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ISP 121 -- Activity 6

Linear Correlation

 

Turn in: a Word file with the requested SPSS output and the typed answers to the questions.

Goals:   (1) See how correlation can help explain relationships. (2) Understand the difference between correlation and causality.

Note:   All Excel files listed on this page can be found on the QRC website. Compute the requested correlations with SPSS. Copy and paste the output onto a Word file to submit. All questions marked with an asterisk (*) require a written answer, as well as the SPSS output on which your answer is based. Remember to delete all lines in the Excel file except the variable names and the data lines. SPSS variable lines cannot include spaces.

Part A:   Open the file StateSATS2006.xls which contains data on average SAT scores and the percent taking the SAT in each state in the US.

  1. Sort the dataset. *Which state has the highest average SAT scores and which state has the lowest the lowest.

  2. Print the dataset.

  3. *Compute the correlation between the average score and the percentage of students taking the test.

  4. Make a scatterplot of the average score and the percentage of students taking the test. Find the R-squared value.

  5. *Write a short paragraph describing the relationship between average SAT score and percentage student taking the test. Include a reasonable explanation for the type of correlation that is apparent.

  6. *How does Illinois compare in average SAT score? In percent taking the SAT? Make a conjecture why so few Illinois students take the SAT. How would go about testing your conjecture?

Part B:   Open the file TVLifeExpectancy.xls, which contains data on life expectancy and the number of TV's per person in selected countries.

  1. Print the dataset.

  2. Make a scatterplot of the data. *Find the R-squared value.

  3. *Is there a correlation between life expectancy and number of TV's per person?

  4. *Can we infer from the data that TV's promote (or cause) longevity? Can you name some common underlying causes for both longevity and higher rates of televisions per capita?