Home » Fitting Statistical Models to Data with Python » What most likely happened to our R-Squared value when we added the third predictor LSTAT to our initial model? What most likely happened to our R-Squared value when we added the third predictor LSTAT to our initial model? 3. Question 3 What most likely happened to our R-Squared value when we added the third predictor LSTAT to our initial model? 1 / 1 point Decreased Increased Stayed the same Other Questions Of This Category The study relating brain weight (grams) and head size (cubic cm) yielded an R-squared of 0.6393. Which of the following is a correct interpretation of the R-squared?Suppose that the migraine researcher was also interested in pain score trends immediately following administration of the drug. The researcher continues to collect pain score measurements at four,…The figure below presents the fits of four different regression models to the same set of data, where there is a predictor variable (x) and a dependent variable (y) of interest. Which of the four…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…Now, lets say that I observe two more people and I see that they also have IQs of 110. So we have three people with IQs of 110. How does the variance of my estimate change from my prior? We can do…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?),…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 estimating the relationship between gender (the IV) and a binary indicator of ever having experienced a major depressive disorder (the DV), where both variables were collected…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…What is true about multiple linear regression and marginal linear models when dependence is present in data?Which values for DMDEDUC2x and RIAGENDRx are represented in our intercept, or what is our reference level?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…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 belowWhat type of model should we use when our target outcome, or dependent variable, is continuous?