fixing whitespace in Rmd so diff of errata is cleaner (#46)

* fixing whitespace in Rmd so diff of errata is cleaner

* reapply kwargs fix

Ch03-linreg-lab.Rmd

@@ -1,4 +1,3 @@
-
 # Linear Regression
 
 <a target="_blank" href="https://colab.research.google.com/github/intro-stat-learning/ISLP_labs/blob/v2.2/Ch03-linreg-lab.ipynb">
@@ -19,7 +18,7 @@ import pandas as pd
 from matplotlib.pyplot import subplots
 
 ```
- 
+
 
 ### New imports
 Throughout this lab we will introduce new functions and libraries. However,
@@ -95,7 +94,7 @@ A.sum()
 
 ```
     
- 
+
 
 ## Simple Linear Regression
 In this section we will  construct model 
@@ -117,7 +116,7 @@ Boston = load_data("Boston")
 Boston.columns
 
 ```
-    
+
 Type `Boston?` to find out more about these data.
 
 We start by using the `sm.OLS()`  function to fit a
@@ -132,7 +131,7 @@ X = pd.DataFrame({'intercept': np.ones(Boston.shape[0]),
 X[:4]
 
 ```
-    
+
 We extract the response, and fit the model.
 
 ```{python}
@@ -154,7 +153,7 @@ method, and returns such a summary.
 summarize(results)
 
 ```
-    
+
 
 Before we describe other methods for working with fitted models, we outline a more useful and general framework for constructing a model matrix~`X`.
 ### Using Transformations: Fit and Transform
@@ -225,8 +224,8 @@ The fitted coefficients can also be retrieved as the
 results.params
 
 ```
-    
- 
+
+
 The `get_prediction()`  method can be used to obtain predictions, and produce confidence intervals and
 prediction intervals for the prediction of  `medv`  for  given values of  `lstat`.
 
@@ -396,7 +395,7 @@ terms = Boston.columns.drop('medv')
 terms
 
 ```
- 
+
 We can now fit the model with all the variables in `terms` using
 the same model matrix builder.
 
@@ -407,7 +406,7 @@ results = model.fit()
 summarize(results)
 
 ```
- 
+
 What if we would like to perform a regression using all of the variables but one?  For
 example, in the above regression output,   `age`  has a high $p$-value.
 So we may wish to run a regression excluding this predictor.
@@ -482,7 +481,7 @@ model2 = sm.OLS(y, X)
 summarize(model2.fit())
 
 ```
- 
+
 
 ## Non-linear Transformations of the Predictors
 The model matrix builder can include terms beyond
@@ -557,7 +556,7 @@ there is little discernible pattern in the residuals.
 In order to create a cubic or higher-degree polynomial fit, we can simply change the degree argument
 to `poly()`.
 
- 
+
 
 ## Qualitative Predictors
 Here we use the  `Carseats`  data, which is included in the