A Zambian Human Resources Expert estimated a multiple linear regression model to predict starting salary of employees in the banking industry based on gender (Male = 0, Female = 1), age (years), level of education (Number of Schooling Years) and previous working experience (years). The SPSS output is presented below: Model Summary Model R R Squared Adjusted R Squared Std. Error of the Estimate 1 0.699a 0.4886 0.485 2260.15287 a. Predictors (Constant, Previous working experience (years), Gender of employees, level of education (years), Age in years Model Sum of Squares df Mean Square F Sig. 1 Regression 2.292E9 4 5.731E8 112.188 0.000a Residual 2.396E9 469 5108291.011 Total 4.688E9 473 a. Predictors (Constant, Previous working experience (years), Gender of employees, level of education (years), Age in years b. Dependent Variable: Starting salary in years Page 6 of 6 Coefficients Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -2665.523 774.710 -3.441 0.001 Gender of employees -1525.618 242.679 -242 -6.287 0.000 Age in years 33.017 15.636 124 2.112 0.035 Level of education (years) 650.548 40.952 0.596 15.886 0.000 Previous working experience (years) 20.342 21.829 0.056 0.932 0.352 a Dependent Variable: Starting salary in years a) State the measurement scale of the variables: i. Gender ii. Level of education b) How many employees took part in the study c) Use the p-value rule to comment on the statistical significance of all the independent variables. d) Interpret the regression coefficient associated with level of education. e) Which variable has the greatest impact on starting salary? Justify your answer f) Calculate the coefficient of multiple determination and comment on its magnitude.