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• 1. Linearna Regresija ILI - KAKO OBJASNITI METODU NAJMANJEG KVADRANTA TOTALNOM DEBILU dr Dragoljub mojja verzija na" Indijanskom Egleskom"
• 2. Learning Objectives In this chapter, you learn:  How to use regression analysis to predict the value of a dependent variable based on an independent variable  The meaning of the regression coefficients b 0 and b1  How to evaluate the assumptions of regression analysis and know what to do if the assumptions are violated  To make inferences about the slope and correlation coefficient  To estimate mean values and predict individual values 13-2
• 3. Correlation vs. Regression   A scatter plot can be used to show the relationship between two variables DCOVA Correlation analysis is used to measure the 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  Scatter plots were first presented in Ch. 2  Correlation was first presented in Ch. 3 13-3
• 4. Introduction to Regression Analysis DCOVA  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 predict or explain Independent variable: the variable used to predict or explain the dependent variable 13-4
• 5. Simple Linear Regression Model DCOVA    Only one independent variable, X Relationship between X and Y is described by a linear function Changes in Y are assumed to be related to changes in X 13-5
• 6. Types of Relationships DCOVA Linear relationships Y Curvilinear relationships Y X Y X Y X X 13-6
• 7. Types of Relationships DCOVA (continued) Strong relationships Y Weak relationships Y X Y X Y X X 13-7
• 8. Types of Relationships DCOVA (continued) No relationship Y X Y X 13-8
• 9. Simple Linear Regression Model DCOVA Population Y intercept Dependent Variable Population Slope Coefficient Independent Variable Random Error term Yi  β0  β1Xi  ε i Linear component Random Error component 13-9
• 10. Simple Linear Regression DCOVA Model (continued) Y Yi  β0  β1Xi  ε i Observed Value of Y for Xi εi Predicted Value of Y for Xi Slope = β1 Random Error for this Xi value Intercept = β0 Xi X 13-10
• 11. Simple Linear Regression Equation (Prediction Line) DCOVA The simple linear regression equation provides an estimate of the population regression line Estimated (or predicted) Y value for observation i Estimate of the regression intercept Estimate of the regression slope ˆ b b X Yi 0 1 i Value of X for observation i 13-11
• 12. The Least Squares Method DCOVA b0 and b1 are obtained by finding the values of that minimize the sum of the squared ˆ differences between Y and Y : ˆ )2  min  (Y  (b  b X ))2 min  (Yi Yi i 0 1 i 13-12
• 13. Finding the Least Squares Equation DCOVA  The coefficients b0 and b1 , and other regression results in this chapter, will be found using Excel Formulas are shown in the text for those who are interested 13-13
• 14. Interpretation of the Slope and the Intercept DCOVA   b0 is the estimated average value of Y when the value of X is zero b1 is the estimated change in the average value of Y as a result of a one-unit increase in X 13-14
• 15. Simple Linear Regression Example DCOVA   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 13-15
• 16. Simple Linear Regression Example: Data 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 DCOVA 1700 13-16
• 17. Simple Linear Regression Example: Scatter Plot House price model: Scatter Plot DCOVA 13-17
• 18. Simple Linear Regression Example: Using Excel Data Analysis Function 1. Choose Data DCOVA 2. Choose Data Analysis 3. Choose Regression 13-18
• 19. Simple Linear Regression Example: Using Excel Data Analysis Function (continued) Enter Y’s and X’s and desired options DCOVA 13-19
• 20. Simple Linear Regression Example: Using PHStat Add-Ins: PHStat: Regression: Simple Linear Regression 13-20
• 21. Simple Linear Regression Example: Excel Output DCOVA Regression Statistics Multiple R 0.76211 R Square 0.58082 Adjusted R Square 0.52842 Standard Error The regression equation is: house price  98.24833  0.10977 (square feet) 41.33032 Observations 10 ANOVA df SS MS Regression 1 18934.9348 18934.9348 Residual 8 13665.5652 9 32600.5000 Coefficients Intercept Square Feet Standard Error t Stat 11.0848 Significance F 1708.1957 Total F P-value 0.01039 Lower 95% Upper 95% 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 13-21
• 22. Simple Linear Regression Example: Graphical Representation DCOVA House price model: Scatter Plot and Prediction Line Slope = 0.10977 Intercept = 98.248 house price  98.24833  0.10977 (square feet) 13-22
• 23. Simple Linear Regression Example: Interpretation of bo DCOVA house price  98.24833  0.10977 (square feet)   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) Because a house cannot have a square footage of 0, b0 has no practical application 13-23
• 24. Simple Linear Regression Example: Interpreting b1 DCOVA house price  98.24833  0.10977 (square feet)  b1 estimates the change in the average value of Y as a result of a one-unit increase in X  Here, b1 = 0.10977 tells us that the mean value of a house increases by .10977(\$1000) = \$109.77, on average, for each additional one square foot of size 13-24
• 25. Simple Linear Regression Example: Making Predictions Predict the price for a house with 2000 square feet: DCOVA house price  98.25  0.1098 (sq.ft.)  98.25  0.1098(200 0)  317.85 The predicted price for a house with 2000 square feet is 317.85(\$1,000s) = \$317,850 13-25
• 26. Simple Linear Regression Example: Making Predictions DCOVA  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 13-26
• 27. Measures of Variation DCOVA  Total variation is made up of two parts: SST  SSR  Total Sum of Squares Regression Sum of Squares SST   ( Yi  Y )2 ˆ SSR   ( Yi  Y )2 SSE Error Sum of Squares ˆ SSE   ( Yi  Yi )2 where: Y = Mean value of the dependent variable Yi = Observed value of the dependent variable ˆ Yi = Predicted value of Y for the given X i value 13-27
• 28. Measures of Variation (continued) DCOVA  SST = total sum of squares   Measures the variation of the Y i values around their mean Y SSR = regression sum of squares (Explained Variation)   (Total Variation) Variation attributable to the relationship between X and Y SSE = error sum of squares (Unexplained Variation)  Variation in Y attributable to factors other than X 13-28
• 29. Measures of Variation (continued) DCOVA Y Yi  SSE = (Yi - Yi )2  Y _  Y SST = (Yi - Y)2  _ SSR = (Yi - Y)2 _ Y Xi _ Y X 13-29
• 30. Coefficient of Determination, r2 DCOVA   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 SSR regression sum of squares r   SST total sum of squares 2 note: 0 r 1 2 13-30
• 31. Examples of Approximate r2 Values DCOVA Y r2 = 1 r2 = 1 X 100% of the variation in Y is explained by variation in X Y r2 =1 Perfect linear relationship between X and Y: X 13-31
• 32. Examples of Approximate r2 Values DCOVA Y 0 < r2 < 1 X Weaker linear relationships between X and Y: Some but not all of the variation in Y is explained by variation in X Y X 13-32
• 33. Examples of Approximate r2 Values DCOVA r2 = 0 Y No linear relationship between X and Y: r2 = 0 X The value of Y does not depend on X. (None of the variation in Y is explained by variation in X) 13-33
• 34. Simple Linear Regression Example: Coefficient of Determination, r2 in Excel DCOVA SSR 18934.9348 r    0.58082 SST 32600.5000 2 Regression Statistics Multiple R 0.76211 R Square 0.58082 Adjusted R Square 0.52842 Standard Error 58.08% of the variation in house prices is explained by variation in square feet 41.33032 Observations 10 ANOVA df SS MS Regression 1 18934.9348 18934.9348 Residual 8 13665.5652 9 32600.5000 Coefficients Intercept Square Feet Standard Error t Stat 11.0848 Significance F 1708.1957 Total F P-value 0.01039 Lower 95% Upper 95% 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 13-34
• 35. Standard Error of Estimate DCOVA  The standard deviation of the variation of observations around the regression line is estimated by n SSE S YX   n2  ˆ (Yi  Yi ) 2 i 1 n2 Where SSE = error sum of squares n = sample size 13-35
• 36. Simple Linear Regression Example: Standard Error of Estimate in Excel DCOVA Regression Statistics Multiple R 0.76211 R Square 0.58082 Adjusted R Square S YX  41.33032 0.52842 Standard Error 41.33032 Observations 10 ANOVA df SS MS Regression 1 18934.9348 18934.9348 Residual 8 13665.5652 9 32600.5000 Coefficients Intercept Square Feet Standard Error t Stat 11.0848 Significance F 1708.1957 Total F P-value 0.01039 Lower 95% Upper 95% 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 13-36
• 37. Comparing Standard Errors SYX is a measure of the variation of observed Y values from the regression line Y DCOVA Y small SYX X large SYX X 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 \$200K - \$400K range 13-37
• 38. Assumptions of Regression L.I.N.E DCOVA     Linearity  The relationship between X and Y is linear Independence of Errors  Error values are statistically independent Normality of Error  Error values are normally distributed for any given value of X Equal Variance (also called homoscedasticity)  The probability distribution of the errors has constant variance 13-38
• 39. Residual Analysis ˆ ei  Yi  Yi   The residual for observation i, e i, is the difference between its observed and predicted value Check the assumptions of regression by examining the residuals  Examine for linearity assumption  Evaluate independence assumption  Evaluate normal distribution assumption   DCOVA Examine for constant variance for all levels of X (homoscedasticity) Graphical Analysis of Residuals  Can plot residuals vs. X 13-39
• 40. Residual Analysis for Linearity DCOVA Y Y x x Not Linear residuals residuals x x  Linear 13-40
• 41. Residual Analysis for Independence DCOVA Not Independent X residuals residuals X residuals  Independent X 13-41
• 42. Checking for Normality DCOVA     Examine the Stem-and-Leaf Display of the Residuals Examine the Boxplot of the Residuals Examine the Histogram of the Residuals Construct a Normal Probability Plot of the Residuals 13-42
• 43. Residual Analysis for Normality DCOVA When using a normal probability plot, normal errors will approximately display in a straight line Percent 100 0 -3 -2 -1 0 1 2 3 Residual 13-43
• 44. Residual Analysis for Equal Variance DCOVA Y Y x x Non-constant variance residuals residuals x x  Constant variance 13-44
• 45. Simple Linear Regression Example: Excel Residual Output DCOVA 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 13-45
• 46. Measuring Autocorrelation: The Durbin-Watson Statistic DCOVA   Used when data are collected over time to detect if autocorrelation is present Autocorrelation exists if residuals in one time period are related to residuals in another period 13-46
• 47. Autocorrelation DCOVA  Autocorrelation is correlation of the errors (residuals) over time Here, residuals show a cyclic pattern, not random. Cyclical patterns are a sign of positive autocorrelation Violates the regression assumption that residuals are random and independent 13-47
• 48. The Durbin-Watson Statistic DCOVA  The Durbin-Watson statistic is used to test for autocorrelation H0: residuals are not correlated H1: positive autocorrelation is present n D  (e  e i 2 i i1 2 ) The possible range is 0 ≤ D ≤ 4 D should be close to 2 if H 0 is true n ei2  i1 D less than 2 may signal positive autocorrelation, D greater than 2 may signal negative autocorrelation 13-48
• 49. Testing for Positive Autocorrelation H0: positive autocorrelation does not exist DCOVA H1: positive autocorrelation is present Calculate the Durbin-Watson test statistic = D (The Durbin-Watson Statistic can be found using Excel or Minitab) Find the values dL and dU from the Durbin-Watson table (for sample size n and number of independent variables k) Decision rule: reject H0 if D < dL Reject H0 0 Inconclusive dL Do not reject H0 dU 2 13-49
• 50. Testing for Positive Autocorrelation (continued) DCOVA  Suppose we have the following time series data:  Is there autocorrelation? 13-50
• 51. Testing for Positive Autocorrelation  (continued) DCOVA Example with n = 25: Excel/PHStat output: Durbin-Watson Calculations Sum of Squared Difference of Residuals 3296.18 Sum of Squared Residuals 3279.98 Durbin-Watson Statistic 1.00494 n D (ei  ei1 )2  i 2 n e i1 2  3296.18  1.00494 3279.98 i 13-51
• 52. Testing for Positive Autocorrelation (continued)  DCOVA Here, n = 25 and there is k = 1 one independent variable  Using the Durbin-Watson table, dL = 1.29 and dU = 1.45  D = 1.00494 < dL = 1.29, so reject H0 and conclude that significant positive autocorrelation exists Decision: reject H0 since D = 1.00494 < d L Reject H0 0 Inconclusive dL=1.29 Do not reject H0 dU=1.45 2 13-52
• 53. Inferences About the Slope DCOVA  The standard error of the regression slope coefficient (b1) is estimated by S YX Sb1   SSX S YX (Xi  X)2  where: Sb1 = Estimate of the standard error of the slope S YX SSE = Standard error of the estimate  n2 13-53
• 54. Inferences About the Slope: t Test DCOVA  t test for a population slope   Null and alternative hypotheses    Is there a linear relationship between X and Y? H0: β1 = 0 H1: β1 ≠ 0 (no linear relationship) (linear relationship does exist) Test statistic t STAT  b1  β 1 Sb 1 d.f.  n  2 where: b1 = regression slope coefficient β1 = hypothesized slope Sb1 = standard error of the slope 13-54
• 55. Inferences About the Slope: t Test Example DCOVA 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 Estimated Regression Equation: 1700 house price  98.25  0.1098 (sq.ft.) The slope of this model is 0.1098 Is there a relationship between the square footage of the house and its sales price? 13-55
• 56. Inferences About the Slope: t Test Example From Excel output: Coefficients Intercept Square Feet DCOVA H0: β1 = 0 H1: β1 ≠ 0 Standard Error t Stat P-value 98.24833 58.03348 1.69296 0.12892 0.10977 0.03297 3.32938 0.01039 b1 Sb1 t STAT  b1  β 1 Sb  0.10977  0  3.32938 0.03297 1 13-56
• 57. Inferences About the Slope: t Test Example DCOVA Test Statistic: tSTAT = 3.329 H0: β1 = 0 H1: β1 ≠ 0 d.f. = 10- 2 = 8 /2=.025 Reject H0 /2=.025 Do not reject H0 -tα/2 -2.3060 0 Reject H0 tα/2 2.3060 3.329 Decision: Reject H0 There is sufficient evidence that square footage affects house price 13-57
• 58. Inferences About the Slope: t Test Example DCOVA H0: β1 = 0 H1: β1 ≠ 0 From Excel output: Coefficients Intercept Square Feet Standard Error t Stat P-value 98.24833 58.03348 1.69296 0.12892 0.10977 0.03297 3.32938 0.01039 Decision: Reject H0, since p-value < α p-value There is sufficient evidence that square footage affects house price. 13-58
• 59. F Test for Significance DCOVA  MSR F Test statistic: F STAT  MSE where MSR  SSR k SSE MSE  n  k 1 where FSTAT follows an F distribution with k numerator and (n – k - 1) denominator degrees of freedom (k = the number of independent variables in the regression model) 13-59
• 60. F-Test for Significance Excel Output DCOVA Regression Statistics Multiple R 0.76211 R Square 0.58082 Adjusted R Square 0.52842 Standard Error FSTAT  MSR 18934.9348   11.0848 MSE 1708.1957 41.33032 Observations 10 With 1 and 8 degrees of freedom p-value for the F-Test ANOVA df SS MS Regression 1 18934.9348 18934.9348 Residual 8 13665.5652 9 11.0848 Significance F 1708.1957 Total F 0.01039 32600.5000 13-60
• 61. F Test for Significance (continued) DCOVA Test Statistic: H0: β1 = 0 H1: β1 ≠ 0  = .05 df1= 1 df2 = 8 FSTAT  Decision: Reject H0 at  = 0.05 Critical Value: F = 5.32 Conclusion:  = .05 0 Do not reject H0 Reject H0 MSR  11.08 MSE F There is sufficient evidence that house size affects selling price F.05 = 5.32 13-61
• 62. Confidence Interval Estimate for the Slope DCOVA Confidence Interval Estimate of the Slope: b1  t α / 2 S b 1 d.f. = n - 2 Excel Printout for House Prices: Coefficients Intercept Square Feet Standard Error t Stat P-value Lower 95% Upper 95% 98.24833 58.03348 1.69296 0.12892 -35.57720 232.07386 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 At 95% level of confidence, the confidence interval for the slope is (0.0337, 0.1858) 13-62
• 63. Confidence Interval Estimate (continued) for the Slope DCOVA Coefficients Intercept Standard Error t Stat P-value Lower 95% Upper 95% 98.24833 Square Feet 58.03348 1.69296 0.12892 -35.57720 232.07386 0.10977 0.03297 3.32938 0.01039 0.03374 0.18580 Since the units of the house price variable is \$1000s, we are 95% confident that the average impact on sales price is between \$33.74 and \$185.80 per square foot of house size 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 13-63
• 64. t Test for a Correlation Coefficient DCOVA   Hypotheses H0: ρ = 0 (no correlation between X and Y) H1: ρ ≠ 0 (correlation exists) Test statistic t STAT  r -ρ 2 1 r n2 (with n – 2 degrees of freedom) where r   r 2 if b1  0 r   r 2 if b1  0 13-64
• 65. t-test For A Correlation Coefficient (continued) Is there evidence of a linear relationship between square feet and house price at the .05 level of significance? H0: ρ = 0 H1: ρ ≠ 0 DCOVA (No correlation) (correlation exists)  =.05 , df = 10 - 2 = 8 t STAT  r ρ 1 r2 n2  .762  0 1  .762 2 10  2  3.329 13-65
• 66. t-test For A Correlation Coefficient (continued) DCOVA t STAT  r ρ 1 r2 n2  .762  0 1  .762 2 10  2  3.329 Conclusion: There is evidence of a linear association at the 5% level of significance d.f. = 10-2 = 8 /2=.025 Reject H0 -tα/2 -2.3060 /2=.025 Do not reject H0 0 Decision: Reject H0 Reject H0 tα/2 2.3060 3.329 13-66
• 67. Estimating Mean Values and Predicting Individual Values DCOVA Goal: Form intervals around Y to express uncertainty about the value of Y for a given X i Confidence Interval for the mean of Y, given Xi Y  Y  Y = b0+b1Xi Prediction Interval for an individual Y, given Xi Xi X 13-67
• 68. Confidence Interval for the Average Y, Given X DCOVA Confidence interval estimate for the mean value of Y given a particular Xi Confidence interval for μ Y|X X : i ˆ Y  t α / 2 S YX hi Size of interval varies according to distance away from mean, X 1 (Xi  X)2 1 (Xi  X)2 hi     n SSX n  (Xi  X)2 13-68
• 69. Prediction Interval for an Individual Y, Given X DCOVA Confidence interval estimate for an Individual value of Y given a particular Xi Confidence interval for YX X : i ˆ Y  t α / 2 S YX 1  hi This extra term adds to the interval width to reflect the added uncertainty for an individual case 13-69
• 70. Estimation of Mean Values: Example DCOVA Confidence Interval Estimate for μY|X=X i Find the 95% confidence interval for the mean price of 2,000 square-foot houses  Predicted Price Yi = 317.85 (\$1,000s) 1 ˆ t Y 0.025S YX  n (X i  X) 2  (X i  X) 2  317.85  37.12 The confidence interval endpoints are 280.66 and 354.90, or from \$280,660 to \$354,900 13-70
• 71. Estimation of Individual Values: Example DCOVA Prediction Interval Estimate for YX=X i Find the 95% prediction interval for an individual house with 2,000 square feet  Predicted Price Yi = 317.85 (\$1,000s) 1 ˆ t Y 0.025S YX 1   n (X i  X) 2  (X i  X) 2  317.85  102.28 The prediction interval endpoints are 215.50 and 420.07, or from \$215,500 to \$420,070 13-71
• 72. Finding Confidence and Prediction Intervals in Excel DCOVA  From 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 13-72
• 73. Finding Confidence and Prediction Intervals in Excel (continued) DCOVA Input values  Y Confidence Interval Estimate for μY|X=Xi Prediction Interval Estimate for Y X=Xi 13-73
• 74. 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 13-74
• 75. Strategies for Avoiding the Pitfalls of Regression   Start with a scatter plot of X vs. Y to observe possible relationship Perform residual analysis to check the assumptions   Plot the residuals vs. X to check for violations of assumptions such as homoscedasticity Use a histogram, stem-and-leaf display, boxplot, or normal probability plot of the residuals to uncover possible non-normality 13-75
• 76. Strategies for Avoiding the Pitfalls of Regression    (continued) If there is violation of any assumption, use alternative methods or models If there is no evidence of assumption violation, then test for the significance of the regression coefficients and construct confidence intervals and prediction intervals Avoid making predictions or forecasts outside the relevant range 13-76
• 77. 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 13-77
• 78. Chapter Summary (continued)     Described inference about the slope Discussed correlation -- measuring the strength of the association Addressed estimation of mean values and prediction of individual values Discussed possible pitfalls in regression and recommended strategies to avoid them 13-78