This document discusses different types of regression analysis. It defines linear regression as relating a continuous dependent variable to one or more independent variables using a linear equation. Linear regression can be simple or multiple. Logistic regression relates a binary dependent variable to independent variables that can be continuous or binary using probabilities. The key differences between linear and logistic regression are that linear regression estimates a continuous output using ordinary least squares while logistic regression estimates a constant binary output using maximum likelihood estimation. Overfitting and underfitting are also discussed as potential issues for regression models.

1. Regression Analysis
-YASH BHANGARE
-BECOA110

2. Regression analysis gives the relationship between dependent variable and one
& more independent variable.
Linear regression:
Dependent variable is continuous in
nature.
Generalized model used to predict
categorical variable.
Linear Regression Equation;
Y = mX + C

3. Linear Regression :----Simple Linear Regression
----Multi-Linear Regression
1 independent variable and 1 dependent variable
More than 1 independent variable and 1 dependent variable
Y = β1 + β2X2 + β3X3 + …… + βkXk + ε
-----where,
Y = dependent variable
X = independent variable
β1 = Regression coefficients
Ε = Error term
β

4. ● When actual means of x & y are given,
● When assumed means of x & y are given,

5. Logistic Regression:
Dependent variable is binary in nature and independent variable can be
continuous or binary.
When more than two categories are involved in dependent variable - multinominal
logistic regression.

6. Difference between Linear regression & Logistic regression
Sr.no. Linear Regression Logistic Regression
1 y= a + bX y = e^(b0 + b1*x) / (1 + e^(b0 + b1*x))
2 Works with single independent
variable
Works with more than one variable
3 Estimated using OLS Estimated using MLS
4 Continuous output Constant output
5 Predicting housewife or stock price Predicting whether a patient has disease
or not

7. Overfitting occurs when algorithm captures the noise of data.
Overfitting - algo works well on training set and fails on testing dataset.
Underfitting occurs when algorithm cannot captures the underlying trend of data.
Underfitting - algo failed to work on both training and testing dataset.

8. Regression Models
❏ Polynomial regression
❏ Ridge regression
❏ Lasso regression
❏ Elasticnet Regression

9. Thank You