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.
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.
Please see the attachments.
Please note that this is not a hand in ...
The solution provides step by step method for the calculation of regression analysis. Formulas for the calculations and interpretations of the results are also included.
Real life applications of regression
"What are some examples of practical applications for correlation and regression analysis that might be of use to us?"
The goal is to get people thinking about how they can actually use correlation and regression in their real life, and where and how can they can really benefit from these techniques?
Please provide examples for each of the following: linear regression, correlation, and multiple linear regressions.View Full Posting Details