CQMS202-Business Statistics II Chapter14
Chapter14 Multiple Regression
1. Describe the relationship between several independent variables
and a dependent variable using a multiple regression
2. Compute and interpret the multiple standard error and the
coefficient of determination
3. Conduct a Global test (F test) to determine the usefulness of the
4. Conduct a test of hypothesis on each of the regression coefficient.
5. Use the multiple regression model for estimation and prediction
Multiple Regression Models
1. The simple linear regression model was used to analyze how one
quantitative variable (the dependent variable y) is related to one
other quantitative variable (the independent variable x).
Multiple regression allows for any number of independent variables.
The director of marketing at Ryerson Wholesale Products is studying
the monthly sales. Three independent variables were selected as
estimators of sales: regional population ( x 1) , per-capita income ( x 2)
and regional unemployment rate ( x3 ). The regression equation was
computed to be (in dollars):
y = 64100 + 0.394 x + 9.6 x − 11600 x
1 2 3
a. What is the full name of the equation?
b. Interpret the number 64100
c. What are the estimated monthly sales for a particular region with a
population of 796000 per-capita income of $6940, and an
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unemployment rate of 6.0 percent?(Use 6.0 in your calculation)
We expect to develop models that fit the data better than would a
simple linear regression model.
4. General form of the Multiple Regression Model
y = β 0 β x 1 1 x + 2..2+ β x + εk k
where is the dependent variable
x1, x 2..., x k are independent variables
E (y) = β 0 β x +1 1x + .2.2+ β x k k is the deterministic portion of
β i determines the contribution of the independent variable
Note: The symbols x 1 x ,2.., xk may represent higher-order terms.
For example, x 1 might represent the current interest rate, x 2 might
represent x1 , and so forth.
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