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6167SU09HW1

# 6167SU09HW1 - Compute the predicted cost of operation for...

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STA 6167 Homework Assn #1 Spring 2007 1. Refer to the data from 19 livestock auction markets in the data set AUCTION. The variables CATTLE, CALVES, HOGS, and SHEEP are the numbers of head (in thousands) of those categories of animals that were sold at each market. The variable COST is the cost (in thousands of dollars) of operating the market. The objective of this study was to relate COST to the numbers of head of livestock. For this homework assignment, you will use only CATTLE as an independent variable. a. Obtain the simple linear regression equation relating COST to CATTLE. b. Construct a 95% confidence interval for the cost per head of cattle. c. Construct a plot of COST versus CATTLE. Does COST appear to be linearly related to CATTLE? d. Plot the residuals versus predicted values. Do see a random scattering of points? Are there apparent outliers? e. Remove apparent outliers and re-work parts a. and b. How did the results change? f. Use the analysis based on the entire data set.
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Unformatted text preview: Compute the predicted cost of operation for each market based on the model equation. g. Plot a band of 95% confidence intervals for the expected cost of operation of all markets. How many observed cost values are in the intervals? h. Plot a band of 95% prediction intervals for the cost of operation of individual markets. How many observed cost values are in the intervals? i. Fit a multiple linear regression equation relating COST to CATTLE and CALVES. Write the prediction equation. Are CATTLE and CALVES apparently collinear? j. How did your estimate of the cost per head of CATTLE change in this model? Why? k. How did the standard error of your estimate of cost per head of CATTLE change? Why? l. What is the interpretation of the regression coefficient for CATTLE in this model? m. Construct a 95% confidence interval for the cost per head of cattle. n. Select a regression model to relate COST to the set of variables CATTLE, CALVES, HOGS, and SHEEP....
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