In Chapter 16, Exercise 54 predicted the price ($/lb) of lobster harvested in the Maine lobster fishing industry. Here€™s a multiple regression to predict the Price from the number of Traps (millions), the number of Fishers, and Pounds/Trap during the years 1957 to 2012.

Dependent variable is: Price/lb

R squared = 94.2% R squared (adjusted) = 93.8%

s = 0.2850 with 56 - 4 = 52 degrees of freedom

a) Write the regression model.
b) Are the assumptions and conditions met?
c) State and test the standard null hypothesis for the coefficient of Pounds/Trap. Use the standard a-level of .05 and state your conclusion.
d) Does the coefficient of Pounds/Trap mean that when the pounds per trap declines the price will increase?
e) This model has an adjusted R2 of 93.8%. The previous model of Chapter 16, Exercise 56, had an adjusted R2 of 91.6%. What does adjusted R2 say about the two models?

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Source Regression 68.3949 Residual Sum of Squares df Mean Square F-ratio 3 22.7983 52 0.0812 280.70 4.2234 Variable Coefficient SE(Coeff) t ratio P-value Intercept 1.0942 Traps(M) Fishers -0.000149 0.0000339-4.40 <0.0001 Pounds/ 0.3118 0.0443 3.51 27 , 90 0.0009 <0. 0001 1.2357 -0.0180 0.00517 3.47 0.0011 0.5 -0.5 Predicted 15 10 -0.5 0.0 0.5 Residuals