A statistical program is recommended. Sherry is a production manager for a small manufacturing shop and is interested in developing a predictive model to estimate the time to produce an order of a given size—that is, the total time to produce a certain quantity of the product. Suppose she has collected data in the following table on the total time (in minutes) to produce 30 different orders of various quantities. Quantity Total Time (minutes) 105 172 125 189 135 221 141 323 149 248 171 319 190 372 204 185 206 250 240 177 255 397 277 227 299 228 335 367 371 490 Quantity Total Time (minutes) 388 351 392 428 400 412 421 545 439 443 439 320 455 589 458 483 480 511 486 423 493 403 506 700 586 591 589 458 665 643 #1) Develop the estimated regression equation. (Let x = quantity, and let y = total time (in minutes). Round your numerical values to four decimal places.) ŷ = #2) Find the value of the test statistic (round to two decimal places). Find the p-value (round to three decimal places). #3) Did the estimated regression equation provide a good fit? (Round your numerical answer to four decimal places.) Since r2 =_______. is (less than OR at least .55), the estimated regression equation (did not provide OR did provide) a good fit.
A statistical program is recommended. Sherry is a production manager for a small manufacturing shop and is interested in developing a predictive model to estimate the time to produce an order of a given size—that is, the total time to produce a certain quantity of the product. Suppose she has collected data in the following table on the total time (in minutes) to produce 30 different orders of various quantities. Quantity Total Time (minutes) 105 172 125 189 135 221 141 323 149 248 171 319 190 372 204 185 206 250 240 177 255 397 277 227 299 228 335 367 371 490 Quantity Total Time (minutes) 388 351 392 428 400 412 421 545 439 443 439 320 455 589 458 483 480 511 486 423 493 403 506 700 586 591 589 458 665 643 #1) Develop the estimated regression equation. (Let x = quantity, and let y = total time (in minutes). Round your numerical values to four decimal places.) ŷ = #2) Find the value of the test statistic (round to two decimal places). Find the p-value (round to three decimal places). #3) Did the estimated regression equation provide a good fit? (Round your numerical answer to four decimal places.) Since r2 =_______. is (less than OR at least .55), the estimated regression equation (did not provide OR did provide) a good fit.
Linear Algebra: A Modern Introduction
4th Edition
ISBN:9781285463247
Author:David Poole
Publisher:David Poole
Chapter4: Eigenvalues And Eigenvectors
Section4.6: Applications And The Perron-frobenius Theorem
Problem 22EQ
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A statistical program is recommended.
Sherry is a production manager for a small manufacturing shop and is interested in developing a predictive model to estimate the time to produce an order of a given size—that is, the total time to produce a certain quantity of the product. Suppose she has collected data in the following table on the total time (in minutes) to produce 30 different orders of various quantities.
Quantity | Total Time (minutes) |
---|---|
105 | 172 |
125 | 189 |
135 | 221 |
141 | 323 |
149 | 248 |
171 | 319 |
190 | 372 |
204 | 185 |
206 | 250 |
240 | 177 |
255 | 397 |
277 | 227 |
299 | 228 |
335 | 367 |
371 | 490 |
Quantity | Total Time (minutes) |
---|---|
388 | 351 |
392 | 428 |
400 | 412 |
421 | 545 |
439 | 443 |
439 | 320 |
455 | 589 |
458 | 483 |
480 | 511 |
486 | 423 |
493 | 403 |
506 | 700 |
586 | 591 |
589 | 458 |
665 | 643 |
#1) Develop the estimated regression equation. (Let x = quantity, and let y = total time (in minutes). Round your numerical values to four decimal places.) ŷ =
#2) Find the value of the test statistic (round to two decimal places). Find the p-value (round to three decimal places).
#3) Did the estimated regression equation provide a good fit? (Round your numerical answer to four decimal places.) Since r2 =_______. is (less than OR at least .55), the estimated regression equation (did not provide OR did provide) a good fit.
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