Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding.

*Regression is the study of dependence — A Predictive modelling technique*

- It attempts to find the relationship between a
variable “Y” and an*DEPENDENT*variable “X”.*INDEPENDENT* - (
*Note: Y should be a continuous variable while X can be categorical or continuous)* - There are two types of regression —_ Simple Linear Regression and Multiple Linear Regression._
*Simple linear regression*will have**one independent variable**(predictor).*Multiple linear regression*will have**more than one independent variable**(predictors).- In a nutshell — Linear Regression maps a continuous X to a continuous Y.

- To determine strength of independent variables (predictors)

— *Example*: *Relationship between Age & Income*

2. To forecast effects

— *Example: Effect on sale income for 1000$ spent on marketing*

3. To forecast trends

*— Example: Predicting price of bitcoin in the next 6 months*

**Classification & Regression Capabilities:**

- Regression models predict continuous variables (Eg: Predict the temperature of a city)
- Once it is known that the aim is to classify data — we choose
*Logistic Regression.* - Linear Regression is not suitable for classification because “*
*the idea of fitting a straight line in case of a polynomial is a challenging task. **”

**2. Data Quality:**

- Each missing value removes one data point that could optimize the regression.
- In simple linear regression, the outliers can significantly disturb the outcome. (
*i.e. removing outliers enhances the model greatly*)

**3. Computational Complexity:**

- It is not expensive computation-wise as compared to decision tree (or) clustering.

**4. Comprehensible & Transparent:**

- Easy to comprehend and understand

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Regression(Data Science Part 6) Linear Regression with Math (6.1) ... Now, we will understand all parts and types of regression in detail.