The Drag Net Fishing Company (DNF) has hired you as a consultant to analyze the

Question

The Drag Net Fishing Company (DNF) has hired you as a consultant to analyze the demand by local restaurants for fresh fish. The available data set includes monthly observations collected over the past 5 years on the number of hundreds of pounds of fish purchased by local restaurants per month (O), the price per pound of fish (P), the average cost of a meal at a local restaurant (M), a seasonal variable (S) that is equal to one during the tourist season and zero otherwise, and average household income (in thousands of dollars) in the area. The results of a regression analysis (with standard errors in parenthesis) are given below -0.6M 4.2S 0.05 (0.12) Q= 110-0.2P (42) (0.28) (0.70) (0.024) R = 0.74 s.E.E. = 12.9 () Evaluate the statistical significance of the equation as a whole and of each of its coefficients

Explanation / Answer

i) Test statistic t = 0.2 / 0.12 = 1.666

Critical value of t = 2 at 5% los and 60-2=58 df

here, t valaue < t critical value, we accept H0

There is no significant effect of regression coefficient (price per pound of fish) on prediction or regresssion equation.

ii)

Test statistic t = 0.6 / 0.28 = 2.143

Critical value of t = 2 at 5% los and 60-2=58 df

here, t valaue > t critical value, we do not accept H0

There is significant effect of regression coefficient (meal cost at local restarent) on prediction or regresssion equation.

iii)

Test statistic t = 4.2/ 0.70 = 6

Critical value of t = 2 at 5% los and 60-2=58 df

here, t valaue > t critical value, we do not accept H0

There is significant effect of regression coefficient (seasonal variable) on prediction or regresssion equation.

iv)

Test statistic t = 0.05/0.024=2.0833

Critical value of t = 2 at 5% los and 60-2=58 df

here, t valaue > t critical value, we do not accept H0

There is significant effect of regression coefficient (oncome) on prediction or regresssion equation.

v) R2 = 0.74 , R = 0.8602

Critical value of r =0.254

Here R > r critical values, so we reject H0

Thus we conclude that the regression equation is best fit to the given data

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