For a sample of USA industries the following variables are recorded:
YOUTPUT is the total production of each industry in millions of dollars in constant prices.
WAGES corresponds to the total wages of the each industry in millions of dollars in constant prices.
KCAPITAL is the fixed capital of each industry in millions of dollars in constant prices. Labor is the total number of employees in each industry in thousands.
D1 is a dummy variable which takes the value of 1 when a manufacturing industry is technologically advanced and 0 otherwise.
D2 is a dummy variable which takes the value of 1 when a
manufacturing industry is profitable and 0 otherwise.
SUMMARY OUTPUT | ||||||||
Regression Statistics | ||||||||
Multiple R | 0,887697 | |||||||
R Square | 0,788006 | |||||||
Adjusted R Square | 0,785736 | |||||||
Standard Error | 14470,28 | |||||||
Observations | 473 | |||||||
ANOVA | ||||||||
df | SS | MS | F | Significance F | ||||
Regression | 5 | 3,63E+11 | 7,27E+10 | 347,1789 | 9,4E-155 | |||
Residual | 467 | 9,78E+10 | 2,09E+08 | |||||
Total | 472 | 4,61E+11 | ||||||
Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95,0% | Upper 95,0% | |
Intercept | 192,2124 | 1250,783 | 0,153674 | 0,877933 | -2265,65 | 2650,073 | -2265,65 | 2650,073 |
WAGES | 13,81387 | 0,913179 | 15,12723 | 2,33E-42 | 12,01942 | 15,60832 | 12,01942 | 15,60832 |
KCAPITAL | -0,33749 | 0,075055 | -4,49658 | 8,72E-06 | -0,48498 | -0,19 | -0,48498 | -0,19 |
Labor | -240,151 | 31,17609 | -7,70306 | 8,02E-14 | -301,414 | -178,888 | -301,414 | -178,888 |
D1 | -221,005 | 1391,105 | -0,15887 | 0,87384 | -2954,61 | 2512,595 | -2954,61 | 2512,595 |
D2 | 5838,63 | 1562,002 | 3,737914 | 0,000209 | 2769,207 | 8908,054 | 2769,207 | 8908,054 |
Based on the data described above, the main research question at hand is the following:
“How industrial production is affected by the level of labor, wages, capital, profitability and technology”
1. Having in mind the main research question, estimate a linear regression model including all the explanatory variables, using the least squares method.
2. Give the interpretation of the regression coefficients of the selected model.
3. Estimate a new model without the insignificant explanatory variables.
4. For the selected model in 3 calculate the coefficient of determination and give its interpretation in terms of the given research question.
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