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Unformatted text preview: STATISTICS 211 PROF EMANUEL PARZEN CHAPTER 9 BIVARIATE DATA ANALYSIS, CORRELATION, REGRESSION LINE A very important application of statistical methods is study of relations between two continuous variables X and Y . given observed data ( 29 , , 1, , j j X Y j n = . One first examines the data by a plot called a scatter diagram or scatter plot to guess what kind of curve fits the points. Often the curve is one of the following types: (A) Line (B) Curve similar to parabola (C) two groups separated by a gap This chapter discusses quantitative methods that apply when looking at the data by scatter plot concludes that points vary around a fitted line. A. PROBABILITY THEORY OF LINEAR REGRESSION MODEL RELATION ( 29 , X Y To understand theory and practice of correlation coeffi cients ( 29 , R X Y we must introduce concept of regression line to fit data which is motivated by concept of a model in the popula tion for the relation of two variables X and Y . We assume that X and Y are both random variables having population means ( 29 X , ( 29 Y Population Variances ( 29 ( 29 ( 29 2 2 X E X X = , ( 29 ( 29 ( 29 2 2 Y E Y Y = and population covariance 2 ( 29 ( 29 ( 29 ( 29 ( 29 cov , X Y E X X Y Y = Song of Sums formula for variance of sum X+Y and difference XY is VAR[X+Y]=VAR[X]+VAR[Y]+2 COV[X,Y] VAR[XY]=VAR[X]+VAR[Y]2 COV[X,Y] Population correlation coefficient is defined ( 29 ( 29 ( 29 ( 29 ( 29 ( 29 ( 29 ( 29 cov , , X Y X X Y Y R X Y E X Y X Y  = = Important inequality: 1&lt;R(X,Y)&lt;1 EXAMPLE&gt; Assume VAR[X]=VAR[Y\=1, COV[X,Y]=.5 Then VAR[X+Y]=3, VAR[X_Y]=1, R(X,Y)=.5 Note that VAR[2X]=4 VAR[X]=4, COV[2X,Y]=2 COV[X,Y]=1, R(2X,2Y)=R(X,Y)=.5 B. LINEAR PREDICTION OF Y FROM X Linear Regres sion model of Y given X is motivated by problem of predicting value of Y from value of X by minimum mean square predic tion of Y by a linear function of X denoted ( 29 ( 29 ( 29 ( 29  Y X Y b X X = + where b is chosen to minimize ( 29 ( 29 ( 29 ( 29 ( 29 ( 29 ( 29 2 2  MSE b E Y Y X E Y Y b X X = = To minimize ( 29 MSE b we apply calculus, take derivative of ( 29 MSE b with respect to b , and solve for value of b at which de rivative equals zero. Derivative of ( 29 MSE b is ( 29 ( 29 ( 29 ( 29 ( 29 ( 29 E Y Y b X X X X  = ; 3 [ ] [ ] cov , X Y bVAR X = HOW? Conclusion: compute population slope coefficient b by formula [ ] [ ] cov , X Y b VAR X = The minimum of ( 29 MSE b equals [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] 2 2 2 2 cov , cov , cov , 1 VAR Y b X Y b VAR X X Y VAR Y VAR X X Y VAR Y VAR X VAR Y + = = HOW? Conclusion: compute population minimum ( 29 MSE b by formula for minimum mean square error...
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 Fall '07
 Parzen
 Statistics, Correlation, Normal Distribution, Regression Analysis, Variance, var, linear regression model

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