual case i YX 2 n X X h 1 S t Ŷ : Y for interval Confidence i    
  • 47.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-47 Estimation of Mean Values: Example Find the 95% confidence interval for the mean price of 2,000 square-foot houses Predicted Price Yi = 317.85 ($1,000s)  Confidence Interval Estimate for μY|X=Xi 37.12 317.85 ) X (X ) X (X n 1 S t Ŷ 2 i 2 i YX 2 - n        The confidence interval endpoints are 280.66 -- 354.90, or from $280,660 -- $354,900
  • 48.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-48 Estimation of Individual Values: Example Find the 95% confidence interval for an individual house with 2,000 square feet Predicted Price Yi = 317.85 ($1,000s)  Prediction Interval Estimate for YX=Xi 102.28 317.85 ) X (X ) X (X n 1 1 S t Ŷ 2 i 2 i YX 1 - n         The prediction interval endpoints are 215.50 -- 420.07, or from $215,500 -- $420,070
  • 49.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-49 Finding Confidence and Prediction Intervals in Excel  In Excel, use PHStat | regression | simple linear regression …  Check the “confidence and prediction interval for X=” box and enter the X-value and confidence level desired
  • 50.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-50 Input values Finding Confidence and Prediction Intervals in Excel (continued) Confidence Interval Estimate for μY|X=Xi Prediction Interval Estimate for YX=Xi Y 
  • 51.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-51 Pitfalls of Regression Analysis  Lacking an awareness of the assumptions underlying least-squares regression  Not knowing how to evaluate the assumptions  Not knowing the alternatives to least-squares regression if a particular assumption is violated  Using a regression model without knowledge of the subject matter  Extrapolating outside the relevant range
  • 52.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-52 Chapter Summary  Introduced types of regression models  Reviewed assumptions of regression and correlation  Discussed determining the simple linear regression equation  Described measures of variation  Discussed residual analysis  Addressed measuring autocorrelation
  • 53.
    Statistics for ManagersUsing Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 12-53 Chapter Summary  Described inference about the slope  Addressed estimation of mean values and prediction of individual values  Discussed possible pitfalls in regression and recommended strategies to avoid them (continued)