OLS linear regression. The results of an OLS linear regression analysis are below. The predictor variable used is the quantity produced and sold. The explanatory power of this variable to explain changes in the size of the indirect manufacturing cost pool is approximately 38%. This exceeds the threshold value for 7” for a reliable relationship of 30%. As a management accountant familiar with OLS linear regression modelling, you are uneasy with the results.

REQUIRED 1. What output causes your unease?
2. What step did you miss, and what should you do at this point?LO1

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Observation Cumulative Sales and Production Quantity Sold and Produced Indirect Manufacturing Cost Pool Regression Statistics 1 198 198 $277.20 0.383655 2 398 200 196.00 adjusted 0.357974 3 792 592 406.11 standard error 84.16542 4. 1,153 561 384.85 observations 26 5 1,317 164 112.50 6 1,584 267 128.21 Coefficients Value 7 2,296 712 341.90 8 2,816 520 249.70 intercept a 36.48440218 9 t-statistica 1.005959215 2,933 117 56.18 10 3.168 p-value a 0.324470437 235 78.99 11 3,852 684 229.92 12 4,348 496 166.73 slope b 0.304644864 13 4,551 203 t-statistic b 3.865131137 68.24 14 4,812 261 87.73 p-value b 0.000740362 15 5,515 703 236.31 16 6,091 576 193.62 17 6,336 245 57.65 18 6,587 251 59.06 19 7,274 687 161.65 20 7,846 572 134.59 21 8,102 256 60.24 22 8,303 201 47.29 23 9,026 723 170.12 24 9,709 683 160.71 25 10,001 292 68.71 26 10,255 254 59.77