The data table available below gives the annual cost per square foot of 50 commercial leases for office space in a city and the reciprocal of the number of square feet. Use the cost per square foot as the response variable and the reciprocal of the number of square feet as the explanatory variable to complete parts (a) through (c). (a) How many leases lie outside the 95% prediction intervals for leases of their size? Does the location regression?) these data indicate a problem with the fitted model? (Hint: Are all of these residuals on the same side, positive or negative, of the There are leases that lie outside the 95% prediction intervals. (Type a whole number.) Does this indicate a problem with the fitted model? O A. Yes, all of the residuals for these data are negative. O B. Yes, all of the residuals for these data are positive. OC. No, none of the leases lie outside the 95% prediction intervals. O D. No, the residuals for these data are evenly divided between positive and negative. (b) Given the context of the problem (costs of leasing commercial property), list several possible lurking variables that might be responsible for the size and position of leases with large residual costs. Which of the following are possible lurking variables? Select all that apply. O A. The condition of the building O B. The location of the city Oc. The type of business OD. The type of building OE. The age of the building OF. The location of the building within the city O G. The economic conditions in the city (c) The leases with the four largest residuals have something in common. What is it, and does it help you identify a lurking variable? What do the four leases with the largest residuals in terms of absolute value have in common? O A. The x-values for these leases are near the minimum x-value. O B. The x-values for these leases are near the maximum x-value. O c. The x-values for these leases are near the average x-value. O D. The x-values for these leases are the minimum and the maximum x-values. Does this help identify a lurking variable? v the prediction intervals are V for these x-values.
The data table available below gives the annual cost per square foot of 50 commercial leases for office space in a city and the reciprocal of the number of square feet. Use the cost per square foot as the response variable and the reciprocal of the number of square feet as the explanatory variable to complete parts (a) through (c). (a) How many leases lie outside the 95% prediction intervals for leases of their size? Does the location regression?) these data indicate a problem with the fitted model? (Hint: Are all of these residuals on the same side, positive or negative, of the There are leases that lie outside the 95% prediction intervals. (Type a whole number.) Does this indicate a problem with the fitted model? O A. Yes, all of the residuals for these data are negative. O B. Yes, all of the residuals for these data are positive. OC. No, none of the leases lie outside the 95% prediction intervals. O D. No, the residuals for these data are evenly divided between positive and negative. (b) Given the context of the problem (costs of leasing commercial property), list several possible lurking variables that might be responsible for the size and position of leases with large residual costs. Which of the following are possible lurking variables? Select all that apply. O A. The condition of the building O B. The location of the city Oc. The type of business OD. The type of building OE. The age of the building OF. The location of the building within the city O G. The economic conditions in the city (c) The leases with the four largest residuals have something in common. What is it, and does it help you identify a lurking variable? What do the four leases with the largest residuals in terms of absolute value have in common? O A. The x-values for these leases are near the minimum x-value. O B. The x-values for these leases are near the maximum x-value. O c. The x-values for these leases are near the average x-value. O D. The x-values for these leases are the minimum and the maximum x-values. Does this help identify a lurking variable? v the prediction intervals are V for these x-values.
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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Transcribed Image Text:The data table available below gives the annual cost per square foot of 50 commercial leases for office space in a city and the reciprocal of the number of square feet. Use the cost per square foot as the response variable and the reciprocal
of the number of square feet as the explanatory variable
complete parts (a) through (c).
(a) How many leases lie outside the 95% prediction intervals for leases of their size? Does the location of these data indicate a problem with the fitted model? (Hint: Are all of these residuals on the same side, positive or negative, of the
regression?)
There are leases that lie outside the 95% prediction intervals.
(Type a whole number.)
Does this indicate a problem with the fitted model?
O A. Yes, all of the residuals for these data are negative.
O B. Yes, all of the residuals for these data are positive.
O C. No, none of the leases lie outside the 95% prediction intervals.
O D. No, the residuals for these data are evenly divided between positive and negative.
(b) Given the context of the problem (costs of leasing commercial property), list several possible lurking variables that might be responsible for the size and position of leases with large residual costs.
Which of the following are possible lurking variables? Select all that apply.
O A. The condition of the building
O B. The location of the city
O c. The type of business
O D. The type of building
O E. The age of the building
O F. The location of the building within the city
O G. The economic conditions in the city
(c) The leases with the four largest residuals have something in common. What is it, and does it help you identify a lurking variable?
What do the four leases with the largest residuals in terms of absolute value have in common?
O A. The x-values for these leases are near the minimum x-value.
O B. The x-values for these leases are near the maximum x-value.
O C. The x-values for these leases are near the average x-value.
O D. The x-values for these leases are the minimum and the maximum x-values.
Does this help identify a lurking variable?
V the prediction intervals are
V for these x-values.
Transcribed Image Text:Cost and area data
TIT
Cost
1
Cost
Square Foot
15.177
Number of Square Feet
0.00023274
Square Foot
16.339
Number of Square Feet
0.00030662
15.322
0.00017586
16.823
0.00021664
14.534
0.00001199
17.388
0.00049304
0.00032233
15.343
0.00017042
15.461
14.693
0.00007745
0.00014383
16.489
0.00008444
16.767
14.526
0.00030256
15.877
0.00011262
15.126
0.00019374
15.284
0.00023278
14.931
0.00012477
14.656
0.00037358
16.672
0.00033635
0.00031089
17.317
0.00033186
15.864
15.447
0.00004029
16.071
0.00022382
0.00005874
16.166
0.00001847
15.086
16.337
0.00006119
16.001
0.00008716
0.00038027
19.058
0.00034061
18.911
14.731
0.00008316
15.425
0.00031117
16.071
0.00019216
17.066
0.00038725
16.363
0.00035243
14.386
0.00002919
16.388
0.00013243
16.084
0.00040724
15.933
0.00018328
17.031
0.00049644
15.126
0.00022587
0.00020026
15.146
18.111
14.863
14.497
0.00014203
0.00049998
0.00003893
0.00005546
0.00023546
16.288
16.059
0.00006475
16.843
14.983
0.00003124
15.631
0.00008181
15.934
0.00012788
14.494
0.00003439
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