\fSUMMARY OUTPUT Dependent Variable is Private Average Salary Regression Statistics Multiple R 0.717856406 R Square 0.51531782 Adjusted R Square 0.513771784 Standard Error 18003.918 Observations 630 ANOVA of SS MS F Significance F Regression 2 2.16083E+11 1.08041+11 333.3156 2.4592E-99 Residual 627 2.03236E+11 324141063.4 Total 629 4.19319E+11 Coefficients Standard Error t Stat P-value Lower 95%% Upper 95% Intercept 32509.27959 1099.478586 29.56790609 5.6E-121 30350.17345 34668.38573 Bachelors 428.3850599 47.35743019 9.045783486 1.84E-18 335.3866881 521.3834318 MastersPlus 730.3663972 38.16218687 19.13848385 1.2E-64 655.4252268 805.3075676 You need to replicate the above result as your first regression result included in your report. You also need to clearly explain the key findings in the regression summary output. Your analysis of should include explanations of:R square and Adjusted R Square Meaning of F-statistic and its Signicance level Meaning of the t-Statistics and their P-values Interpretation of the Coefcients on the explanatory variables and their 95% condence intervals Meaning of the Standard Error of the regression and its use in forecasting a value for dependent variable Private Average Salary given assumed values for the explanatory variables. Use either the average or median values for your explanatory x variables when predicting a value for Private Average Salary [found in data set}. For the skill va ria bles. just use a value of 5D since these variables are set to go from I] to 10:1 NEIT, you will review the 35 listed skills in the data set and select four skills that you believe are likely to be the most rewarded in the private sector labor market. Forthese skills, you expect that a rise in the importance of the skill for an occupation is associated with a rise in the private ave rage salary. 1ultl'rite a clear but concise explanation of why your team selected these four skills as the skills most likely to receive higher pay in the private sector. IMPORTANT: do NOT first run regressions looking for those skills with the largest coefficients. 'rour grade does not depend upon correctly guessing which skills show the largest impact. Just make your guesses and explain your reasoning. You will run four different regressions. Each regression will have three right-side explanatory variables: the Bachelors and Maste rsPlus variables along with one of your four selected skill variables [placed in column G]. Suppose you had selected Active Learning as one of your skill variables, if so your regression output would be: SUMNER? OUTPUT Dependent 1ill'ariable is Private Average Salaryr Regression Statistics Multiple R 034103549 n can\". n uo-n-zm ECON 634 AAUP Aug/Sep 2021 1at Regression Assignment Regression number 1 Regression Statistics Multiple R 0.717856406 R Square 0.51531782 Adjusted R Square 0.513771784 Standard Error 18003.918 Observations 630 ANOVA Significance df SS MS F F Regression 2 2.16083E+11 1.08E+11 333.3156 2.4592E-99 Residual 527 2.03236E+11 3.24E+08 Total 629 4.19319E+11 Standard Lower Coefficients Error t Stat P-value Lower 95% Upper 95% 95.0% Upper 95.0% Intercept 32509.27959 1099.478586 29.56791 5.6E-121 30350.17335 34668.38583 30350.1733 34668.38583 Bachelor 428.3850599 47.35743019 9.045783 1.84E-18 335.3866838 521.3834361 335.386684 521.3834361 Master 730.3663972 38.16218687 19.13848 1.2E-64 655.4252233 805.3075711 655.425223 805.3075711 R square and Adjusted R Square : is used to measure the goodness of fit Meaning of F-statistic and its Significance level : numbers used to testing Ho=a+b are both=0 Meaning of the t-Statistics and their P-values : Is used for testing hypothesis test a=0 or b=0Regression Statistics Multiple R 0.717856406 R Square 0.51531782 Adjusted R Square 0.513771784 Standard Error 18003.918 Observations 630 ANOVA Significance df SS MS F F Regression 2 2.16083E+11 1.08E+11 333.3156 2.4592E-99 Residual 627 2.03236E+11 3.248+08 Total 629 4.19319E+11 Standard Lower Coefficients Error t Stat P-value Lower 95% Upper 95% 95.0% Upper 95.0% Intercept 32509.27959 1099.478586 29.56791 5.6E-121 30350.17335 34668.38583 30350.1733 34668.38583 Bachelor 428.3850599 47.35743019 9.045783 1.84E-18 335.3866838 521.3834361 335.386684 521.3834361 Master 730.3663972 38.16218687 19.13848 1.2E-64 655.4252233 805.3075711 655.425223 805.3075711 R square and Adjusted R Square : is used to measure the goodness of fit Meaning of F-statistic and its Significance level : numbers used to testing Ho=a+b are both=0 Meaning of the t-Statistics and their P-values : Is used for testing hypothesis test a=0 or b=0 Interpretation of the Coefficients on the explanatory variables and their 95% confidence intervals Meaning of the Standard Error of the regression and its use in forecasting a value for dependent variable : used for forecasting Private Average Salary given assumed values for the explanatory variables.SUMMARY OUTPUT Regression Statistics Multiple R 0.740782 R Square 0.548759 Adjusted R Square 0.546596 Standard Error 17385.59 Observations 630 ANOVA Significance f SS MS F F Regression 3 2.3E+11 7.67E+10 253.7614 1E-107 Residual 626 1.89E+11 3.02E+08 Total 629 4.19E+11 Standard Upper Lower Coefficients Error t Stat P-value Lower 95% 95% 95.0% Upper 95.0% Intercept 27364.49 1302.994 21.00124 1.58E-74 24805.72 29923.26 24805.72 29923.25974 Bachelor 286.305 50.26388 5.696039 1.89E-08 187.5988 385.0113 187.5988 385.0112676 Master 660.8949 38.23702 17.28417 5.14E-55 585.8066 735.9833 585.8066 735.9832988 time management 192.9099 28.3226 6.811165 2.28E-11 137.2911 248.5288 137.2911 248.528768You will run four different regressions. Each regression will have three right-side explanatory variables: the Bachelors and MastersPlus variables along with one of your four selected skill variables (placed in column G). Suppose you had selected Active Learning as one of your skill variables, if so your regression output would be: SUMMARY OUTPUT Dependent Variable is Private Average Salary Regression Statistics Multiple R 0.74103649 R Square 0.54913508 Adjusted R Square 0.546974386 Standard Error 17378.34147 Observations 630 ANOVA of 55 MS F Significance F Regression 3 2.30263E+11 76754257428 254.1474879 7.7213E-108 Residual 626 1.89056E+11 302006752.3 Total 629 4.19319E+11 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 26774.26122 1351.59076 19.80944382 3.605E-68 24120.06044 29428.46201 Bachelors 237.4471559 53.53538396 4.435331146 1.08594E-05 132.316474 342.5778378 MastersPlus 586.5625366 42.39495032 13.83566987 3.53112E-38 503.3090003 669.816073 Active Learning 238.3642398 34.7862767 6.85224929 1.74318E-11 170.0523175 306.6761621 Please do not use Active Learning as one of your four selected skill variables. Thank You. For each of your four regressions that include a skill variable, you need to clearly explain the key findings in the regression summary output. Your analysis of should include explanations of: R square and Adjusted R Squareregression number 3 skill is : negotiation SUMMARY OUTPUT Regression Statistics Multiple R 0.722381 R Square 0.521834 Adjusted R Square 0.519542 Standard Error 17896.76 Observations 530 ANOVA Significance df SS MS F F Regression 3 2.19E+11 7.29E+10 227.7229 7.4E-100 Residual 626 2.01E+11 3.2E+08 Total 629 4.19E+11 Standard Upper Lower Upper Coefficients Error t Stat P-value Lower 95% 95% 95.0% 95.0% Intercept 30140.88 1360.898 22.14779 1.06E-80 27468.41 32813.36 27468.41 32813.36 Bachelor 360.1296 52.5569 6.852185 1.74E-11 256.9204 463.3387 256.9204 463.3387 Master 706.6966 38.79102 18.21804 8.08E-60 630.5203 782.8729 630.5203 782.8729 Negotiations 85.6873 29.33744 2.920749 0.003618 28.07558 143.299 28.07558 143.299Meaning of F-statistic and its Signicance level Meaning of the t'Statistics and their P-value-s Interpretation of the Coefcients on the explanatory variables and their 55% condence intervals Meaning of the Standard Error of the regression and its use in forecasting a value for dependent variable Private Average Salary given assumed values for the explanatory variables. Use either the average or median values for your explanatory 1 variables when predicting a value for Private Average Salary [found in data set}. For the skill variables. just use a value of 5D since these variables are set to go from I] to l. Once you have completed your analysis of each of the ve regressions you estimated. provide a summary that compares the regression results across the four regressions that each had a different skill variable. WhiCh skill variable associates most strongly. or weakly, with higher salaries? Is the overall t of the regression model notably bettery or worse, for one of the skill variables? In sum your entire report will include ve different regressions: your replication of the rst regression in this handout and your four separate regressions that added a skill to your initial regression. regression number 4 skill is : troubleshooting Regression Statistics Multiple R 0.744507 R Square 0.554291 Adjusted R Square 0.552155 Standard Error 17278.69 Observations 630 ANOVA Significance df SS MS F F Regression 3 2.32E+11 7.75E+10 259.5013 2.1E-109 Residual 626 1.87E+11 2.99E+08 Total 629 4.19E+11 Standard Upper Lower Upper Coefficients Error t Stat P-value Lower 95% 95% 95.0% 95.0% Intercept 20859.7 1895.451 11.00514 7.13E-26 17137.49 24581.92 17137.49 24581.92 Bachelor 538.0178 47.80442 11.25456 6.89E-27 444.1413 631.8942 444.1413 631.8942 Master 795.3243 37.66262 21.11708 3.79E-75 721.3639 869.2847 721.3639 869.2847 Troubleshooting 186.7146 25.23676 7.398517 4.45E-13 137.1557 236.2736 137.1557 236.2736Regression number 5 skill is: critical Thinking Regression Statistics Multiple R 0.769425 R Square 0.592015 Adjusted R Square 0.59006 Standard Error 16531.31 Observations 630 ANOVA Significance df SS MS F F Regression 3 2.48E+11 8.27E+10 302.7899 2.1E-121 Residual 626 1.71E+11 2.73E+08 Total 629 4.19E+11 Standard Upper Lower Upper Coefficients Error t Stat P-value Lower 95% 95% 95.0% 95.0% Intercept 24087.75 1273.518 18.91434 1.89E-63 21586.87 26588.64 21586.87 26588.64 Bachelor 155.8318 50.2204 3.102958 0.002002 57.21093 254.4526 57.21093 254.4526 Master 525.3777 39.81109 13.19677 2.93E-35 447.1983 603.5572 447.1983 603.5572 Critical Thinking 350.4856 32.30843 10.84811 3.05E-25 287.0395 413.9316 287.0395 413.9316Names: Amira Rantisi, Haya Assaf , Rasha Ibrahim , Sahar Abu Lehia most likely to receive higher pay in the private sector. Once you have completed your analysis of each of the five regressions you estimated, provide a summary that compares the regression results across the four regressions that each had a different skill variable. Which skill variable associates most strongly, or weakly, with higher salaries? Is the overall fit of the regression model notably better, or worse, for one of the skill variables