Home » Fitting Statistical Models to Data with Python » Which of our predictors has the largest coefficient? Which of our predictors has the largest coefficient? 5. Question 5 Which of our predictors has the largest coefficient? 1 / 1 point Intercept DMDEDUC2x[T.HS] DMDEDUC2x[T.SomeCollege] DMDEDUC2x[T.x9_11] Other Questions Of This Category Which values for DMDEDUC2x and RIAGENDRx are represented in our intercept, or what is our reference level?Based on the logistic regression with both Age and BMI as covariates, are the coefficients statistically significant at a two-sided 10% significance level?Are the predictors for this model statistically significant, yes or no? (Hint: What are their p-values?)You want to fit a model that enables the prediction of a continuous measure of birth weight for all of the newborns at a single large hospital. The data arise from a simple random sample of 500…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…Which of the following scatterplot(s) would have a correlation coefficient that is close to 0? (Select all that apply.)The 95% confidence interval for the coefficient for Age is given above as (0.014, 0.020). If instead we wanted a 90% confidence interval, how would the width of the interval change?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?What most likely happened to our R-Squared value when we added the third predictor LSTAT to our initial model?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…A new model was fit, this time adding in the two categorical variables Sex (0=male, 1=female) and Age (0=young, 20-46 years old, 1= old, 46+ years old), the model summary is shown belowLet's say that we observe a person with an IQ of 125, as we did in the lecture. Which way should the posterior distribution, after our Bayesian update, shift?What model should we use when our target outcome, or dependent variable is binary, or only has two outputs, 0 and 1?For this problem, we are going to be using the above code to recreate some of the mathematics behind the Introduction to Bayesian Statistics lecture. The math has already been worked out for you, so…The head size of an 8 year old child is found to be 1800 cm3, What caution(s) should be noted if asked to predict this child’s brain weight? (Select all that apply.)