Question

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)

Solution Preview

This material may consist of step-by-step explanations on how to solve a problem or examples of proper writing, including the use of citations, references, bibliographies, and formatting. This material is made available for the sole purpose of studying and learning - misuse is strictly forbidden.

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%....

This is only a preview of the solution. Please use the purchase button to see the entire solution

$123.00

or free if you
register a new account!

Related Homework Solutions

Microeconomics Questions
Homework Solution
$48.00
Business
Economics
Microeconomics
Consumer
Choice
Marginal
Utility
Rational
Maximization
Income
Price
Profit
Payoff
Prisoner’s Dilemma
High Prices
Low Prices
Consumer Surplus
Sunk Cost
Microeconomics Questions
Homework Solution
$75.00
Business
Economics
Microeconomics
Strategy
Profit
Price
Commission
Fee
Manufacturing
Economics Questions
Homework Solution
$60.00
Economcs
Budweiser
Miller
Coors
Television
Advertising
Campaign
Reduce
Costs
Market
Cargill
Graph
Shaun Sinclair
Homework Solution
$8.00
Business
Economics
Shaun
Sinclair
Community
Sales
Cost
Profit
Get help from a qualified tutor
Live Chats