Here’s question 11 of our exam: 11. We defined a new variable based on weight (in pounds): heavy 200 and then ran a logistic regression, predicting “heavy” from height (in inches): glm(formula = heavy ~ height, family = binomial(link = “logit”)) coef.est coef.se (Intercept) -21.51 1.60 height 0.28 0.02 — n = 1984, k = […]

**Teaching**category.

## Question 10 of our Applied Regression final exam (and solution to question 9)

Here’s question 10 of our exam: 10. For the above example, we then created indicator variables, age18_29, age30_44, age45_64, and age65up, for four age categories. We then fit a new regression: lm(formula = weight ~ age30_44 + age45_64 + age65up) coef.est coef.se (Intercept) 157.2 5.4 age30_44TRUE 19.1 7.0 age45_64TRUE 27.2 7.6 age65upTRUE 8.5 8.7 n […]

## Question 9 of our Applied Regression final exam (and solution to question 8)

Here’s question 9 of our exam: 9. We downloaded data with weight (in pounds) and age (in years) from a random sample of American adults. We created a new variables, age10 = age/10. We then fit a regression: lm(formula = weight ~ age10) coef.est coef.se (Intercept) 161.0 7.3 age10 2.6 1.6 n = 2009, k […]

## Question 8 of our Applied Regression final exam (and solution to question 7)

Here’s question 8 of our exam: 8. Out of a random sample of 50 Americans, zero report having ever held political office. From this information, give a 95% confidence interval for the proportion of Americans who have ever held political office. And the solution to question 7: 7. You conduct an experiment in which some […]

## Question 7 of our Applied Regression final exam (and solution to question 6)

Here’s question 7 of our exam: 7. You conduct an experiment in which some people get a special get-out-the-vote message and others do not. Then you follow up with a sample, after the election, to see if they voted. If you follow up with 500 people, how large an effect would you be able to […]

## Question 6 of our Applied Regression final exam (and solution to question 5)

Here’s question 6 of our exam: 6. You are applying hierarchical logistic regression on a survey of 1500 people to estimate support for a federal jobs program. The model is fit using, as a state-level predictor, the Republican presidential vote in the state. Which of the following two statements is basically true? (a) Adding a […]

## Question 5 of our Applied Regression final exam (and solution to question 4)

Here’s question 5 of our exam: 5. You have just graded an exam with 28 questions and 15 students. You fit a logistic item-response model estimating ability, difficulty, and discrimination parameters. Which of the following statements are basically true? (a) If a question is answered correctly by students with low ability, but is missed by […]

## Question 4 of our Applied Regression final exam (and solution to question 3)

Here’s question 4 of our exam: 4. A researcher is imputing missing responses for income in a social survey of American households, using for the imputation a regression model given demographic variables. Which of the following two statements is basically true? (a) If you impute income deterministically using a fitted regression model (that is, imputing […]

## Question 3 of our Applied Regression final exam (and solution to question 2)

Here’s question 3 of our exam: Here is a fitted model from the Bangladesh analysis predicting whether a person with high-arsenic drinking water will switch wells, given the arsenic level in their existing well and the distance to the nearest safe well. glm(formula = switch ~ dist100 + arsenic, family=binomial(link=”logit”)) coef.est coef.se (Intercept) 0.00 0.08 […]

## Question 2 of our Applied Regression final exam (and solution to question 1)

Here’s question 2 of our exam: 2. A multiple-choice test item has four options. Assume that a student taking this question either knows the answer or does a pure guess. A random sample of 100 students take the item. 60% get it correct. Give an estimate and 95% confidence interval for the percentage in the […]

## Question 1 of our Applied Regression final exam

As promised, it’s time to go over the final exam of our applied regression class. It was an in-class exam, 3 hours for 15 questions. Here’s the first question on the test: 1. A randomized experiment is performed within a survey. 1000 people are contacted. Half the people contacted are promised a $5 incentive to […]

## John Le Carre is good at integrating thought and action

I was reading a couple old Le Carre spy novels. They have their strong points and their weak points; I’m not gonna claim that Le Carre is a great writer. He’s no George Orwell or Graham Greene. (This review by the great Clive James nails Le Carre perfectly.) But I did notice one thing Le […]

## Do regression structures affect research capital? The case of pronoun drop. (also an opportunity to quote Bertrand Russell: This is one of those views which are so absurd that only very learned men could possibly adopt them.)

A linguist pointed me with incredulity to this article by Horst Feldmann, “Do Linguistic Structures Affect Human Capital? The Case of Pronoun Drop,” which begins: This paper empirically studies the human capital effects of grammatical rules that permit speakers to drop a personal pronoun when used as a subject of a sentence. By de‐emphasizing the […]

## “Incentives to Learn”: How to interpret this estimate of a varying treatment effect?

Germán Jeremias Reyes writes: I am currently taking a course on Applied Econometrics and would like to ask you about how you would interpret a particular piece of evidence. Some background: In 2009, Michael Kremer et al. published an article called “Incentives to learn.” This is from the abstract (emphasis is mine): We study a […]

## Lessons about statistics and research methods from that racial attitudes example

Yesterday we shared some discussions of recent survey results on racial attitudes. For students and teachers of statistics or research methods, I think the key takeaway should be that you don’t want to pull out just one number from a survey; you want to get the big picture by looking at multiple questions, multiple years, […]

## David Weakliem on the U.S. electoral college

The sociologist and public opinion researcher has a series of excellent posts here, here, and here on the electoral college. Here’s the start: The Electoral College has been in the news recently. I [Weakliem] am going to write a post about public opinion on the Electoral College vs. popular vote, but I was diverted into […]

## Ben Lambert. 2018. *A Student’s Guide to Bayesian Statistics.*

Ben Goodrich, in a Stan forums survey of Stan video lectures, points us to the following book, which introduces Bayes, HMC, and Stan: Ben Lambert. 2018. A Student’s Guide to Bayesian Statistics. SAGE Publications. If Ben Goodrich is recommending it, it’s bound to be good. Amazon reviewers seem to really like it, too. You may […]

## (Markov chain) Monte Carlo doesn’t “explore the posterior”

[Edit: (1) There’s nothing dependent on Markov chain—the argument applies to any Monte Carlo method in high dimensions. (2) No, (MC)MC is not not broken.] First some background, then the bad news, and finally the good news. Spoiler alert: The bad news is that exploring the posterior is intractable; the good news is that we […]

## Kevin Lewis has a surefire idea for a project for the high school Science Talent Search

Here’s his idea: If I were a student, I’d do a study on how Science Talent Search judges are biased. That way, they can’t reject it, otherwise it’s self-confirming. That’s a great idea! Maybe it’s possible to go meta on this one by adding some sort of game-theoretic model or simulation of talent search submission […]

## Statmodeling Retro

As many of you know, this blog auto-posts on twitter. That’s cool. But we also have 15 years of old posts with lots of interesting content and discussion! So I had this idea of setting up another twitter feed, Statmodeling Retro, that would start with our very first post in 2004 and then go forward, […]