The manager of Shrimp Jamaica wants to analyze the market demand for shrimp during the restricted season for shrimp catching. He believes that six variables (price per pound, advertising in dollars, competitor's pricing per pound in dollars, income in dollars, population, and time in storage per day) are crucial in explaining shrimp demand. As part of the analysis, he collects the necessary data and generates the edited version of a multiple regression output shown in Exhibit 1.

SUMMARY OUTPUT [Exhibit 1]

Multiple R 0.94656

R Square 0.896

Adjusted R Square 0.881

Standard Error 60699.81

Observations 48

ANOVA

Regression 6 1.3E+12 2.17E+11 58.856 1.39E-18

Residual 41 1.51E+11 3.68E+09

Total 47 1.45E+12

Coefficients Standard t Stat P- Lower 95% Upper

Error value 95%

Intercept 646957.8 154146.9 4.197 0.0001 335651 958264

Price (x1)	-127443 15112.6 -8.433	1.7E-10	-157963	-96922
Ads(x2)	5.353 1.113918 4.806	2.09E-05	3.1034	7.6026
Comp_Price (x3) Income (x4) Population (x5) Time Var (x6)	29337 12388.6 2.368 0.344 3.18672 0.108 0.024 0.00235 10.20 4405.9 4400.4 1.001	0.0227 0.9145 8.12E-13 0.3226	4318.069 -6.09158 0.01922 -4480.84	54356.6 6.7798 0.0287 13292.7

Required:

1. State and explain two (2) assumptions of the Multiple Linear Regression Model.
2. Comment on the overall adequacy of the multiple linear regression model. (Use

0.05 level of significance).

1. Write out the estimated regression equation from the output in exhibit 1.
2. Interpret the estimated regression coefficients for price (x1), com_price(x3) and time var (x6).
3. Which variable(s) are not significant to the regression model. Explain by outlining the steps of the t-test. (Assume 5% level of significance).

State and interpreted the adjusted R.