Understanding Linear Regression in Python

What is linear regression?

• A way to visualize data using scatter plots.

• Linear regression is a method for modeling the relationship between a dependent variable and one or more independent variables using a linear equation.

• A statistical technique for analyzing time series data.

• A method for predicting categorical outcomes using a decision tree.

What is the purpose of the linear regression model?

• To visualize data trends in a graph.

• To predict the value of a dependent variable based on the values of independent variables.

• To classify data into distinct categories.

• To calculate the mean of a dataset.

How do you import the necessary libraries for linear regression in Python?

• import numpy as np from sklearn.linear_model import LinearRegression import pandas as pd

• from numpy import array

• import sklearn as sk

• import matplotlib.pyplot as plt

What function is commonly used to fit a linear regression model in Python?

• LogisticRegression

• PolynomialRegression

• RidgeRegression

• LinearRegression

What is the difference between simple and multiple linear regression?

• Simple linear regression uses one predictor; multiple linear regression uses multiple predictors.

• Simple linear regression is more complex than multiple linear regression.

• Multiple linear regression requires no predictors at all.

• Simple linear regression can only be used for categorical data.

How do you interpret the coefficients in a linear regression model?

• The coefficients indicate the expected change in the dependent variable for a one-unit increase in the independent variable.

• The coefficients indicate the correlation between the dependent and independent variables.

• The coefficients show the average of all independent variables combined.

• The coefficients represent the total value of the independent variable.

What is the significance of the R-squared value in linear regression?

• The R-squared value is used to determine the sample size needed for the study.

• The R-squared value indicates the accuracy of the predictions.

• The R-squared value signifies the proportion of variance explained by the model.

• The R-squared value measures the slope of the regression line.

What assumptions must be met for linear regression analysis?

• Linearity, dependence, homoscedasticity, normality, no correlation.

• Non-linearity, dependence, heteroscedasticity, uniformity, high multicollinearity.

• Linearity, independence, homoscedasticity, randomness, no multicollinearity.

• Linearity, independence, homoscedasticity, normality, no multicollinearity.

How can you visualize the results of a linear regression model?

• Create bar graphs for each feature's importance.

• Use scatter plots with regression lines and residual plots.

• Utilize line graphs to show time series data.

• Use pie charts to display the coefficients.

What is the purpose of the train-test split in linear regression?

• To ensure all data points are used for training.

• To reduce the size of the dataset.

• To evaluate the model's performance and generalization ability.

• To increase the model's complexity.