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Key - Lab 8 - Multiple Regression

# Key - Lab 8 - Multiple Regression - year 1971 1971 1972...

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Unformatted text preview: year 1971 1971 1972 1972 1972 1972 1973 1973 1973 1973 1974 1974 1974 1974 1975 1975 Means: quarter 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 sales 11484 9348 8429 10079 9240 8862 6216 8253 8038 7476 5911 7950 6134 5868 3160 5872 7645 prose 2.260 2.540 3.070 2.910 2.730 2.770 3.590 3.230 2.600 2.890 3.770 3.640 2.820 2.960 4.240 3.690 \$3.11 pcarn 3.490 2.850 4.060 3.640 3.210 3.660 3.760 3.490 3.130 3.200 3.650 3.600 2.940 3.120 3.580 3.530 \$3.43 dinc 158.110 173.360 165.260 172.920 178.460 198.620 186.280 188.980 180.490 183.330 181.870 185.000 184.000 188.200 175.670 188.000 \$180.53 trend 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 sales sales prose pcarn dinc 1 -0.784 -0.023 -0.413 prose 1 0.472 0.289 pcarn dinc 1 -0.104 Price (own) -1.47 1 Income Price (sub) -0.455 1.18 Elasticity of Demand A 1% increase in the price of roses, causes a 1.47% decrease in the quantity of roses demanded, holding constant the price of carnations and weekly disposable income. A 1% increase in the price of carnations causes a 1.18% increase in the demand for roses, holding the price of roses and weekly disposable income constant. The tests should all critical value in Exce that Excel wants the probability). So, I ent Next, we had 16 obs population paramete are (n-4) = 12. The tests should all critical value in Exce that Excel wants the probability). So, I ent Next, we had 16 obs population paramete are (n-4) = 12. SUMMARY OUTPUT Regression Statistics Multiple R 0.78 R Square 0.61 Adjusted R Square 0.59 Standard Error 1312.19 Observations 16 ANOVA df Regression Residual Total 1 14 15 SS 38490399.31 24105956.69 62596356 MS ### ### F 22.35 Significance F 0 Intercept prose SUMMARY OUTPUT Coefficients Standard Error 16898.97 1984.57 -2978.55 629.98 t Stat 8.52 -4.73 P-value 0 0 Lower 95% 12642.49 -4329.72 Regression Statistics Multiple R 0.88 R Square 0.78 Adjusted R Square 0.72 Standard Error 1076.29 Observations 16 ANOVA df Regression Residual Total 3 12 15 SS 48695526.34 13900829.66 62596356 MS ### ### F 14.01 Significance F 0 Intercept prose pcarn dinc Coefficients Standard Error 13354.6 6485.42 -3628.19 635.63 2633.75 1012.64 -19.25 30.69 t Stat 2.06 -5.71 2.6 -0.63 P-value 0.06 0 0.02 0.54 Lower 95% -775.92 -5013.1 427.41 -86.13 t-critical (0.05, 12) 1.782 The tests should all be one-tail tests. To get the critical value in Excel for one-tail tests, remember that Excel wants the area in both tails (the probability). So, I entered 0.10 for the probability. Next, we had 16 observations and estimated 4 population parameters, so our degrees of freedom are (n-4) = 12. The tests should all be one-tail tests. To get the critical value in Excel for one-tail tests, remember that Excel wants the area in both tails (the probability). So, I entered 0.10 for the probability. Next, we had 16 observations and estimated 4 population parameters, so our degrees of freedom are (n-4) = 12. Upper 95% 21155.44 -1627.37 Lower 90.0% 13403.53 -4088.13 Upper 90.0% 20394.41 -1868.96 Upper 95% 27485.12 -2243.27 4840.1 47.62 Lower 90.0% 1795.72 -4761.06 828.94 -73.96 Upper 90.0% 24913.49 -2495.31 4438.56 35.45 ...
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