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DQ Module 4

# DQ Module 4 - Module 4 DQ Run a multiple regression for...

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Interpret the results. Post your regression results and your regression results in the DQ Forum. Month MachHrs ProdRuns Overhead 1 1539 31 99798 2 1284 29 87804 Interpretation 3 1490 27 93681 4 1355 22 82262 5 1500 35 106968 6 1777 30 107925 7 1716 41 117287 8 1045 29 76868 9 1364 47 106001 10 1516 21 88738 11 1623 37 105830 12 1376 37 88730 13 1327 49 100624 14 1178 50 98857 15 1491 37 102622 16 1667 41 108059 17 1769 34 110054 18 1104 44 91892 19 1196 46 98693 20 1794 29 110530 21 1379 38 96883 22 1448 32 99593 23 1505 32 94564 24 1420 42 105752 25 1475 27 93224 26 1118 34 75398 27 1433 58 113137 28 1589 26 85609 29 1585 32 98498 30 1493 33 101803 31 1124 36 88371 32 1536 28 102419 33 1678 41 117183 34 1723 35 107828 35 1413 30 88032 36 1390 54 117943 Module 4 DQ Run a multiple regression for Example 11.2 in the Albright et al. text using StatTools or Excel's regression option. As we are taught in our book R 2 value is one of the most frequently quoted values from a regression. So from that standpoint lets break down the interpretation from there. We understand from our readings that R2 measures the goodness of a linear fit, and ideally we would like it close to 1. So the fact that R2 here is .86, is good. In other words the single explanatory variable is able to explain only 86% of the variation in the Sales variable. Leaving only

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