# Columbia's Final Mission Case Study Solutions

Posted By Admin @ Mar 02, 2022

and Finance

APA

ABC

Tittle: Discussion on Regression analysis

Type: Courses

Subject: Accounting Date

Regression analysis

Strongest predictor

The coefficient in the regression analysis for Household income in 2014-2015 is 5144.5.

The value of significance factor in the regression analysis of % single parent 2012-2016 is above 0.005 that is 0.009.

The regression analysis calculated for Population density in 2010 showed value of significance factor is 1.69×10^(-107).

In case of regression analysis of Income in 1990 the value of significance factor is below than 0.005 that is measured as 0.

Like the income analysis the significance factor for Median rent 2012-2016 is 0.

The regression analysis of Census response rate in 2010 shows significance factor as zero. On the basis of these analysis the maximum predictive parameter is observed for percentage single parent 2012-2016.

Consistency

In the regression analysis 5 parameters were considered to predict the household income. The value of significance factor in the regression analysis of % single parent 2012-2016 is above 0.005 that is 0.009 therefore only this result is valuable. The result is consistent with the regression outcomes of the analysis.

SUMMARY OUTPUT

Regression Statistics

Multiple R 0.082403245

R Square 0.006790295

Adjusted R Square 0.005794096

Standard Error 14461.97686

Observations 999

ANOVA

df SS MS F Significance F

Regression 1 1425601545 1425601545 6.816207973 0.009168997

Residual 997 2.08521E+11 209148774.6

Total 998 2.09947E+11

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 41772.21188 1102.545418 37.88706677 2.6616E-195 39608.63602 43935.78773 39608.63602 43935.78773

Fraction_Single_Parents_in_2012-16 -5095.747473 1951.805248 -2.610786849 0.009168997 -8925.865155 -1265.629792 -8925.865155 -1265.629792

strength and weakness

The analysis predicts the strength and weakness of the results a dhow it can be used to reduce the poverty. Different policy implications can be used to image the research from different perspectives. The report can be used to overcome the issues faced by the average families in the selected area. There are different possibilities to reduce poverty along with variety of disciplines. The best solution is to maintain the competence even under the adverse conditions. The maximize optimal response is used to overcome the challenges of low-income families.