Regression model development and assess the future employment scenario in developing countries (Please refer to attachment 'Table 1')
Answers may address the following items:
1) Comment on the relevance of data presented in Table 1 (attached Excel file) and briefly describe the approach you would like to adopt in developing a model that might address major concerns in the employment situation.
2) With the help of regression analysis, create a model that best describes the situation. Indicate clearly the effect that each of the factors given in the attached Excel file and other factors may have on unemployment.
3) Recommend the summary measures that would be most useful to understand the obtained results.
4) Comment on the results of your empirical analysis and suggest if they can be used for forecasting the future employment situation in any country.© BrainMass Inc. brainmass.com December 20, 2018, 10:32 am ad1c9bdddf
Regression Model Development and Analysis:
Table-1 gives the data on Population (in thousands), Population (65 & above, %), Life Expectancy (years), Literacy (%), GDP/Cap (in thousands USD), Labour Force (in millions) and Unemployment (%) for 45 countries. All the above aspects are either directly or indirectly related to the employment situation for a country and are relevant for developing a model that might address major concerns in the employment situation for countries in general. We shall study the relationship of each of these aspects with the unemployment percentage for the countries using multiple regression analysis.
Multiple Regression Analysis has been selected as the appropriate statistical model to conduct an analysis on the factors affecting employment situation in countries. In this model, the dependent variable is the unemployment percentage. All the other factors have been considered as independent variables or predictors, for predicting the dependent variable. The Multiple Regression Analysis has been performed in EXCEL.
Multiple R 0.681507376
R Square 0.464452304
Adjusted R Square 0.379892142
Standard Error 4.684151615
df SS MS F Significance F
Regression 6 723.0834985 120.513916 5.49256636 0.000357371
Residual 38 833.7685015 21.9412764
Total 44 1556.852
Coefficients Standard Error t Stat P-value
Intercept 55.35896361 11.04135883 5.01378177 0.0000
Population (in thousands) -5.54825E-05 3.47696E-05 -1.5957187 0.1188
Population (65 & above, %) 0.59536218 0.224083034 2.656882 0.0115
Life Expectancy ...
Regression model development and analysis are examined. The expert assess the future employment scenarios in developing countries.