stars to dashes

Author: trevorcampbell

source/clustering.md

@@ -39,16 +39,16 @@ including techniques to choose the number of clusters.
 
 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 Python.
-* Perform K-means clustering in Python using `scikit-learn`.
-* Use the elbow method to choose the number of clusters for K-means.
-* Visualize the output of K-means clustering in Python using a colored scatter plot.
-* Describe 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 Python.
+- Perform K-means clustering in Python using `scikit-learn`.
+- Use the elbow method to choose the number of clusters for K-means.
+- Visualize the output of K-means clustering in Python using a colored scatter plot.
+- Describe advantages, limitations and assumptions of the K-means clustering algorithm.
 
 
 ## Clustering

source/inference.md

@@ -36,16 +36,16 @@ populations and then introduce two common techniques in statistical inference:
 
 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 Python to draw random samples from a finite population.
-* Use Python to create a sampling distribution from a finite population.
-* Describe how sample size influences the sampling distribution.
-* Define bootstrapping.
-* Use Python 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 Python to draw random samples from a finite population.
+- Use Python to create a sampling distribution from a finite population.
+- Describe how sample size influences the sampling distribution.
+- Define bootstrapping.
+- Use Python to create a bootstrap distribution to approximate a sampling distribution.
+- Contrast the bootstrap and sampling distributions.
 
 +++
 

source/regression1.md

@@ -51,15 +51,15 @@ 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 Python.
-* Evaluate K-NN regression prediction quality in Python using the root mean squared prediction error (RMSPE).
-* Estimate the RMSPE in Python 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 Python.
+- Evaluate K-NN regression prediction quality in Python using the root mean squared prediction error (RMSPE).
+- Estimate the RMSPE in Python 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.
 
 +++
 

source/regression2.md

@@ -38,10 +38,10 @@ predictor.
 ## Chapter learning objectives
 By the end of the chapter, readers will be able to do the following:
 
-* Use Python 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 Python 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.
 
 +++