Question

True or False:

-Linear regression is one of the least commonly used regression techniques.

-The difference between simple linear regression and multiple linear regression is that, multiple linear regression has (>1) independent variables, whereas simple linear regression has only 1 independent variable.

-Least Square Method calculates the best-fit line for the observed data by minimizing the sum of the squares of the horizontal deviations from each data point to the line.

-We can evaluate the model performance using the metric R-square.

-Logistic regression is used to find the probability of event=Success and event=Failure.

Answer 1

1. Linear regression is one of the least commonly used regression techniques: False.

2. The difference between simple linear regression and multiple linear regression is that, multiple linear regression has (>1) independent variables, whereas simple linear regression has only 1 independent variable: True.

3. Least Square Method calculates the best-fit line for the observed data by minimizing the sum of the squares of the horizontal deviations from each data point to the line: False

(It depends on the direction of dependent variable i.e. if dependent variable is in vertical direction and we denote it by "y" then error (i.e. y-hat(y)) is also in vertical direction so in that situation Least Square Method calculates the best-fit line for the observed data by minimizing the sum of the squares of the vertical deviations from each data point to the line).

4. We can evaluate the model performance using the metric R-square: True (as value of R-square increases then goodness of fit also increases).

5. Logistic regression is used to find the probability of event=Success and event=Failure: False. (Logistic regression is used when the response is binary).