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Correlation and Simple Linear Regression Using SPSS

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1. Use SPSS to provide key descriptive statistics for each continuous and ordinal variable (mean, median, standard deviation) in a table format. Provide a frequency table for categorical variables. Briefly describe the results in your tables.
2. Use SPSS to provide bivariate analysis. Compute multiple correlation coefficients and their p-values: (a) for the relationship between social studies and Math and Reading considered simultaneously; (b) the partial correlation coefficients for variables reading and Social studies when Gender is held constant; (c) the partial correlation coefficient for variables Math and Reading when Gender is held constant. Describe and interpret the results of these correlation coefficients and p-values.
B. What test is appropriate to measure the correlation between reading in rank and visual acuity in rank? Perform this test. Describe and interpret the results.
C. Perform two simple linear regressions: 1) social studies as a predictor of reading scores, and 2) math as a predictor of reading scores. Describe and interpret the results (including the coefficients). Include the linear regression equations.

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Understanding Linear Regressions

Application: Assessment

The testing of assumptions, recognition of limitations, and proper use of diagnostics are all necessary elements in the use of multiple linear regression for public health research. All of these elements allow biostatisticians to better assess the results of multiple linear regression models.

For this Assignment, you test that assumptions for multiple linear regression have been met, use SPSS to create a multiple linear regression, evaluate results to determine whether the model is appropriate, and finally interpret the relationships uncovered through this statistical test between the independent and dependent variables. Use the Week 4 Dataset (SPSS document) from the Learning Resources area to complete this assignment.

The Assignment
1. Explain the assumptions of Linearity, Sampling independence, Normality, and Homoscedasticity (or equal variance).
2. How would you test whether these have been met? (Note: for the exam you do not need to test these assumptions)
3. Using SPSS, test the assumption of Linearity between the independent and dependent variables.
4. Using SPSS, test the assumption of Normality for the dependent variable.
5. Conduct a multiple linear regression using SPSS. Provide relevant SPSS output and assess the statistical significance of the effects of mother's Age, BMI, and Coffee (Cups per Day) on Birth weight.
6. Explain the practical implications of your finding. Include a reference to the R square of the model in your discussion.
7. Discuss whether or not there is interaction (effect modification) first between Age and BMI and second between BMI and Coffee.

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