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Chapter+4

# Chapter+4 - Instruc ctor H.H Kim 1 Econo ometrics on Linear...

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Instruc Linear Linear ctor : H.H. Regressio regression Kim on Model n model w with a sing TESTSCR 1 gle regress 600 620 640 660 680 700 720 12 sor 14 16 18 20 STR Econo 0 22 ometrics 24 26

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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, EሺYሻ ൌ ߤ - Defining β as the least square estimator of β
Instruc - D - E S ctor : H.H. Deriving β Example : Student-Te Kim : OLS Estim eacher Ra mates of th atio 3 he Relatio nship Betw ween Test Econo t Scores an ometrics nd the

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Fall 2011 Rutgers University 4 - Predicted Value Measures of Fit - R 2 o The fraction of the sample variance of Y i explained by X i - The Standard Error of the Regression (SER) o The estimator of the standard deviation of u i