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WorksheetsLinearRegression
Total questions: 10
Worksheet time: 10mins
1. What is the primary objective of Linear Regression in machine learning?
A) To find clusters
in data
B) To minimize the
Euclidean distance between points
C) To predict continuous values
based on input features
D) To classify data
into distinct categories
2. In Linear Regression, what does the term "residual" refer to?
A) The difference between the
predicted value and the actual value
B) The slope of the
regression line
C) The intercept of
the regression line
D) The total sum of squared errors
3. Which of the following is the cost function used in Linear Regression?
A) Cross-Entropy Loss
B) Mean Absolute Error
C) Mean Squared Error
D) Hinge Loss
4. In Linear Regression, the relationship between
input variables and output are assumed to be:
A) Linear
B) Exponential
C) Polynomial
D) Logarithmic
5. Which method is commonly used to
find the best-fit line in Linear Regression?
A) Gradient Descent
B) Decision Trees
C) Random Forests
D) Backpropagation
6. In simple linear regression, the equation of
the line is represented as y=mx+cy = mx + cy=mx+c.
What does "m" represent in this equation?
A) The y-intercept
of the line
B) The residuals of
the data points
C) The slope
of the line
D) The output
variable
7. Which of the following is NOT an
assumption of Linear Regression?
A) Linearity of
the relationship
B) Homoscedasticity
C) Multicollinearity
D) Independence
of errors
8. What is R-squared in the
context of Linear Regression?
A) A metric that evaluates
how well the model
fits the data
B) A measure of the
error rate of predictions
C) The slope of the
regression line
D) The p-value of
the model
9. If R-squared is equal to 1 in a
Linear Regression model, what does it indicate?
A) The model does
not fit the data well
B) The model perfectly
explains the
variance in the data
C) There is no linear
relationship in the data
D) The data is
overfitting the model
10. Which of the following is TRUE about
Linear Regression with multiple variables?
A) It is called
simple linear regression
B) It is known as
multiple linear regression
C) It is known as
logistic regression
D) It is not
possible in linear regression
