From c9f160390aed231933ad2879d4be77167e68d091 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Metehan=20G=C3=9CNG=C3=96R?= <102655648+gungorMetehan@users.noreply.github.com> Date: Fri, 12 Sep 2025 09:28:41 +0300 Subject: [PATCH] Update inf-model-mlr.qmd some typos were fixed. --- inf-model-mlr.qmd | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/inf-model-mlr.qmd b/inf-model-mlr.qmd index c7fb389c..a17bf611 100644 --- a/inf-model-mlr.qmd +++ b/inf-model-mlr.qmd @@ -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`. ::: @@ -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. @@ -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. @@ -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.