Data Selection, Analysis And Reporting - Development Report Writing - Data Analytics Assignment Help

Assignment Task

1. Introduction
• You will need to apply the concepts you learned in the lecture to the current situation of the countries in your sample
– describe the data for your set of countries including the mean, quartiles and standard deviation of the variables
– report the number of missing values in the respective variables per country and impose spline interpolation for obtaining full dataset.
– produce meaningful tables and graphs.

2. Growth Regression
Run a Multiple linear regression analysis on the following equation

GDP Growth = β1+ β2Exports+ β3FDI Where FDI= Foreign Direct Investment
• Interpret the estimated co-efficients.
• In the above equation, suppose the researcher forgot to include Foreign Direct Investment as a variable. What bias it would induce in the equation and calculate that bias
Now, work with the expanded regression equation by adding Population growth (P) and Inflation (I).
GDP Growth = β1 + β2Exports + β3FDI +β4Inflation+β5 Pop Where Pop = Population Growth.
• Test the assumptions of Ordinary Least squares for the regression above and discuss the implications for the violation of the assumptions
• Using t-test, test the hypothesis that the coefficient of variable Foreign Direct Investment (FDI) is insignificant, use critical values for 5 and 1 percent using the normal
approximation. Interpret the results.
• Using F-test, test the joint hypothesis that the coefficient of all the variables are insignificant. Use critical values for 1 and 5 percent using the F distribution with appropriate degrees of freedom. Interpret the results.
• As an alternative to F-test, use LM test for part above and compare the results.

• Interpret the regression results for all of the models
• Please use polynomial of degree 2 for each of the independent variables for the first model. Explain the rationale behind using polynomial of 2 degree.
• Calculate the value of the variables at which the function is maximum or minimum.
• What can we learn from the models about the direction of causality between education, health and income? Explain.

4. Governance Interactions
• Run a multiple linear regression on the following equations. Download the data from for the purpose.
GDP percapita= β1+β2INFR +β3Gov
+β4Health+β5Education+β6Health∗Gov
GDP percapita=β1+β2INFR +β3Gov
+β4Health+β5Education+β6Education∗Gov
where Gov. is governement effectiveness, INFR is quality of infrastructure and GDP is GDP per capita
• Interpret the coefficients of interacted variables. Explain the rationale behind the interaction of the variables.
• By using Regression equation specification error (RESET) test, check to see if the above equation‘s functional form is adequate
• Carry out the Variance Inflation Factor test to check for multicollinearity. Interpret if multicollinearity is an issue in the model.
• Calculate all OLS residuals and report the highest and lowest number. Generate the histogram of the residuals.

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