Eviews output for the value of houses in towns surrounding Boston is in the table: Dependent...
Question:
Eviews output for the value of houses in towns surrounding Boston is in the table:
Dependent Variable: VALUE Observations: 57
| Variable | Coefficient | St. Error |
| C | 32.040 | 6.059 |
| CRIME | -0.631 | 0.289 |
| NITOX | -9.402 | 4.178 |
| ROOM | 11.765 | 4.336 |
| ACCESS | 0.547 | 0.202 |
| TAX | -0.869 | 0.385 |
| STRATIO | -1.543 | 0.814 |
The variables are as follows: VALUE=mean value of owner-occupied homes (in $1000); CRIME=per capita crime rate; NITOX=nitric oxides concentration (parts); ROOM=average number of rooms per dwelling; ACCESS=index of accessibility to highways; TAX=property-tax rate per $10,000; STRATIO=student-teacher ratio by town.
(a) How does each variable affect the value of the home?
(b) Test the hypothesis that an additional room raises the average value of a house by $15,000, against the alternative that the increase is less than $15,000, at the 5% significance level.
Linear Regression:
A linear regression model estimates the relationship between the dependent variable and the independent (explanatory) variables via minimizing the summation of least square errors. The linear regression model's estimates are called at least square estimates.
Answer and Explanation: 1
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- If per capita crime rate increases by one point, the mean value of owner-occupied homes decreases by $631, given everything else held constant.
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Problem Solving Using Linear Regression: Steps & Examples
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Chapter 8 / Lesson 2
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Linear regression is a process used to model and evaluate the relationship between dependent and independent variables. Learn about problem solving using linear regression by exploring the steps in the process and working through examples. Review a linear regression scenario, identify key terms in the process, and practice using linear regression to solve problems.
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