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### 17 Cards in this Set

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 binary variable a variable with only two values continuous variable a variable in which the numbers act as numerical values. ex: age, birth weight also called quantitative variable nominal variable categorica variable where the categories are unordered. E.g.: gender, birth type ordinal variable categorical variable where the categories imply an order on some continuum (e.g., level of health problems) Type I Error rejecting the null hypothesis when it is true concluding there is a difference when there is not called alpha level of significance of the test probability of committing a type I error Type II Error accepting the null hypothesis when it is false concluding there is no difference when there is one called beta power of the study ability to detect a difference if a true difference exists i.e., probability of rejecting null hypothesis when it is false 1 - beta beta usu. = 0.2, so power = 80% p-value the probability that an observed association is due to chance logistic regression outcome of interest is binary allows adjustment for confounding variables provides direct estimate of odds ratio for each indep. variable correlation coefficient measures strength of association btwn 2 continuous variables between -1 and 1 not equal to slope of line Kaplan-Meier plot survival vs. time each "step" represents an outcome event multiple regression like linear regression but allows multiple independent variables can be used to adjust for confounders case fatality (deaths due to a disease) / (number of people with that disease) absolute risk difference difference between risk of outcome in exposed group and risk in unexposed number needed to treat (NNT) 1/ARD Cox Proportional Hazards Regression similar to other multiple regressions form of survival analysis assesses independent effect of multiple variables on survival can predict rate at which outcomes will occur estimates relative risk