CATCH RESIDENCE SIZE ACCESS STRUCTURE 3.6 92.2 0.21 81 0.8 86.7 0.30 26 2.5 80.2 0.31 52 2.9 87.2 0.40 64 1.4 64.9 0.44 40 0.9 90.1 0.56 22 3.2 60.7 0.78 80 2.7 50.9 1.21 60 2.2 86.1 0.34 1 30 5.9 90.0 0.40 1 90 3.3 80.4 0.52 1 74 2.9 75.0 0.66 1 50 3.6 70.0 0.78 1 61
A state fisheries commission wants to estimate the number of bass caught in a given
lake during a season in order to restock the lake with the appropriate number of
young fish. The commission has information on the seasonal catch (CATCH) in
thousands of bass per square mile; the size of the lake in square miles (SIZE); the
number of lakeshore residences per square mile (RESIDENCE); the number of living
places for bass (i.e. weed beds, sunken trees, drop offs etc.) (STRUCTURE) and
ACCESS a dummy variable equal to 1 if the lake has public access and 0 if not.
What do you think are the expected signs of the regression coefficients?
Justify your choice.
Using R-studio, estimate the multiple linear regression model. ALL input codes and output tables should be provided.
(b) Is the overall regression equation statistically significant at the 1% level?
Justify your answer. Ensure that you state the null hypothesis(es) and the
decision rule in your analysis.
(c) Find and interpret a 95% confidence interval for the change in the seasonal catch resulting from a one-unit increase in the number of living places for bass, with all other variables unchanged.
Trending now
This is a popular solution!
Step by step
Solved in 2 steps with 1 images
