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Regression Quiz

Total questions: 10

Worksheet time: 3mins

Name
Class
Date
1.

What is the primary purpose of regression analysis in machine learning?

a)

Classifying images

b)

Predicting continuous variables

c)
  • Reducing dataset dimensions

d)

Clustering similar data

2.

Which of the following is a dependent variable also known as?

a)

Explanatory variable

b)

Covariate

c)

Target variable

d)

Domain point

3.

What does the regression model aim to establish?

a)
  • Relationship between independent variables

b)
  • Relationship between an independent and a dependent variable

c)

Clusters of input data

d)

Feature importance only

4.

What is the main difference between correlation and causation?

a)

Correlation implies cause and effect

b)

Correlation is random, causation is systematic

c)

Correlation is a relationship; causation implies one variable affects another

d)

Causation is used in unsupervised learning

5.

Which plot is commonly used to visually examine correlation?

a)

Bar graph

b)

Scatter plot

c)

Pie chart

d)

Histogram

6.

When is Ridge Regression particularly useful?

a)

When features are categorical

b)

When there is multicollinearity

c)

When outliers are present

d)

When data is missing

7.

What is the effect of Lasso Regression on model coefficients?

a)

Increases all coefficients

b)

Makes coefficients zero to select features

c)

Converts coefficients into binary

d)

Has no effect

8.

What kind of model does Polynomial Regression create?

a)

A logistic function

b)

A straight-line model

c)
  • An nth-degree polynomial curve

d)

A tree-like structure

9.

Which type of regression is used when the relationship between the variables can be represented as a straight line?

a)

Logistic Regression

b)
  • Polynomial Regression

c)
  • Linear Regression

d)

Ridge Regression

10.

What distinguishes multiple regression from simple linear regression?

a)

It predicts multiple dependent variables

b)
  • It uses multiple dependent and independent variables

c)

It uses two or more independent variables

d)

It fits a non-linear curve to the data