You were tasked to study the impact of advertising costs to the customer acquisition. To implement this, the quarterly data for the past 2 years was provided. Create a regression line that fits the data and justify if the said data fits the curve. Sales ($) Advertising Costs ($) Quarter Additional Customers Q12020 1,900 124 20,290 Q22020 2,100 105 41,000 Q32020 2,500 150 49,500 Q42020 3,000 300 80,000 Q12021 3,000 200 83,200 Q22021 2,800 220 120,000 Q32021 3,300 315 161,000 Q42021 3,300 320 189,000 Activate

You were tasked to study the impact of advertising costs to the customer acquisition. To implement this, the quarterly data for the past 2 years was provided. Create a regression line that fits the data and justify if the said data fits the curve. Sales ($) Advertising Costs ($) Quarter Additional Customers Q12020 1,900 124 20,290 Q22020 2,100 105 41,000 Q32020 2,500 150 49,500 Q42020 3,000 300 80,000 Q12021 3,000 200 83,200 Q22021 2,800 220 120,000 Q32021 3,300 315 161,000 Q42021 3,300 320 189,000 Activate

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter7: Production Economics

Section: Chapter Questions

Problem 1.3CE

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Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7339 R Square 0.5386 Adjusted R Square 0.5185 Standard Error 2137.5200 Observations 49 ANOVA SS df Regression 2 245,370,679.3850 122,685,339.6925 26.8517 MS F Significance F 1.9E-08 Total Residual 46 210,173,612.6150 48 455,544,292.0000 4,568,991.5786 Coefficients Standard Error Intercept Education (Years) 14290.37278 2350.8671 2,528.5819 338.1140 Experience (Years) 829.3167 392.5627 t Stat P-value 5.6515 0.000000961 9200.6014 6.9529 0.000000011 2.1126 0.040093183 Lower 95 % Upper 95% 19,380.1442 1670.2789 3031.4553 39.129 1619.5044 Step 2 of 2: How much would you expect your salary to increase if you had one more year of education?
Consider the following computer output of a multiple regression analysis relating annual salary to years of education and years of work experience. Regression Statistics Multiple R 0.7339 R Square 0.5386 Adjusted R Square 0.5185 Standard Error 2137.5200 Observations 49 ANOVA SS df Regression 2 245,370,679.3850 122,685,339.6925 26.8517 MS F Significance F 1.9E-08 Residual 46 210,173,612.6150 Total 48 455,544,292.0000 4,568,991.5786 Coefficients Standard Error Intercept Education (Years) 14290.37278 2350.8671 2,528.5819 338.1140 Experience (Years) 829.3167 392.5627 t Stat P-value 5.6515 0.000000961 6.9529 0.000000011 2.1126 0.040093183 Lower 95 % Upper 95 % 9200.6014 19,380.1442 1670.2789 3031.4553 39.129 1619.5044 Step 1 of 2: What would be your expected salary with no education and no experience?