# 1 Theory and Estimation of Regression Models Simple Regression Theory Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎

## Presentation on theme: "1 Theory and Estimation of Regression Models Simple Regression Theory Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎"— Presentation transcript:

1 Theory and Estimation of Regression Models Simple Regression Theory Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎

2 Population Line: uiui Y i = E[Y i ]+u i Xi E[Y] = B 0 +B 1 X E[Y i ] = B 0 +B 1 X i Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎

3 Population Line: eiei Y i = Y i + e i Xi E[Y] = B 0 +B 1 X Y i = B 0 +B 1 X i ^ ^^^ Estimated Line: Y = B 0 +B 1 X ^^^ uiui E[Y i ] Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎

4 eiei Xi Y = B 0 +B 1 X ^^^ eiei eiei eiei eiei eiei Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎

Metode Ordinary Least Squares (OLS) In the Ordinary Least Squares (OLS) method, the criterion for estimating β 0 and β 1 is to make the sum of the squared residuals (SSR) of the fitted regression line as small as possible i.e.: Minimize SSR = minimize = minimize Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎

6 Rumus estimator OLS : (5.12) (5.13) Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎ Metode Ordinary Least Squares (OLS)

7 Garis regresi yang diestimasi dengan menggunakan metode OLS mempunyhai ciri- ciri : 1. (i.e. the sum of its residuals is zero) 2. It always passes through the point The residual values (e i ’s) are not correlated with the values of the independent variable (X i ’s) Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎ Metode Ordinary Least Squares (OLS)

8 Interpretasi Model Regresi Assume, for example, that the estimated or fitted regression equation is:  or  Y i = 3.7 + 0.15X i + e i Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎

9

10 The value of = 0.15 indicates that if the average cotton price received by farmers in the previous year increases by 1 cent/pound (i.e.  X=1), then this year’s cotton acreage is predicted to increase by 0.15 million acres (150,000 acres). Y i = 3.7 + 0.15X i + e i Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎ Interpretasi Model Regresi

11 The value of = 3.7 indicates that if the average cotton price received by farmers in the previous year was zero (i.e. =0), the cotton acreage planted this year will be 3.7 million (3,700,000) acres; sometimes the intercept makes no practical sense. Y i = 3.7 + 0.15X i + e i Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎ Interpretasi Model Regresi

12 There are two statistics (formulas) that quantify how well the estimated regression line fits the data: 1. The standard error of the regression (SER) (Sometimes called the standard error of the estimate) 2. R 2 - coefficient of determination Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎ Mengukur Goodness of Fit: R 2

13 SER agak berbeda dengan simpangan-baku (standard deviasi S) e i (oleh derajat bebasnya): (5.20) Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎ Mengukur Goodness of Fit: R 2

 The term on the left measures the proportion of the total variation in Y not explained by the model (i.e. by X) R 2 mengukur proporsi dari total ragam Y yang dapat dijelaskan oleh model (yaitu dijelaskan oleh X) Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎

15 The Gauss-Markov Theorem states the properties of the OLS estimators; i.e. of the: and They are unbiased E[B 0 ]= and E[B 1 ]= Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎ Sifat-sifat Estimator OLS

16 Sifat-sifat Estimator OLS If the dependent variable Y (and thus the error term of the population regression model, u i ) has a normal distribution, the OLS estimators have the minimum variance Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎

17 BLUE – Best Linear Unbiased Estimator Unbiased => bias of β j = E(β j ) - β j = 0 Best Unbiased => minimum variance & unbiased Linear => the estimator is linear ^ ^ Sumber: www.aaec.ttu.edu/faculty/omurova/aaec_4302/.../Chapter%205.ppt‎ Sifat-sifat Estimator OLS

Similar presentations