Question

The cotton aphid poses a threat to cotton crops in Iraq. The accompanying data on y = infestation rate (aphids/100 leaves) x1 = mean temperature (C) x2 = mean relative humidity appeared in the article “Estimation of the Economic Threshold of Infestation for Cotton Aphid” (Mesopotamia Journal of Agriculture [1982]: 71–75). Use the data to find the estimated regression equation and assess the utility of the multiple regression model
This activity requires the use of a statistical computer package capable of fitting multiple regression models. Background: The given data on y, x1, x2, x3, and x4 were generated using a computer package capable of producing random observations from any specified normal distribution. Because the data were generated at random, there is no reason to believe that y is related to any of the proposed predictor variables x1, x2, x3, and x4.
you expected based on the way the data were generated? Explain.
2. Fit each of the following regression models: i. y with x1 ii. y with x1 and x2 iii. y with x1 and x2 and x3 iv. y with x1 and x2 and x3 and x4 3. Make a table that gives the R2 , the adjusted R2 , and se values for each of the models fit in Step 2. Write a few sentences describing what happens to each of these three quantities as additional variables are added to the multiple regression model. 4. Given the manner in which these data were generated, what is the implication of what you observed in Step 3? What does this suggest about the relationship between number of predictors and sample size?


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  • CreatedSeptember 19, 2015
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