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Regression analysis - Statistics Project Example

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Where p is the probability that someone will be tested with prostate cancer, k is the coefficient of regression, and v is the most significant variable in determining the probability p. Outliers in this dataset include cancer, inv and cap since they have fixed and immeasurable…
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Regression analysis project
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Download file to see previous pages The best model, therefore, uses c to represent c.vol, in the regression model, which now becomes:
The code for this regression is found in the Appendix A section, while the output is found in Appendix B section. The coefficient of logistic regression in this analysis for the c.vol is 0.80404, a highly positive coefficient, giving a probability (pr = 0.01539) that the patient will test positive. The second variable that indicates a positive test is psa, with a coefficient of 0.00226 a low positive coefficient, with a probability of 0.03847. The rest of the variables have negative association with the test for the hypothesis (Long, 1997).
This test estimates the cancer diagnosis for someone with 10 psa, 5 c.vol, 40g for weight, age 67, with 2.5 benign, with no seminal vesicle invasion, and with 0.5 cm cap. The test is done through the same model and the results are found in Appendix C section. The coefficient of association is 1.46414, with a probability of 0.160296, hence the patient tests positive with prostate cancer.
Where p is the probability that gender or treatment has a significant effect on blood calcium level, C is the coefficient of regression, and v is the most significant variable in determining the probability p. there is no outlier in this dataset.
The necessary transformation in this regression process is the binomial transformation using “mylogit” function. The two variables gender and treatment are both significant in determining the results of the hypothesis test (Hosmer & Lemeshow, 2000). The best model, therefore, combines the two variables in the regression as follows:
The code for this regression is found in the Appendix D section, while the output is found in Appendix E section. The coefficient of logistic regression in this analysis is 0.0206, a low positive coefficient, giving a probability (pr = 0.0247) that the Treatment significantly affects the ...Download file to see next pagesRead More
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