Question

1.    Fully interpret the meaning of the coefficient on gender, x3   

2.    Predict the annual income for a female aged 45 with 10 years of education. How much would the predicted income have changed for a male.

3. Plot the standardized residuals against predicted income, from regression in part (a). Check for outliers and explain whether the residual plot supports the assumptions about Ɛ. What is your conclusion? Submit the graph to earn full point

EDUCATION AGE GENDER INCOME (in $1000) 12 60 female 6.5 16 39 male 120 16 33 female 21.75 12 51 male 82.5 16 42 female 55 14 20 male 7.5 14 57 male 37.5 13 61 female 5.5 16 31 male 9 12 30 male 37.5 14 68 female 13.75 16 50 male 32.5 12 27 male 0.5 16 30 male 55 18 65 female 55 19 36 male 67.5 Please show all work in excel

Answer 1

KIR Filt Home Imert Page Layout Formulas Data Review View Connections Properties Refresh + Sort All - Edit Links Connections LALLA From From From From Other Access WebText Sources Get External Data Data Ana Existing Connections Clear Reapply Filter Advanced otter o wer - Hide Detail Group Ungroup Subtotal E P Text to Remove Data Consolidate What Columns Duplicates Validation Analysis Data Tools Outline D C NOP AB 1 SUMMARY OUTPUT education Residual Plot gender Residual Pla 3 Regression Statistics 4 Multiple R 0.546307 5 RSquare 0.298452 6 Adjusted R Square 0.123065 7 Standard Error 31.10579 8 Observations 16 Residuals Besidual 2 0.4 0.6 0 Bender 10 ANOVA education df 12 Regression 13 Residual 14 Total 3 12 15 SS MS F gnificance! 4939.467491 1646.489 1.701674 0.219565 11610.84501 9675704 16550.3125 Normal Probability Plot age Residua 16 Coefficient: Standard Error Stat P value Lower 95%Upper 95%ower 95.0%pper 95.0% -104.824 69.22974347 1.51414 0.155876 -255.662 46.01514 -255.662 46.01514 6.385254 3.66078561 1.744231 0.106653 -1.59091 14.36142 1.59091 14.36142 0.66308 0.655355037 1.011788 0.331506 0.76482 2.090976 -0.76482 2.090976 31.30999 19.86094981.57645 0.140903 11.9633 74.58328 -11.9633 74.58328 17 Intercept 18 education 19 age 20 gender 100 50 Residents O 0 20 100 -20 120 40 60 80 Sample Percentile 60 PROBABILITY OUTPUT Sheets 24 RESIDUAL OUTPUT Sheet4 Sheet1 Ready Sheet2 Sheet3 Sheet6 0 38% ENG 12 PM IN 11-2009 D O E H B = 8 Type here to search 9 Wero DOLBY

1 Dato: Page No.: Got data dota into exiel data analysis then click Regression on 20K Regression - Select data The coefficorut of gender = 31.30 for male annual income increased by 31.30 units but for female annual in breasy by ounits while other coefficicuts kept constants income = -104,824 + 6.38 (10 + 0.66 (45) + 0.31.30 0) intime How for male income = -104.824 + 6.38 (10) + 0.66 (45) +31.30 W) lincome = 19.976. 37 While fitting mode click on Residual plot, Here residual plot is hot rondom so linear regression is not appropriate but bormal prob plot showes hormality is satisfied. Here puolue for each coefficient is greater than 0.05, hence that is non-fignificant same for overal significance