## Statistical Reasoning in Public Health II

Provide a broad overview of bio-statistical methods and concepts used in the public health sciences, emphasizing interpretation and concepts rather than calculations or mathematical details. Develop ability to read the scientific literature to critically evaluate study designs and methods of data analysis. Introduce basic concepts of statistical inference, including hypothesis testing, p-values, and confidence intervals. Topics include comparisons of means and proportions; the normal distribution; regression and correlation; confounding; concepts of study design, including randomization, sample size, and power considerations; logistic regression; and an overview of some methods in survival analysis. Draw examples of the use and abuse of statistical methods from the current biomedical literature.

After completion of this course, you will be able to recognize different study designs and understand the pros and cons of each; learn methods for randomly assigning subjects to two groups; understand the concepts of confounding and statistical interaction; know how to recognize each; explain the relationship between power and sample size; use Stata to perform sample size calculations; create a scatter-plot to visually assess the nature of an association between two continuous variables; interpret the calculated values of the correlation coefficient and the coefficient of determination, and understand the relationship between these two measures of association; perform a simple linear regression using Stata and use the results to assess the magnitude and significance of the relationship between a continuous outcome variable and a continuous predictor variable and for predicting values of the outcome variable; understand why multiple regression techniques allow for the analysis of the relationship between an outcome and a predictor in the presence of confounding variables; perform a multiple linear regression using Stata and use the results to assess the magnitude and significance of the relationship between a continuous outcome variable and multiple continuous and categorical predictor variables and for predicting values of the outcome variable; perform a multiple logistic regression using Stata and use the results to assess the magnitude and significance of the relationship between a dichotomous outcome variable and multiple continuous and categorical predictor variables; and interpret the results from a proportional hazards regression model.

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