## answer

```
"Because we are interested in the model parameters that
best describe the population from which the sample was
drawn. Due to sampling error, we can expect some
variability in the model parameters."
```

Not graded, just practice

Author

Katie Schuler

Published

November 28, 2023

- As we collect more data, our parameter estimates

- Each figure below plots 100 bootstrapped models with data drawn from the same population. In one figure, the model is fit to 10 data points. In the other, each model is fit to 200 data points. Which figure shows the model fit to 200 data points?

- As we collect more data, what happens to the confidence interval around our parameter estimates?

- True or false, we can obtain confidence intervals around parameter estimates for models in the same we we did for point estimates like the mean.

- Model reliability asks how certain we can be about our parameter estimates. Why is there uncertainty around our parameter estimates?

- The figure below shows the model fit for a sample of 10 participants. Suppose we repeated our experiment with 10 new participants. True or false, fitting the same model to these new data would yield approximately the same parameter estimates.

- True or false, a model with high accuracy must also have high reliability.

- The model
`y ~ poly(x,2)`

is plotted in which of the figures below?

Which of the equations below expresses a quadratic polynomial model in R?

`y ~ poly(x, 1)`

`y ~ poly(x, 2)`

`y ~ poly(x, 3)`

`y ~ poly(x, 4)`

- True or false,
`lm()`

can be used to fit a linearized nonlinear model.

Fill in each blank below with the model building process best described by the definition:

- is finding the best fitting parameter estimates.
- is quantifying the uncertainty on the parameter estimates.
- is choosing the type of model and its functional form.
- is quantifying how well our model fits our data.

- Which of the following aspects of model building apply to classification models? (Choose all that apply)

- What is the difference between regression and classification?

- Which accuracy metric is best applied to classification models?

- In the figure below, which aspect shows the response variable?

- Which figure below could show a plotted classification model? Choose all that apply.

- Name two kinds of linear classifiers

We can impliment classification via

True or false, in R, we can perform logistic regression with a generalized linear model.

- What 3 elements do all GLMs have?

- What is the link function for logistic regression?

- Which of the following fits a logistic regression model in R? Choose one.

```
# code A
glm(y ~ x, data = data, family = "binomial")
# code B
data %>%
specify(y ~ x) %>%
fit()
# code C
logistic_reg %>%
set_engine("glm") %>%
fit(y ~ x, data = data)
```