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# Statr session14, Jan 11

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### Statr session14, Jan 11

1. 1. Correlation and Regression Analysis: Learning Objectives • Explain the purpose of regression analysis and the meaning of independent versus dependent variables. • Compute the equation of a simple regression line from a sample of data, and interpret the slope and intercept of the equation. • Estimate values of Y to forecast outcomes using the regression model. • Understand residual analysis in testing the assumptions and in examining the fit underlying the regression line. • Compute a standard error of the estimate and interpret its meaning. • Compute a coefficient of determination and interpret it.
2. 2. Correlation • Correlation is a measure of the degree of relatedness of variables. • Coefficient of Correlation (r) - applicable only if both variables being analyzed have at least an interval level of data.
3. 3. Three Degrees of Correlation r<0 r>0 r=0
4. 4. Degree of Correlation • The term (r) is a measure of the linear correlation of two variables – The number ranges from -1 to 0 to +1  Positive correlation: as one variable increases, the other variable increases  Negative correlation: as one variable increases, the other one decreases  No correlation: the value of r is close to 0 – Closer to +1 or -1, the higher the correlation between two variables
5. 5. Pearson Product-Moment Correlation Coefficient
6. 6. Regression Analysis • Regression analysis is the process of constructing a mathematical model or function that can be used to predict or determine one variable by another variable or variables.
7. 7. Simple Regression Analysis • Bivariate (two variables) linear regression -- the most elementary regression model – dependent variable, the variable to be predicted, usually called Y – independent variable, the predictor or explanatory variable, usually called X – Usually the first step in this analysis is to construct a scatter plot of the data • Nonlinear relationships and regression models with more than one independent variable can be explored by using multiple regression models
8. 8. Regression Models • Deterministic Regression Model - - produces an exact output: ˆ y   0  1 x • Probabilistic Regression Model ˆ y   0  1 x   • 0 and 1 are population parameters • 0 and 1 are estimated by sample statistics b0 and b1
9. 9. Equation of the Simple Regression Line
10. 10. A typical regression line Y ϴ X
11. 11. Least Squares Analysis • Least squares analysis is a process whereby a regression model is developed by producing the minimum sum of the squared error values • The vertical distance from each point to the line is the error of the prediction. • The least squares regression line is the regression line that results in the smallest sum of errors squared.
12. 12. Least Squares Analysis   X  X Y  Y    XY  nXY  b  X n X  X  X  2 1 2 2   Y   X b Y b X  n b n 0 1 1  X  Y  XY  n X 2   X n 2
13. 13. Least Squares Analysis SSXY    X  X Y  Y    SSXX   b1  X  X 2  X  X  Y  XY  n 2   X 2 n SSXY SSXX Y   X b  Y b X  n b n 0 1 1
14. 14. Airlines Cost Data include the costs and associated number of passengers for twelve 500-mile commercial airline flights using Boeing 737s during the same season of the year. Number of Passengers 61 63 67 69 70 74 76 81 86 91 95 97 Cost (\$1,000) 4,280 4,080 4,420 4,170 4,480 4,300 4,820 4,700 5,110 5,130 5,640 5,560
15. 15. Number of Passengers x x2 61 63 67 69 70 74 76 81 86 91 95 97 x Cost (\$1,000) y 4.28 4.08 4.42 4.17 4.48 4.30 4.82 4.70 5.11 5.13 5.64 5.56 3,721 3,969 4,489 4,761 4,900 5,476 5,776 6,561 7,396 8,281 9,025 9,409 = 930 y = 56.69 x 2 = 73,764 xy 261.08 257.04 296.14 287.73 313.60 318.20 366.32 380.70 439.46 466.83 535.80 539.32  xy = 4,462.22
16. 16. SS XY   XY  SS XX  X b1  b0  2   X Y n ( X ) 2 n  4,462 .22  (930 )( 56 .69 )  68 .745 12 (930 ) 2  73,764   1689 12 SS XY 68 .745   .0407 SS XX 1689 Y n  b1 X n ˆ Y  1.57  .0407 X  56 .69 930  (. 0407 )  1.57 12 12
17. 17. Residual Analysis
18. 18. Residual Analysis: Airline Cost Example Number of Passengers X 61 63 67 69 70 74 76 81 86 91 95 97 Cost (\$1,000) Y Predicted Value ˆ Y Residual ˆ Y Y 4.28 4.08 4.42 4.17 4.48 4.30 4.82 4.70 5.11 5.13 5.64 5.56 4.053 4.134 4.297 4.378 4.419 4.582 4.663 4.867 5.070 5.274 5.436 5.518 .227 -.054 .123 -.208 .061 -.282 .157 -.167 .040 -.144 .204 .042  (Y  Yˆ )  .001
19. 19. Residual Analysis: Airline Cost Example Outliers: Data points that lie apart from the rest of the points. They can produce large residuals and affect the regression line.
20. 20. Using Residuals to Test the Assumptions of the Regression Model • The assumptions of the regression model – The model is linear – The error terms have constant variances – The error terms are independent – The error terms are normally distributed
21. 21. Using Residuals to Test the Assumptions of the Regression Model • The assumption that the regression model is linear does not hold for the residual plot shown above • In figure (a) below the error variance is greater for smaller values of x and smaller for larger values of x and vice-versa in figure (b) below. This is a case of heteroscedasiticity.
22. 22. Standard Error of the Estimate • Residuals represent errors of estimation for individual points. • A more useful measurement of error is the standard error of the estimate. • The standard error of the estimate, denoted by se, is a standard deviation of the error of the regression model.
23. 23. Standard Error of the Estimate Sum of Squares Error SSE   Standard Error of the Estimate    Y Y 2   Y  b0  Y  b1  XY 2 SSE Se  n  2
24. 24. Determining SSE for the Airline Cost Data Example Number of Passengers X Cost (\$1,000) Y Residual ˆ Y Y ˆ (Y  Y ) 2 61 63 67 69 70 74 76 81 86 91 95 97 4.28 4.08 4.42 4.17 4.48 4.30 4.82 4 .70 5.11 5.13 5.64 5.56 .227 -.054 .123 -.208 .061 -.282 .157 -.167 .040 -.144 .204 .042 .05153 .00292 .01513 .04326 .00372 .07952 .02465 .02789 .00160 .02074 .04162 .00176  (Y ˆ  Y )  .001  (Y ˆ  Y ) 2 =.31434 Sum of squares of error = SSE = .31434
25. 25. • The coefficient of determination is the proportion of variability of the dependent variable (y) accounted for or explained by the independent variable (x) • The coefficient of determination ranges from 0 to 1. • An r 2 of zero means that the predictor accounts for none of the variability of the dependent variable and that there is no regression prediction of y by x. • An r 2 of 1 means perfect prediction of y by x and that 100% of the variability of y is accounted for by x.
26. 26. SSYY   Y Y   Y 2  Y   2 2 n SSYY  exp lained var iation  un exp lained var iation SSYY  SSR  SSE SSR SSE 1  SSYY SSYY SSR 2  r SSYY SSE  1 SSYY SSE  1 2 Y 2 Y  n  
27. 27. SSE  0.31434  Y   270.9251 56.69  3.11209  Y  2 SSYY 2 n SSE r  1 SSYY .31434  1 3.11209  .899 2 2 12 89.9% of the variability of the cost of flying a Boeing 737 is accounted for by the number of passengers.
28. 28. Exercise in R: Linear Regression Open URL: www.openintro.org Go to Labs in R and select 7 - Linear Regression