Linear regression quiz 2

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

• Least Square Error

• Maximum Likelihood

• Logarithmic Loss

• Both A and B

Tags

NGSS.HS-LS3-3

Which of the following statement is true about outliers in Linear regression?

• Linear regression is sensitive to outliers

• Linear regression is not sensitive to outliers

• Can’t say

• None of these

Suppose that you have a dataset D1 and you design a linear regression model of degree 3 polynomial and you found that the training and testing error is small or in another terms it almost perfectly fits the data.

What will happen when you fit degree 4 polynomial in linear regression?

• There are high chances that degree 4 polynomial will over fit the data

• There are high chances that degree 4 polynomial will under fit the data

• Can’t say

• None of these

Suppose that you have a dataset D1 and you design a linear regression model of degree 3 polynomial and you found that the training and testing error is small or in another terms it almost perfectly fits the data.

What will happen when you fit degree 2 polynomial in linear regression?

• It is high chances that degree 2 polynomial will over fit the data

• It is high chances that degree 2 polynomial will under fit the data

• Can’t say

• None of these

If a degree 3 polynomial fits the data perfectly, it’s highly likely that a simpler model(degree 2 polynomial) might under fit the data.

Suppose, you got a situation where you find that your linear regression model is under fitting the data.

1) Add more variables

2) Start introducing polynomial degree variables

3) Remove some variables

• 1 and 2

• 2 and 3

• 1 and 3

• 1, 2 and 3

Suppose you have to predict the salary of an employee from their years of experience where the dataset has a salary range from 10000 to 50000. In which of the intervals your regressive model should predict?

• 10000 to 20000

• 10000 to 40000

• 25000 to 50000

• 10000 to 50000

In simple linear regression, if you change the input value by 1 then output value will be changed by:

• 1

• The slope parameter

• The intercept parameter

• None

You can compute the residual by

• actual y‐coordinate value - predicted y‐coordinate value

• predicted y‐coordinate value - actual y coordinate value

• actual y‐coordinate value / predicted y‐coordinate value

• None

Tags

NGSS.HS-LS3-3

What would be the best regression model for more than one independent variable and continuous dependent variable?

• Simple Linear Regression

• Multiple Linear Regression

• Logistic Regression

• All of the Above

Suppose you got a training accuracy of 90% and a test accuracy of 50%. What happened with your model

• The model was over fitted with the training data

• The model was under fitted with the training data

• The model is absolutely fine

• None