Linear Regression Predicting Threatened Species 
Statistical Topic:
Testing relationships between numerical variables is done using correlation and regression techniques.  In the determining the relationship between two numerical variable, linear regression is the most common type of model tested but other types of models could also be tested such as exponential, logarithmic, power curves, etc.
Student Issue:
Can GDP Per Capita predict the number of threatened species?
Data Set:
Using the data set created from the United Nations Cyber School Bus web site last lab, work with data for the 28 countries chosen, 
Goal of Data Analysis Lab:
Using linear regression descriptive and inferential analyses, test to determine if GPD per Capita predicts the number of threatened species in a country?
Statistical Techniques: 
  1. Which variable is the dependent variable? Which is the independent variable? Why does this information make a difference in your analyses?
  2. For descriptive analysis, create a scatterplot.  Compute the least square linear regression line that best fits your data set.  Put the equation for this line in the title for the scatterplot.  Compute r, the Pearson's product moment correlation, and R2, the coefficient of determination.  Are there any outliers or influential data values?
  3. Test to determine if the correlation coefficient is equal to 0.
  4. Test the "usefulness" of this regression line to predict the number of threatened species using ANOVA.  Explain what SSRegression and SSE actually mean in the context of this data set.
  5. Write a results paragraph that summarizes both types of analyses.
Social Commentary:
  1. Do underdeveloped countries have  a lower number of threatened species?  How would you test this?
  2. Why don't nations pay more attention to the number of animal and plant species faced with the possibility of extinction?