Quantitative Business Analysis

Using the dataset provided in InternationalHealth.xls file, analyze worldwide life expectancy by answering the following questions:

  • (10 points) Determine the mean, median, mode, range, variance and standard deviations for life expectancy – total, male, and female. What do these numbers show?
  • (10 points) What are the 95 percent confidence intervals for the mean life expectancy of males and females? Set up and perform a hypothesis test to determine if there is a statistically significant difference between the mean life expectancy for males and females. Please make sure you write the null and the alternative hypothesis. Also, assume a significance level of 5%. Interpret the result.
  • (20 points) Construct frequency distributions, histograms, and normal probability plots for life expectancy at birth (total), GDP per person, televisions, and hospital beds. Do these variables appear normally distributed? How can you tell?
  • (10 points) Construct a correlation matrix for all the variables in the dataset. Which variables appear to be highly correlated with life expectancy at birth (total)?
  • (30 points) Perform three separate simple regressions with life expectancy at birth (total) as the dependent variable and GDP per person, television sets, and hospital beds respectively as independent variables.
    • Interpret the regression coefficients
    • Interpret Rsquare
    • Are the coefficients statistically significant at 95 percent level of significance? How can you tell?
    • Is the independent variable a good predictor of the dependent variable? Why or why not?
  • (20 points) Perform a multiple regression analysis with life expectancy at birth (total) as the dependent variable and GDP per capita, tv sets, telephones, hospital beds, physicians, nurses, and pharmacists as your independent variables.
    • Interpret the regression coefficients.
    • Interpret Rsquare.
    • Which regression coefficients are statistically significant at 95 percent level of significance? How can you tell?
    • Are all the independent variables taken together good predictors for the dependent variable? Why or why not?

For each individual regression:

Notes:

  • You need to have Data Analysis add-in installed in Excel to be able to perform the tasks above. To add Data Analysis: File=> Options=> Add-ins => (in the lower part of the screen) Manage Excel Add-ins => Go => Analysis Took Pack => Ok
  • Excel will not perform a regression analysis when data are missing. For each regression, you may need to sort the data, eliminate the observations with missing values, and then run the analysis only on the rest of the data.
  • You will also need to eliminate missing data for histograms and normal probability plots.

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