Multiple Linear Regression Introduction to Business Statistics 5

Multiple Linear Regression Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

The Multiple Regression Model Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

Assumptions of the Multiple Regression Model • The errors follow a normal distribution, centered at zero, with common variance • The errors are independent Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

Multiple Regression Model Assumptions Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing Figure 15. 3

Hypothesis Test for the Significance of the Model Ho: 1 = 2 = … = k Ha: at least one of the ’s 0 Reject Ho if F > F , k, n-k-1 Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

F Statistic Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing Figure 15. 5

Test for Ho: i = 0 Ho: 1 = 0 Ha : 1 0 Reject Ho if |t| > t . /2, n-k-1 Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

(1 - ) 100% Confidence Interval for i Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

Coefficient of Determination SSR R 2 = SST Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

Curvilinear Models Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

Curvilinear Models Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing Figure 15. 10

Extrapolation with Curvilinear Models Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing Figure 15. 12

Multicollinearity- VIF’s Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

Dummy Variables The use of dummy or indicator variables in a regression analysis allows you to include qualitative variables in the model. For example: Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

Stepwise Procedures allow the evaluation of an additional variable in an established model. There are three methods: Forward Regression Backward Regression and Stepwise Regression Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

Figure 15. 18 Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

(1 - ) 100% Confidence Interval for y|x Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

Prediction Interval for YXo Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

Interaction Effects Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing

Interaction Effects Introduction to Business Statistics, 5 e Kvanli/Guynes/Pavur (c)2000 South-Western College Publishing Figure 15. 23