Use Auto MPG data file. Unit of analysis in this dataset is an individual car. (N=398). Delete all rows that have a missing value on horsepower. The resulting final sample size should be 392. Answer questions in the sequence in which they are presented. Variable definitions are as follows:

id: unique car ID
mpg: miles per gallon (fuel efficiency)
cylinders: number of cylinders
displacement: engine displacement (in cubic inches)
horsepower: engine horsepower
weight: vehicle weight (in lbs)
acceleration: time to accelerate from 0 mph to 60 mph (in seconds)
model_year: model year
car_name: car name

1. Construct a scatter plot with mpg on the Y axis and horsepower on the X-axis. This scatter plot suggests that there is ______relationship between the two variables.


2. Construct a scatter plot with mpg on the Y-axis and acceleration on the X-axis. The scatter plot suggests that there is. ____ relationship between the two variables.


3. Estimate a simple linear regression model that predicts mpg(Y) from horsepower(X). The R-Square in this model equals ___.


4. Estimate a simple linear regression model that predicts mpg (Y) from acceleration (X). The R-Square in this model equals ___.


5. R square always ranges between ____ and ____.


6. It is possible for the Adjusted R square value to be negative.


7. Estimate a multiple linear regression model that predicts mpg (Y) from horsepower (X1) and acceleration (X2). The R-Square in this model equals___.


8. Estimate a multiple linear regression model that predicts mpg (Y) from horsepower (X1) and acceleration (X2). Regression ANOVA results suggest that _____ (base your answer only on the ANOVA table; ignore all other output.)

9. Estimate a multiple linear regression model that predicts mpg (Y) from horsepower (X1) and acceleration (X2). Regression results suggest that ____.


10. Estimate a multiple linear regression model that predicts mpg (Y) from horsepower (X1) and acceleration (X2). Regression results suggests that ____.