1 / 47

Simple Linear Regression Analysis

Learn the fundamentals of simple linear regression, including the regression model, estimation process, coefficient of determination, and significance testing with examples and interpretation. Improve your statistical analysis skills.

pfeiferm
Download Presentation

Simple Linear Regression Analysis

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 12 Simple Linear Regression • Simple Linear Regression Model • Least Squares Method • Coefficient of Determination • Model Assumptions • Testing for Significance • Using the Estimated Regression Equation for Estimation and Prediction • Computer Solution • Residual Analysis: Validating Model Assumptions

  2. Simple Linear Regression Model • The equation that describes how y is related to x and an error term is called the regression model. • The simple linear regression model is: y = b0 + b1x +e • b0 and b1 are called parameters of the model. • e is a random variable called the error term.

  3. Simple Linear Regression Equation • The simple linear regression equation is: E(y) = 0 + 1x • Graph of the regression equation is a straight line. • b0 is the y intercept of the regression line. • b1 is the slope of the regression line. • E(y) is the expected value of y for a given x value.

  4. Simple Linear Regression Equation • Positive Linear Relationship E(y) Regression line Intercept b0 Slope b1 is positive x

  5. Simple Linear Regression Equation • Negative Linear Relationship E(y) Regression line Intercept b0 Slope b1 is negative x

  6. Simple Linear Regression Equation • No Relationship E(y) Regression line Intercept b0 Slope b1 is 0 x

  7. Estimated Simple Linear Regression Equation • The estimated simple linear regression equation is: • The graph is called the estimated regression line. • b0 is the y intercept of the line. • b1 is the slope of the line. • is the estimated value of y for a given x value.

  8. Estimation Process Sample Data: x y x1 y1 . . . . xnyn Regression Model y = b0 + b1x +e Regression Equation E(y) = b0 + b1x Unknown Parameters b0, b1 b0 and b1 provide estimates of b0 and b1 Estimated Regression Equation Sample Statistics b0, b1

  9. Least Squares Method • Least Squares Criterion where: yi = observed value of the dependent variable for the ith observation yi = estimated value of the dependent variable for the ith observation ^

  10. The Least Squares Method • Slope for the Estimated Regression Equation

  11. The Least Squares Method • y-Intercept for the Estimated Regression Equation where: xi = value of independent variable for ith observation yi = value of dependent variable for ith observation x = mean value for independent variable y = mean value for dependent variable n = total number of observations _ _

  12. Example: Reed Auto Sales • Simple Linear Regression Reed Auto periodically has a special week-long sale. As part of the advertising campaign Reed runs one or more television commercials during the weekend preceding the sale. Data from a sample of 5 previous sales are shown on the next slide.

  13. Example: Reed Auto Sales • Simple Linear Regression Number of TV AdsNumber of Cars Sold 1 14 3 24 2 18 1 17 3 27

  14. Example: Reed Auto Sales • Slope for the Estimated Regression Equation b1 = 220 - (10)(100)/5 = 5 24 - (10)2/5 • y-Intercept for the Estimated Regression Equation b0 = 20 - 5(2) = 10 • Estimated Regression Equation y = 10 + 5x ^

  15. Example: Reed Auto Sales • Scatter Diagram ^

  16. The Coefficient of Determination ^ ^ • Relationship Among SST, SSR, SSE SST = SSR + SSE where: SST = total sum of squares SSR = sum of squares due to regression SSE = sum of squares due to error

  17. The Coefficient of Determination • The coefficient of determination is: r2 = SSR/SST where: SST = total sum of squares SSR = sum of squares due to regression

  18. Example: Reed Auto Sales • Coefficient of Determination r2 = SSR/SST = 100/114 = .8772 The regression relationship is very strong because 88% of the variation in number of cars sold can be explained by the linear relationship between the number of TV ads and the number of cars sold.

  19. The Correlation Coefficient • Sample Correlation Coefficient where: b1 = the slope of the estimated regression equation

  20. Example: Reed Auto Sales • Sample Correlation Coefficient The sign of b1 in the equation is “+”. rxy = +.9366

  21. Model Assumptions • Assumptions About the Error Term  • The error  is a random variable with mean of zero. • The variance of  , denoted by  2, is the same for all values of the independent variable. • The values of  are independent. • The error  is a normally distributed random variable.

  22. Testing for Significance • To test for a significant regression relationship, we must conduct a hypothesis test to determine whether the value of b1 is zero. • Two tests are commonly used • t Test • F Test • Both tests require an estimate of s2, the variance of e in the regression model.

  23. Testing for Significance • An Estimate of s2 The mean square error (MSE) provides the estimate of s2, and the notation s2 is also used. s2 = MSE = SSE/(n-2) where:

  24. Testing for Significance • An Estimate of s • To estimate s we take the square root of s 2. • The resulting s is called the standard error of the estimate.

  25. Testing for Significance: t Test • Hypotheses H0: 1 = 0 Ha: 1 = 0 • Test Statistic where

  26. Testing for Significance: t Test • Rejection Rule Reject H0 if t < -tor t > t where: tis based on a t distribution with n - 2 degrees of freedom

  27. Example: Reed Auto Sales • t Test • Hypotheses H0: 1 = 0 Ha: 1 = 0 • Rejection Rule For  = .05 and d.f. = 3, t.025 = 3.182 Reject H0 if t > 3.182

  28. Example: Reed Auto Sales • t Test • Test Statistics • t = 5/1.08 = 4.63 • Conclusions • t = 4.63> 3.182, so reject H0

  29. Confidence Interval for 1 • We can use a 95% confidence interval for 1 to test the hypotheses just used in the t test. • H0 is rejected if the hypothesized value of 1 is not included in the confidence interval for 1.

  30. Confidence Interval for 1 • The form of a confidence interval for 1 is: where b1 is the point estimate is the margin of error is the t value providing an area of a/2 in the upper tail of a t distribution with n - 2 degrees of freedom

  31. Example: Reed Auto Sales • Rejection Rule Reject H0 if 0 is not included in the confidence interval for 1. • 95% Confidence Interval for 1 = 5 +/- 3.182(1.08) = 5 +/- 3.44 or 1.56 to 8.44 • Conclusion 0 is not included in the confidence interval. Reject H0

  32. Testing for Significance: F Test • Hypotheses H0: 1 = 0 Ha: 1 = 0 • Test Statistic F = MSR/MSE

  33. Testing for Significance: F Test • Rejection Rule Reject H0 if F > F where: F is based on an F distribution with 1 d.f. in the numerator and n - 2 d.f. in the denominator

  34. Example: Reed Auto Sales • F Test • Hypotheses • H0: 1 = 0 Ha: 1 = 0 • Rejection Rule • For  = .05 and d.f. = 1, 3: F.05 = 10.13 • Reject H0 if F > 10.13.

  35. Example: Reed Auto Sales • F Test • Test Statistic • F = MSR/MSE = 100/4.667 = 21.43 • Conclusion • F = 21.43 > 10.13, so we reject H0.

  36. Some Cautions about theInterpretation of Significance Tests • Rejecting H0: b1 = 0 and concluding that the relationship between x and y is significant does not enable us to conclude that a cause-and-effect relationship is present between x and y. • Just because we are able to reject H0: b1 = 0 and demonstrate statistical significance does not enable us to conclude that there is a linear relationship between x and y.

  37. Using the Estimated Regression Equationfor Estimation and Prediction • Confidence Interval Estimate of E(yp) • Prediction Interval Estimate of yp yp+t/2 sind where: confidence coefficient is 1 -  and t/2 is based on a t distribution with n - 2 degrees of freedom

  38. Example: Reed Auto Sales • Point Estimation If 3 TV ads are run prior to a sale, we expect the mean number of cars sold to be: y = 10 + 5(3) = 25 cars ^

  39. Example: Reed Auto Sales • Confidence Interval for E(yp) 95% confidence interval estimate of the mean number of cars sold when 3 TV ads are run is: 25 + 4.61 = 20.39 to 29.61 cars

  40. Example: Reed Auto Sales • Prediction Interval for yp 95% prediction interval estimate of the number of cars sold in one particular week when 3 TV ads are run is: 25 + 8.28 = 16.72 to 33.28 cars

  41. Residual Analysis ^ • Residual for Observation i yi – yi • Standardized Residual for Observation i where: and ^ ^ ^

  42. Example: Reed Auto Sales • Residuals

  43. Example: Reed Auto Sales • Residual Plot

  44. Residual Analysis • Residual Plot Good Pattern Residual 0 x

  45. Residual Analysis • Residual Plot Nonconstant Variance Residual 0 x

  46. Residual Analysis • Residual Plot Model Form Not Adequate Residual 0 x

  47. End of Chapter 12

More Related