### Personality and Academic Performance

textbook example

prediction

This post provides details of an example of predicting a quantitative outcome from

*Lessons*Chapter 22 . The example concerns predicting a college student’s overall grade-point average (GPA) from predictor variables that could be available at…### Trade deficits and the Opium Wars

p-values

significance

William Playfair (1759-1823) is credited as the…

### A Bayes interpretation of Hypothesis testing

likelihood

prior

posterior

likelihood ratio

odds

The Bayesian paradigm provides a complete framework for…

### Lesson 36 take-aways

Class sessions

likelihood

null hypothesis

alternative hypothesis

prior

posterior

p-value

confidence intervals

We are spending this week on a topic…

### Named tests from Stat 101

one- and two-sample tests

p-test

t-test

ANOVA

simple regression

A Stat 101 course will cover many…

### A bad graph for medical screening

prevalence

sensitivity

specificity

prior

posterior

In Lesson 35, in the context of medical screening tests, we presented diagrams like this one.

### Competing two hypotheses

likelihood

prior

posterior

likelihood ratio

odds

The idea of competing two hypotheses …. [UNDER CONSTRUCTION]

### Lesson 35 take-aways

Class sessions

hypothesis (definition)

sensitivity/specificity

prevalence

prior

posterior

Define a

**hypothesis**(in a statistical sense) as “a statement that might or might not be true.” This is really just a wordy way of…### Lesson 34 take-aways

Class sessions

classifier

logistic regression

threshold

loss function

false-positive/false-negative

A

**classifier**is a machine that, based on measurements of some sort assigns a**categorical level**to the object the measurements came from. We can make this less abstract by talking about classifiers in the context of…### Lesson 32 take-aways

Class sessions

experiment

blocking

random assignment

The data analysis techniques we have been using apply equally well…

### Lesson 26 take-aways

Class sessions

prediction

probability distribution

bayesian updating

In Lesson 25 we pointed out that the proper form for a prediction is a list of the potential outcomes, each matched to a probability of…

### Lesson 25 take-aways

Class sessions

prediction

estimation

intervals

probability distribution

We contrasted the very different tasks of ….

### Bayesian updating for self-driving cars

self-driving cars

prediction

belief

Bayes

probability distribution

likelihood

Suppose ordinary new-ish…

### Lesson 23 take-aways

Class sessions

confidence intervals

precision

accuracy

`lm()`

creates a model, which we can summarize in several ways. These numerical summaries—for instance, the coefficients reported by `lm()`

—are called **sample statistics**. Mathematically, the sample statistics are exact, that is, the arithmetic is done correctly and everyone will get the same sample statistics when building the same model on…

### Lesson 22 take-aways

Class sessions

signal and noise

sampling variability

sampling variance

We will use DAGs for three purposes:

### Lesson 21 take-aways

Class sessions

signal and noise

linear model shapes

model function

model value

residual

response value

DAGs (“directed acyclic graphs”) have three properties, all of which are essential for representing causality.

### Lesson 20 take-aways

Class sessions

variance

DAGs

causality

Interpretation: The heights of the people in the

`Galton`

data frame…
### Lesson 19 take-aways

Class sessions

variance

Response variable will always be quantitative/numerical in a regression model.

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