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Welcome to the Visualizing Data and Linear Regression MCQs Page

Dive deep into the fascinating world of Visualizing Data and Linear Regression with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Visualizing Data and Linear Regression, a crucial aspect of R Programming. In this section, you will encounter a diverse range of MCQs that cover various aspects of Visualizing Data and Linear Regression, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within R Programming.

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Check out the MCQs below to embark on an enriching journey through Visualizing Data and Linear Regression. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of R Programming.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Visualizing Data and Linear Regression. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Visualizing Data and Linear Regression MCQs | Page 4 of 7

Q31.
Which of the following metrics can be used for evaluating regression models?
i) R Squared
ii) Adjusted R Squared
iii) F Statistics
iv) RMSE / MSE / MAE
Discuss
Answer: (d).i, ii, iii and iv
Q32.
How many coefficients do you need to estimate in a simple linear regression model (One independent variable)?

a.

1

b.

2

c.

3

d.

4

Discuss
Answer: (b).2
Q33.
In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change?
Discuss
Answer: (d).by its slope
Q34.
Function used for linear regression in R is __________
Discuss
Answer: (a).lm(formula, data)
Q35.
In syntax of linear model lm(formula,data,..), data refers to ______
Discuss
Answer: (b).Vector
Q36.
In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to __________
Discuss
Answer: (c).(Y-Intercept, Slope)
Q37.
________ is an incredibly powerful tool for analyzing data.
Discuss
Answer: (a).Linear regression
Q38.
The square of the correlation coefficient r 2 will always be positive and is called the ________
Discuss
Answer: (b).Coefficient of determination
Q39.
Predicting y for a value of x that’s outside the range of values we actually saw for x in the original data is called ___________
Discuss
Answer: (b).Extrapolation
Q40.
What is predicting y for a value of x that is within the interval of points that we saw in the original data called?
Discuss
Answer: (c).Intra polation
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