Chapter+4 - Instruc Linear Linear ctor : H.H. Regressio...

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Unformatted text preview: Instruc Linear Linear ctor : H.H. Regressio regression Kim on Model n model w ith a sing TESTSCR 1 gle regress 600 620 640 660 680 700 720 12 sor 14 16 18 2 STR Econo 22 ometrics 24 26 Fall 2011 Rutgers University 2 Estimating the Coefficients of the Linear Regression Model TESTSCR Mean 654.1565 Median 654.4500 Maximum 706.7500 Minimum 605.5500 Std. Dev. 19.05335 Skewness 0.091615 Kurtosis 2.745712 Jarque-Bera 1.719129 Probability 0.423346 Sum 274745.8 Sum Sq. Dev. 152109.6 Observations 420 STR Mean 19.64043 Median 19.72321 Maximum 25.80000 Minimum 14.00000 Std. Dev. 1.891812 Skewness -0.025365 Kurtosis 3.609597 Jarque-Bera 6.548185 Probability 0.037851 Sum 8248.979 Sum Sq. Dev. 1499.581 Observations 420 The Ordinary Least Squares Estimator - is the least square estimator of population mean, EY - Defining as the least square estimator of Instruc- D- E S ctor : H.H....
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Chapter+4 - Instruc Linear Linear ctor : H.H. Regressio...

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