Sam Lender, a mortgage loan manager, is studying the relationship of applicant Income in thousands of dollars) to home value in thousands of dollars), Education, Age, current monthly mortgage Payment (in dollars), and Gender. Sam uses the indicator variable Female to represent Gender (a value of 1 indicates a gender of female). Sam also adds the Value*Female interaction term. Sam fits a multiple regression model to the data using Income as the Dependent or (V) variable and the other five variables and the interaction term as independent or (X) variables. A portion of the data and the excel output appear below. The complete dataset and excel output (labeled Full Model) appear in the Sam Lender worksheet of the Quiz S Moodle data workbook MUR sure MS Standard Error Observations 0.8825 0.7784 0.000 05711 V 403 10.56 4.69152E OS Psal 119 119 Pay Care Tende T 230 M 370 397 Mode 0 181 Made 378 Fem 1 104 For 2015 Mi 151 Feb 1 370 Formule 1 135 Tende 332 MB 217 Na Ge Income Vie E n 190 39.6 161 403 161 40 381 114 40 7 20 1 4 49 408 1111337 37 1 30 43 399 181 14 404 143 54 t Stoward Error 1.511 Stut 25 44 20 re Vale Taxat 1.41 1 MIF 15 0.17 0112 0.023 0012 18 0.14 Pay Female FemaleValue 001 LE OM 0.002 15 3.37 2.14 Using the regression model above, predict the Income (in thousands of dollars) for a 50 year-old Male mortgage loan applicant whose home has a value of 150 (in thousands of dollars), has an education level of 14 and has a current monthly mortgage payment of $500. When making the prediction use regression model coefficients rounded to 3 decimals (as shown in the above Excel output) and round your prediction to 2 decimal places