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The appraisal of warehouse can appear straightforward compared to other appraisal assignments. A W warehouse appraisal involves comparing a building that is primarily an open shell to other such buildings. However, there are still a number of warehouse attributes that are plausibly related to appraised value. Consider the accompanying data on truss height (ft), which determines how high stored goods can be stacked, and sale price ($) per square foot. 16 Height 12 14 14 15 15 Price 35.51 37.84 36.88 39.99 37.98 37.51 Height 24 26 26 27 Price 46.19 50.33 49.11 48.09 18 22 24 40.99 48.51 47.01 47.52 28 30 30 33 36 50.92 54.76 54.34 57.16 57.45 22 (a) Estimate the true average change in sale price associated with a one-foot increase in truss height, and do so in a way that conveys information about the precision of estimation. (Use a 95% CI. Round your answers to three decimal places.) ], $[ dollars per square foot (b) Estimate the true average sale price for all warehouses having a truss height of 25 ft, and do so in a way that conveys information about the precision of estimation. (Use a 95% CI. Round your answers to three decimal places.) (c) Predict the sale price for a single warehouse whose truss height is 25 ft, and do so in a way that conveys information about the precision of prediction. (Use a 95% PI. Round your answers to three. decimal places.) dollars per square foot How does this prediction compare to the estimate of (b)? The prediction interval is ---Select--the confidence interval in part (b). (d) Without calculating any intervals, how would the width of a 95% prediction interval for sale price when truss height is 25 ft compare to the width of a 95% interval when height is 30 ft? Explain your reasoning. Since 25 is ---Select--the mean than 30, a PI at 30 would be ---Select-- than the PI at 25.

The appraisal of warehouse can appear straightforward compared to other appraisal assignments. A W warehouse appraisal involves comparing a building that is primarily an open shell to other such buildings. However, there are still a number of warehouse attributes that are plausibly related to appraised value. Consider the accompanying data on truss height (ft), which determines how high stored goods can be stacked, and sale price ($) per square foot. 16 Height 12 14 14 15 15 Price 35.51 37.84 36.88 39.99 37.98 37.51 Height 24 26 26 27 Price 46.19 50.33 49.11 48.09 18 22 24 40.99 48.51 47.01 47.52 28 30 30 33 36 50.92 54.76 54.34 57.16 57.45 22 (a) Estimate the true average change in sale price associated with a one-foot increase in truss height, and do so in a way that conveys information about the precision of estimation. (Use a 95% CI. Round your answers to three decimal places.) ], $[ dollars per square foot (b) Estimate the true average sale price for all warehouses having a truss height of 25 ft, and do so in a way that conveys information about the precision of estimation. (Use a 95% CI. Round your answers to three decimal places.) (c) Predict the sale price for a single warehouse whose truss height is 25 ft, and do so in a way that conveys information about the precision of prediction. (Use a 95% PI. Round your answers to three. decimal places.) dollars per square foot How does this prediction compare to the estimate of (b)? The prediction interval is ---Select--the confidence interval in part (b). (d) Without calculating any intervals, how would the width of a 95% prediction interval for sale price when truss height is 25 ft compare to the width of a 95% interval when height is 30 ft? Explain your reasoning. Since 25 is ---Select--the mean than 30, a PI at 30 would be ---Select-- than the PI at 25.

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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The appraisal of a warehouse can appear straightforward compared to other appraisal assignments. A warehouse appraisal involves comparing a building that is primarily an open shell to other such buildings. However, there are still a number of warehouse attributes that are plausibly related to appraised value. Consider the accompanying data on truss height (ft), which determines how high stored goods can be stacked, and sale price ($) per square foot. Height 12 14 15 16 14 Price 35.51 37.84 36.88 39.99 37.98 37.51 15 26 28 Height 24 26 27 30 33 36 Price 46.19 50.33 49.11 48.09 50.92 54.76 54.34 57.16 57.45 18 40.99 dollars per square foot 22 48.51 47.01 47.52 30 22 dollars per square foot (a) Estimate the true average change in sale price associated with a one-foot increase in truss height, and do so in a way that conveys information about the precision of estimation. (Use a 95% CI. Round your answers to three decimal places.) $ $ 24 (b) Estimate the true average sale price for all warehouses having a truss height of 25 ft, and do so in a way that conveys information about the precision of estimation. (Use a 95% CI. Round your answers to three decimal places.) (c) Predict the sale price for a single warehouse whose truss height is 25 ft, and do so in a way that conveys information about the precision of prediction. (Use a 95% PI. Round your answers to three decimal places.) How does this prediction compare to the estimate of (b)? The prediction interval is ---Select--the confidence interval in part (b). (d) Without calculating any intervals, how would the width of a 95% prediction interval for sale price when truss height is 25 ft compare to the width of a 95% interval when height is 30 ft? Explain your reasoning. Since 25 is ---Select-- the mean than 30, a PI at 30 would be ---Select--- than the PI at 25.
Transcribed Image Text:The appraisal of a warehouse can appear straightforward compared to other appraisal assignments. A warehouse appraisal involves comparing a building that is primarily an open shell to other such buildings. However, there are still a number of warehouse attributes that are plausibly related to appraised value. Consider the accompanying data on truss height (ft), which determines how high stored goods can be stacked, and sale price ($) per square foot. Height 12 14 15 16 14 Price 35.51 37.84 36.88 39.99 37.98 37.51 15 26 28 Height 24 26 27 30 33 36 Price 46.19 50.33 49.11 48.09 50.92 54.76 54.34 57.16 57.45 18 40.99 dollars per square foot 22 48.51 47.01 47.52 30 22 dollars per square foot (a) Estimate the true average change in sale price associated with a one-foot increase in truss height, and do so in a way that conveys information about the precision of estimation. (Use a 95% CI. Round your answers to three decimal places.) $ $ 24 (b) Estimate the true average sale price for all warehouses having a truss height of 25 ft, and do so in a way that conveys information about the precision of estimation. (Use a 95% CI. Round your answers to three decimal places.) (c) Predict the sale price for a single warehouse whose truss height is 25 ft, and do so in a way that conveys information about the precision of prediction. (Use a 95% PI. Round your answers to three decimal places.) How does this prediction compare to the estimate of (b)? The prediction interval is ---Select--the confidence interval in part (b). (d) Without calculating any intervals, how would the width of a 95% prediction interval for sale price when truss height is 25 ft compare to the width of a 95% interval when height is 30 ft? Explain your reasoning. Since 25 is ---Select-- the mean than 30, a PI at 30 would be ---Select--- than the PI at 25.
(d) Without calculating any intervals, how would the width of a 95% prediction interval for sale price when truss height is 25 ft compare to the width of a 95% interval when height is 30 ft? Explain your reasoning. Since 25 is ---Select--- the mean than 30, a PI at 30 would be ---Select--than the PI at 25. (e) Calculate the sample correlation coefficient. (Round your answer to three decimal places.) Interpret the sample correlation coefficient. There is a ---Select--- correlation between the variables. You may need to use the appropriate table in the Appendix of Tables to answer this question.
Transcribed Image Text:(d) Without calculating any intervals, how would the width of a 95% prediction interval for sale price when truss height is 25 ft compare to the width of a 95% interval when height is 30 ft? Explain your reasoning. Since 25 is ---Select--- the mean than 30, a PI at 30 would be ---Select--than the PI at 25. (e) Calculate the sample correlation coefficient. (Round your answer to three decimal places.) Interpret the sample correlation coefficient. There is a ---Select--- correlation between the variables. You may need to use the appropriate table in the Appendix of Tables to answer this question.
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