In order to investigate the effect of capital investment (x1) and advertising expenditures (x2) on the...

 

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In order to investigate the effect of capital investment (x1) and advertising expenditures (x2) on the profit (y) of the medium-sized firms, the data is collected from 50 firms and a multiple linear regression model is fitted. You can find the MINITAB output and some of the graphs obtained from this model below. Comment on everything given. Regression Analysis: Profit versus Capital Investment; Advertising The regression equation is Profit = 3.21 + 0.0439 Capital Investment 0.0445 Advertising Predictor Соef SE Coef T P Constant 3.207 1.741 1.84 0.072 Capital Investment Advertising 0.043910 0.004579 9.59 0.000 -0.04451 0.02636 -1.69 0.098 S = 2.58009 R-Sq 66.3% R-Sq (adj) = 64.9% Analysis of Variance Source DF SS MS F Regression 616.68 308.34 46.32 0.000 Residual Error 47 312.87 6.66 Total 49 929.55 Residual Plots for Profit Normal Probability Plot Versus Fits 99 90 5- 50 10 1 -5 -5 4 8. 12 16 Residual Fitted Value Histogram Versus Order 10.0- 7.5- 5- 5.0- 2.5 0.0 -5 -4 -2 4 8. 1 5 10 15 20 25 30 35 40 45 50 Residual Observation Order Frequency Percent Residual In order to investigate the effect of capital investment (x1) and advertising expenditures (x2) on the profit (y) of the medium-sized firms, the data is collected from 50 firms and a multiple linear regression model is fitted. You can find the MINITAB output and some of the graphs obtained from this model below. Comment on everything given. Regression Analysis: Profit versus Capital Investment; Advertising The regression equation is Profit = 3.21 + 0.0439 Capital Investment 0.0445 Advertising Predictor Соef SE Coef T P Constant 3.207 1.741 1.84 0.072 Capital Investment Advertising 0.043910 0.004579 9.59 0.000 -0.04451 0.02636 -1.69 0.098 S = 2.58009 R-Sq 66.3% R-Sq (adj) = 64.9% Analysis of Variance Source DF SS MS F Regression 616.68 308.34 46.32 0.000 Residual Error 47 312.87 6.66 Total 49 929.55 Residual Plots for Profit Normal Probability Plot Versus Fits 99 90 5- 50 10 1 -5 -5 4 8. 12 16 Residual Fitted Value Histogram Versus Order 10.0- 7.5- 5- 5.0- 2.5 0.0 -5 -4 -2 4 8. 1 5 10 15 20 25 30 35 40 45 50 Residual Observation Order Frequency Percent Residual