Use the MIcrosoft Excel file above to identify a potential cause-effect relationship (X-->Y) between two variables in the data that are of interest to you.

Use Microsoft Excel to complete the following:

Produce a scatterplot of the relationship to examine the correlation visually. Be sure to add the linear regression (trend) line to your scatterplot. (If the pattern is non-linear, you may need to transform one or more variables by taking the natural log.)

Run a simple (bivariate) regression analysis of the relationship. Interpret the slope, statistical significance (t-test, p-value), and R-squared.

Finally, identify a third variable that may be a potential common cause of both your X and Y variables and include this variable in a multiple regression analysis. Interpret the difference between these results and your simple (bivariate) results.
Summarize all of your analysis and results in the form of a memo to a policy maker or organization with an interest in the topic. Be sure to discuss the strengths, and limitations, of this kind of analysis for demonstrating a cause-effect relationship.
X: Expected years of Schooling (Indep. variable) ---(POSITIVE)---> Y: Life Expectancy at Birth (Dep. variable); Government expenditure on education (% of GDP) (Common Cause)

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Introduction
Life expectancy at birth depends on a wide range of factors. Accordingly, this report seeks to analyze cause-effect relationship between life expectancy and other variables such as expected years of schooling and Government expenditure on education. Since the report is based on statistical analysis only, the limitations are also discussed.
Regression analysis
The scatter plot has been shown in Appendix 1 and it can be seen that there is a linear relationship between the variables expected years of schooling (independent variable) and life expectancy at birth (dependent variable). It can be seen that there is a positive linear relation between the variables and the R squared of the linear regression is 59%....