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Could you find a regression equation that would be acceptable as a statistically significant and yet...

Could you find a regression equation that would be acceptable as a statistically significant and yet offer no acceptable interpretational value to management? Detail the variables associated with the regression equation and explain how it is statistically significant. **I have come to the conclusion that yes, it is possible. But i am having trouble finding a data set to relate this to in addition to detailing the variables associated with the regression equation. Thanks!

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Regression Analysis: y versus X1, X2 The regression equation is y 2013.4 X1 + 244 X2 Predictor Constant X1 X2 Coef SE Coef -20.4 13.350 243.71 652.70.03 0.976 7.672 1.7ซื้ 0.107 63.51 3.84 0.002 S 547.737 R-Sq 58.2% R-Sq (adj) -51.3% # Analysis of Variance Source Regression Residual Error 12 3600196 300016 Total DE MS 2 5018232 2509116 8.36 0.005 14 8618428 Source DE Seg sS X1 X2 1 600498 1 4417734 Unusual Observations Obs X1 y Fit SE Fit Residual St Resid 4 18.0 1552 2657 205 -1105 -2.18R R denotes an observation with a large standardized residual.The first-order model for m ean annual earnings, E(v), as a function of age (%) and hours worked (%) is,From the above 1 TTAB software output, the least square regression equati on (or first or der model) is, y--20.4+13.350, +243.71x2The coefficients are, 20.1 When x1(age) and x2 (hours worked) is zero, then E()-20.4, which means that the estimated mean annual earnings will be -20.4. A 13.35 A 243.71 The mean annual eanings will be increases 13.35 times per number of years increases and 243.71 times increases per num ber of hours increases.

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