LCT26 - Introduction to Business Statistics Lecture 26...

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1 Introduction to Business Statistics L e c t u r e 2 6 Multiple Regression Analysis Introduction: Including more independent variables in a regression sometimes can greatly increase its prediction and explanation power. Example: Dependent variable Y Independent variables X Market value of a flat size, # of rooms, # of bath rooms, age, location, view Salary experience, education, performance
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2 The Model: } part random part systematic 2 2 1 1 0 ε β + + + + + = 4 4 4 4 43 4 4 4 4 42 1 L k k X X X Y Assumptions: Random errors are normally distributed with mean zero and variance 2 σ . Random errors for different values of Y are independent. Regression parameters k , , , 1 0 L are constants. Independent variables k X X X , , , 2 1 L are constants measured without error.
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3 The population regression equation: k k Y X X X β μ + + + + = L 2 2 1 1 0 , where 0 is the intercept and k , , 1 L are called partial slopes . Interpretation of i ( k i , , 1 L = ): When we increase X i by one unit keeping all other X’s fixed, on the average Y will increase by i . Method of least squares for fitting: Find k b b b , , , 1 0 L such that + + = = n i ki k i i X X Y L 1 2 1 1 0 )] ( [ L is minimized.
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This note was uploaded on 02/13/2012 for the course ISOM 111 taught by Professor Hu,inchi during the Fall '10 term at HKUST.

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LCT26 - Introduction to Business Statistics Lecture 26...

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