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10 changes: 5 additions & 5 deletions inf-model-mlr.qmd
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Expand Up @@ -29,7 +29,7 @@ Recall the `loans` data from [Chapter -@sec-model-mlr].

::: {.data data-latex=""}
The [`loans_full_schema`](http://openintrostat.github.io/openintro/reference/loans_full_schema.html) data can be found in the [**openintro**](http://openintrostat.github.io/openintro) R package.
Based on the data in this dataset we have created two new variables: `credit_util` which is calculated as the total credit utilized divided by the total credit limit and `bankruptcy` which turns the number of bankruptcies to an indicator variable (0 for no bankruptcies and 1 for at least 1 bankruptcies).
Based on the data in this dataset we have created two new variables: `credit_util` which is calculated as the total credit utilized divided by the total credit limit and `bankruptcy` which turns the number of bankruptcies to an indicator variable (0 for no bankruptcies and 1 for at least 1 bankruptcy).
We will refer to this modified dataset as `loans`.
:::

Expand Down Expand Up @@ -159,7 +159,7 @@ We also note that the total `number_of_coins` and the `number_of_low_coins` are
#| fig-cap: |
#| Two plots describing the total amount of money (USD) as a function of the
#| total number of coins or low coins. As you might expect, the total amount
#| of money is more highly postively correlated with the total number of coins
#| of money is more highly positively correlated with the total number of coins
#| than with the number of low coins.
#| fig-subcap:
#| - Total number of coins on the x-axis.
Expand Down Expand Up @@ -417,7 +417,7 @@ terms_chp_25 <- c(terms_chp_25, "cross-validation", "prediction error")
```

::: {.data data-latex=""}
The [`penguins`](https://allisonhorst.github.io/palmerpenguins/articles/intro.html) data can be found in the [**palmerpenguings**](https://github.com/allisonhorst/palmerpenguins) R package.
The [`penguins`](https://allisonhorst.github.io/palmerpenguins/articles/intro.html) data can be found in the [**palmerpenguins**](https://github.com/allisonhorst/palmerpenguins) R package.
:::

Our goal in this section is to compare two different regression models which both seek to predict the mass of an individual penguin in grams.
Expand All @@ -444,9 +444,9 @@ Cross-validation is one way to get accurate independent predictions with which t

The question we will seek to answer is whether the predictions of `body_mass_g` are substantially better when `bill_length_mm`, `bill_depth_mm`, `flipper_length_mm`, `sex`, and `species` are used in the model, as compared with a model on `bill_length_mm` only.

We refer to the model given with only `bill_lengh_mm` as the **smaller** model.
We refer to the model given with only `bill_length_mm` as the **smaller** model.
It is seen in @tbl-peng-lm-bill with coefficient estimates of the parameters as well as standard errors and p-values.
We refer to the model given with `bill_lengh_mm`, `bill_depth_mm`, `flipper_length_mm`, `sex`, and `species` as the **larger** model.
We refer to the model given with `bill_length_mm`, `bill_depth_mm`, `flipper_length_mm`, `sex`, and `species` as the **larger** model.
It is seen in @tbl-peng-lm-all with coefficient estimates of the parameters as well as standard errors and p-values.
Given what we know about high correlations between body measurements, it is somewhat unsurprising that all of the variables have low p-values, suggesting that each variable is a statistically discernible predictor of `body_mass_g`, given all other variables in the model.
However, in this section, we will go beyond the use of p-values to consider independent predictions of `body_mass_g` as a way to compare the smaller and larger models.
Expand Down
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