This quiz covers the fundamental concepts important to using linear regression, the types of problems this method can be applied to, and how to implement it using Scikit-learn.

Linear Regression with Scikit-learn

Which of the following best describe a standard normal distribution?

Which two plots or graphs best show the distribution of values of a continuous numeric variable?

Correlation is best described by which of the following?

Which of the following plot types is the most useful for showing the correlation of one continuous variable with another continuous variable? 

What are the possible reasons that variables within a data set are correlated?  

If two variables are correlated, it always means one effect caused the other.  

Including two input variables that are closely related (eg: speed(mph) and speed(kph)) will help your ML model be more accurate.  

Referring to the graphs below, which two show the highest correlation?


<img src="https://i.imgur.com/Xybx0Vk.png" align="left" width=400>

Which of the following statements are true about collinearity?  (Select all that apply)

Why is correlation analysis important before training our linear regression model?  (Select all that apply)

A multivariate linear regression model can have _________. (Select all that apply)

A univariate linear regression model can have which of the following?  (Select all that apply)

Linear regression is a ___________ machine learning technique.  (Select all that apply)


Linear Regression is best for ________. (Select the best solution)

Which of the following expressions are synonymous to "Independent variable"?  (Select all that apply)

The word "optimization" in machine learning is best described by which of the following?  

When fitting large data sets using a linear regression model, what is the preferred optimization method?  

What is the loss function used in standard linear regression?

The loss function in machine learning should always _________. (Select the best answer)

Outliers in the data usually make the linear regression predictions less accurate.

Of the following evaluation metrics, which penalizes the presence of outliers **the least**

Data can be classified as an outlier if________. (Select all that apply)

In linear regression analysis which of the following statements is true? 

In Scikit-learn, the preprocessing library contains functions that can do which of the following: (Select all that apply)


Standard Scaling in the Scikit-learn preprocessing module does which of the following? (Select all that apply)

True or False?  If the input variables are scaled, the target variable should be scaled as well?  

Encoding refers to which of the following?  

If you have an input column “colors” that have values of either “red”, “green”, “blue”, “yellow”, the best way to preprocess this data for Scikit-learn would be to use __________.

Assume you have run the following lines of code: 

 	 from sklearn.linear_model import LinearRegression 
 	 model = LinearRegression() 

Then model.fit() will perform which of the following steps? (Select all that apply)

If your linear regression model takes in three independent variables (3 columns of input data), how many total values (ie: parameters) are calculated by model.fit()? 

What are model "parameters" in a linear regression model?  

True of False?  Computing the accuracy of the model using the Scikit-learn metrics.accuracy function is the best way to evaluate a linear regression model.

Put the following steps in the order that you would do them (Once you have your data):
1. Split data into train and test sets
2. Call model.predict on validation or test data and evaluate error
3. Encode categorical values and scale numeric values
4. Exploratory data analysis
5. Call model.fit on the training data
6. Define an instance of the chosen model type


Which of the following are the assumptions of linear regression?


What is the underlying method which is used to fit the training data in the algorithm of linear regression?

Which is the technique we use to perform the task of variable selection?

Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data.

You found that correlation coefficient for one of it’s variable(Say X1) with Y is -0.95.

Which of the following is true for X1?

Which of the following methods do we use to find the best fit line for data in Linear Regression?

Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?

In a simple linear regression problem, a single input variable (x) and a single output variable (y), the linear equation would be y = ax + b; where a and b are _______ and ________ respectively.

In a linear regression model, which technique can find the coefficients/Weights?

Which parameter determines the size of the improvement step to take on each iteration of Gradient Descent?

For a Linear Regression model, we choose the coefficients/weights and the bias term by minimizing the _____.



One of the major assumptions of linear regression: 
When the error in variance is constant for all values of the predictor variable is called ______?


What is the Null Hypothesis in regression?

A term used to describe the case when the predictors in a multiple regression model are correlated is called:

Which of the following is true regarding residual in Linear Regression?

 Which method is used to find the best fit line for linear regression?

Test your knowledge of the Linear Regression Machine Learning Algorithm using Scikit-learn 