3 An efficiency expert wants to determine whether the time an assembly worker requires to perform astandard task might be related to (1) the number of years on the job and (2) the score on a pre-employment aptitude test. Please analyze the regression below and answer the following questions:AВEFG16 SUMMARY OUTPUTRegression Statistics1718 Multiple R19 R Square20 Adjusted R Square21 Standard Error22 Observations0.73410.53890.44672.8624132324 ANOVA25dfSSMSFSignificance F26 Regression27 Residual28 Total295.75747.8795.8430.0211081.9358.19412177.69229Coefficients103.8529Upper 95%141.04530Standard Errort StatP-valueLower 95%66.66131 Intercept32 Years33 Score16.69196.22170.000-0.8966-3.06230.3910.428-0.185-0.28840.3216-1.005-0.67920.22180.012-1.173а).Identify and interpret bo, bi, and b2.bọ :Interpretation:bịInterpretation:b2 : Interpretation:Identify the coefficient of multiple determination, R².b).Interpret the value.с).Predict the time an assembly worker performs a task based on 5 years on the joband a score of 240 on the pre-employment aptitude test.d).For an assembly worker who performs a task with 5 years of experience and ascore of 240 on the aptitude test from above, what is the approximate 95% confidence interval fortheir average time performing a task? Please explain the significance of this confidence interval inexplaining for time required to perform a task.

Note: "Since you have posted a question with multiple sub -parts, we will solve first three sub parts for you. To get remaining subpart solved please re post the complete question and mention the sub parts to be solved". The multiple regression equation having one dependent variable and more than one independent variables. The multiple regression equation line is defined as below y= bo + b1*x1 + b2*x2 + b3*x3 ------+ bn*xn Where bo is intercept of the equation b1, b2, b3 ,-----bn are slope coefficients of equation y is dependent variable x1, x2, x3-----xn are independent variables Attached output is, From the above table we have coefficients bo = intercept = 103.8529 b1 = slope coefficient of years = -0.2884 b2 = slope coefficient of scores = -0.6792 Thus, coefficients are bo = 103.8529, b1 = -0.2884 & b2 = -0.6792 Interpretation of bo : The intercept of 103.8529 reveals that, we can expect that on average time an assembly worker required is 103.8529. when the number of years on the job is zero '0' and as well as pre employment aptitude test score is zero '0' Interpretation of b1 : Slope coefficient of years is -0.2884 and it explains that as increasing the number of years on the job by 1 year then time an assembly worker required would be decrease by 0.2884 Interpretation of b2 : Slope coefficient of pre employment aptitude test score is -0.6792 and it describes that as increasing the pre employment aptitude test score by score 1 then the time an assembly worker required would be decrease by 0.6792. Generally, Coefficient of determination is denoted with R2 and it interprets that how much % of variation in y variable can be explained by the regression model. Output is : From the output, the multiple coefficient of determination R2 is 0.5389 Interpretation : The multiple coefficient of determination R2 is 0.5389 and it explains that 53.89% of the variation in the time an assembly worker can be explained by the variation in the number of years on the job & pre employment aptitude test score. The coefficients are bo = 103.8529, b1 = -0.2884 & b2 = -0.6792 With these coefficients regression equation is y = 103.8529 - 0.2884*x1 - 0.6792*x2 Predict the time an assembly worker if x1 = 5 years & x2 = 240 Keep the values x1 = 5 years , x2 = 240 in the above regression line y = 103.8529 - 0.2884*x1 - 0.6792*x2 = 103.8529 - 0.2884*5 - 0.6792*240 = 103.8529 - 1.442 - 163.008 = -60.597 ~ -61 Therefore, the Predicted time an assembly work is -61