Campus crime rates are generally lower than the national average;however thousands of crimes take place on college campuses daily.Cities that are notoriously dangerous would likely be undesirable locations for a college campus.A study examined the crime rates on campuses throughout the United States and whether or not they were significantly affected by surrounding cities.A regression analysis was performed to investigate which characteristics of a city,along with a few chosen demographics of a school,impacted the crime rate on a college campus.There are over 4000 colleges and universities in the United States.The study included a random sample of 129 institutions.The response variable was the number of crimes per 1000 people.Explanatory variables included the percent of married couples in the city (married) ,tuition of the university (tuition) ,average income of the city (income) ,unemployment rate of the city (unemployment) ,percent of students who belong to a fraternity or sorority (Greek) ,average age of the students at the university (age) ,and number of liquor stores in the city (liquor) .A complete analysis of the data is shown below.
The explanatory variables tuition,age,and liquor not highly corrected with crime.Does that mean they should not be in the model?
A) Yes,because only variables that are highly correlated with the response variable will be significant predictors in a regression analysis.
B) No.Just because a variable is not correlated with the response variable does not imply it will not be a statistically significant predictor.
C) No,because as long the correlation coefficient is large enough,that variable will be a significant predictor in a regression model.
D) Yes.Only in simple linear regression would variables that are not highly correlated with the response variable be statistically significant predictors in a regression model.
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