You are interested in predicting the probability that an NCAA men’s basketball team wins their first round game in the annual NCAA men’s basketball tournament, where potential predictors of the binary indicator of winning the first game include a variety of team-level variables measured for each of the 64 teams competing in the first round. There is only one observation per team, and the dependent variable is a binary indicator (1, 0) of whether the team won their first round game.

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You wish to fit a model to a "forced choice" binary dependent variable measuring political party preference (if you had to pick a political party, which would you select: Democratic or Republican?),…

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After publishing a research paper describing the results from the model fitted for Question #3, you are contacted by 20 other large hospitals, and they wish to contribute to the estimation of a model…

What is true about multiple linear regression and marginal linear models when dependence is present in data?

What model should we use when our target outcome, or dependent variable is binary, or only has two outputs, 0 and 1?

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Next, a model is fit adding Age as an additional covariate to BMI as the variables predicting smoking status. The output is here: What does the coefficient of 0.0169 mean in context?

Multilevel models are expressed in terms of ________.

Which of the following is a possible form/shape for a logistic regression model, where the y-axis represents the probability of success?

We’d like to predict the log odds of smoking 100+ cigarettes for a given individual using the logistic regression model with the two variables: BMI and Age. For an individual with a BMI of 22 who is…

What type of model should we use when our target outcome, or dependent variable, is continuous?

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Based on the logistic regression with both Age and BMI as covariates, are the coefficients statistically significant at a two-sided 10% significance level?

NHANES records whether an individual has smoked 100 cigarettes or more. The next few questions will focus on fitting models to predict whether someone has smoked 100+ cigarettes. First, a model is fit…