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Show all Excel work, answer every parts of the question, and write necessary explanations. Have to use Excel to solve this numerical problem (project). The

Show all Excel work, answer every parts of the question, and write necessary explanations. Have to use Excel to solve this numerical problem (project).

The following table gives the data for per capita income in thousands of US dollars with the percentage of the labor force in Agriculture, the average years of schooling of the population over 25 years of age, and overall health index for 16 selected countries in 2018 (data modified for educational purpose). Develop multiple regression model for per capita income (dependent variable) using Excel and answer the questions below the table. Can use symbols Y, X1, X2 and X3 for the variables in your calculation. Show computer output.

Country no Per Capita % of L in Ag Average Yr school Health Index
1 42 15 7 70
2 51 12 12 80
3 50 8 11 75
4 47 10 8 72
5 48 11 10 76
5 62 6 16 96
7 53 8 10 82
8 50 9 11 80
9 55 6 13 85
10 60 8 15 92
11 55 7 11 90
12 66 4 16 99
13 58 6 14 90
14 56 7 12 85
15 61 6 15 95
16 40 16 6 67

Find the Y-intercept and slopes for the three independent variables and interpret them. Predict the per capita income when percentage of labor force in Agriculture is 5, average years of schooling is 14. And Health Index is 90. Find the overall explanatory power (Coefficient of Determination) of the model and interpret it. Also find the adjusted coefficient of Determination and interpret it. Find the standard error of estimate. From the ANOVA table find SSR, SSE and SST and the F-value. Perform the F-test and comment on the overall usefulness of the model. What are the degrees of freedom for Regression, Error and Total? Perform t-test for the statistical significance of individual coefficients. Do you any indication of Multicollinearity? Plot the errors or residuals by countries and comment on the visible pattern. Plot the errors separately by each explanatory variable and comment on the visible patterns with respect to heteroscedasticity.

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