stars to dashes lobjs

Author: trevorcampbell

source/clustering.Rmd

@@ -61,16 +61,16 @@ including techniques to choose the number of clusters.
 ## Chapter learning objectives
 By the end of the chapter, readers will be able to do the following:
 
-* Describe a situation in which clustering is an appropriate technique to use,
+- Describe a situation in which clustering is an appropriate technique to use,
 and what insight it might extract from the data.
-* Explain the K-means clustering algorithm.
-* Interpret the output of a K-means analysis.
-* Differentiate between clustering, classification, and regression.
-* Identify when it is necessary to scale variables before clustering, and do this using R.
-* Perform K-means clustering in R using `tidymodels` workflows.
-* Use the elbow method to choose the number of clusters for K-means.
-* Visualize the output of K-means clustering in R using colored scatter plots.
-* Describe the advantages, limitations and assumptions of the K-means clustering algorithm.
+- Explain the K-means clustering algorithm.
+- Interpret the output of a K-means analysis.
+- Differentiate between clustering, classification, and regression.
+- Identify when it is necessary to scale variables before clustering, and do this using R.
+- Perform K-means clustering in R using `tidymodels` workflows.
+- Use the elbow method to choose the number of clusters for K-means.
+- Visualize the output of K-means clustering in R using colored scatter plots.
+- Describe the advantages, limitations and assumptions of the K-means clustering algorithm.
 
 ## Clustering
 Clustering \index{clustering} is a data analysis technique

source/inference.Rmd

@@ -37,16 +37,16 @@ populations and then introduce two common techniques in statistical inference:
 ## Chapter learning objectives
 By the end of the chapter, readers will be able to do the following:
 
-* Describe real-world examples of questions that can be answered with statistical inference.
-* Define common population parameters (e.g., mean, proportion, standard deviation) that are often estimated using sampled data, and estimate these from a sample.
-* Define the following statistical sampling terms: population, sample, population parameter, point estimate, and sampling distribution.
-* Explain the difference between a population parameter and a sample point estimate.
-* Use R to draw random samples from a finite population.
-* Use R to create a sampling distribution from a finite population.
-* Describe how sample size influences the sampling distribution.
-* Define bootstrapping.
-* Use R to create a bootstrap distribution to approximate a sampling distribution.
-* Contrast the bootstrap and sampling distributions.
+- Describe real-world examples of questions that can be answered with statistical inference.
+- Define common population parameters (e.g., mean, proportion, standard deviation) that are often estimated using sampled data, and estimate these from a sample.
+- Define the following statistical sampling terms: population, sample, population parameter, point estimate, and sampling distribution.
+- Explain the difference between a population parameter and a sample point estimate.
+- Use R to draw random samples from a finite population.
+- Use R to create a sampling distribution from a finite population.
+- Describe how sample size influences the sampling distribution.
+- Define bootstrapping.
+- Use R to create a bootstrap distribution to approximate a sampling distribution.
+- Contrast the bootstrap and sampling distributions.
 
 ## Why do we need sampling?
 We often need to understand how quantities we observe in a subset

source/regression1.Rmd

@@ -63,15 +63,15 @@ however that is beyond the scope of this book.
 ## Chapter learning objectives
 By the end of the chapter, readers will be able to do the following:
 
-* Recognize situations where a regression analysis would be appropriate for making predictions.
-* Explain the K-nearest neighbors (K-NN) regression algorithm and describe how it differs from K-NN classification.
-* Interpret the output of a K-NN regression.
-* In a data set with two or more variables, perform K-nearest neighbors regression in R.
-* Evaluate K-NN regression prediction quality in R using the root mean squared prediction error (RMSPE).
-* Estimate the RMSPE in R using cross-validation or a test set.
-* Choose the number of neighbors in K-nearest neighbors regression by minimizing estimated cross-validation RMSPE.
+- Recognize situations where a regression analysis would be appropriate for making predictions.
+- Explain the K-nearest neighbors (K-NN) regression algorithm and describe how it differs from K-NN classification.
+- Interpret the output of a K-NN regression.
+- In a data set with two or more variables, perform K-nearest neighbors regression in R.
+- Evaluate K-NN regression prediction quality in R using the root mean squared prediction error (RMSPE).
+- Estimate the RMSPE in R using cross-validation or a test set.
+- Choose the number of neighbors in K-nearest neighbors regression by minimizing estimated cross-validation RMSPE.
 - Describe underfitting and overfitting, and relate it to the number of neighbors in K-nearest neighbors regression.
-* Describe the advantages and disadvantages of K-nearest neighbors regression.
+- Describe the advantages and disadvantages of K-nearest neighbors regression.
 
 ## The regression problem
 

source/regression2.Rmd

@@ -51,10 +51,10 @@ predictor.
 ## Chapter learning objectives
 By the end of the chapter, readers will be able to do the following:
 
-* Use R to fit simple and multivariable linear regression models on training data.
-* Evaluate the linear regression model on test data.
-* Compare and contrast predictions obtained from K-nearest neighbors regression to those obtained using linear regression from the same data set.
-* Describe how linear regression is affected by outliers and multicollinearity.
+- Use R to fit simple and multivariable linear regression models on training data.
+- Evaluate the linear regression model on test data.
+- Compare and contrast predictions obtained from K-nearest neighbors regression to those obtained using linear regression from the same data set.
+- Describe how linear regression is affected by outliers and multicollinearity.
 
 ## Simple linear regression
 

source/wrangling.Rmd

@@ -21,29 +21,29 @@ application, providing more practice working through a whole case study.
 
 By the end of the chapter, readers will be able to do the following:
 
-  - Define the term "tidy data".
-  - Discuss the advantages of storing data in a tidy data format.
-  - Define what vectors, lists, and data frames are in R, and describe how they relate to
-    each other.
-  - Describe the common types of data in R and their uses.
-  - Use the following functions for their intended data wrangling tasks:
-      - `c`
-      - `pivot_longer`
-      - `pivot_wider`
-      - `separate`
-      - `select`
-      - `filter`
-      - `mutate`
-      - `summarize`
-      - `map`
-      - `group_by`
-      - `across`
-      - `rowwise`
-  - Use the following operators for their intended data wrangling tasks:
-      - `==`, `!=`, `<`, `<=`, `>`, and `>=`
-      - `%in%`
-      - `!`, `&`, and `|`
-      - `|>` and `%>%`
+- Define the term "tidy data".
+- Discuss the advantages of storing data in a tidy data format.
+- Define what vectors, lists, and data frames are in R, and describe how they relate to
+  each other.
+- Describe the common types of data in R and their uses.
+- Use the following functions for their intended data wrangling tasks:
+    - `c`
+    - `pivot_longer`
+    - `pivot_wider`
+    - `separate`
+    - `select`
+    - `filter`
+    - `mutate`
+    - `summarize`
+    - `map`
+    - `group_by`
+    - `across`
+    - `rowwise`
+- Use the following operators for their intended data wrangling tasks:
+    - `==`, `!=`, `<`, `<=`, `>`, and `>=`
+    - `%in%`
+    - `!`, `&`, and `|`
+    - `|>` and `%>%`
 
 ## Data frames, vectors, and lists