Regression analysis is a statistical technique that develop a relationship between explanatory (independent) variable and response (dependent) variable. It measures the dependence of one (dependent) variable on one or more than one other (independent) variable.

Linear regression |

**Introduction of regression analysis**

### Objectives of Regression analysis

- Estimate the relationship between explanatory and response variable.
- Determine the effect of each of the explanatory variables on the response variable.
- Predict the value of the response variable for a given value of explanatory variable.

### Types of regression analysis

- Linear Regression
- Logistic Regression
- Polynomial Regression
- Stepwise Regression
- Ridge Regression
- Lasso Regression
- ElasticNet Regression

- Linear regression model
- Multiple linear regression model
- Polynomial regression model

### Linear regression model

### Multiple linear regression model

### Polynomial regression model

- A company might wish to improve its marketing process. After collecting data on the demand for a product, the product’s price and the advertising expenditure incurred in promoting the product, the company might use regression analysis to develop an equation to predict the future demand on the basis of price and advertising.
- A real state company fixes the selling price of its apartments, as it claims, on the basis of the size of the apartments measured in terms of square footage of living space. A sample of 20 apartments was chosen and the apartment owners were asked to report the size of their apartments and the price they paid. On basis of this information, a regression analysis may be undertaken to see if there is any basis of such claim of the company and to make prediction of the price for a specified floor space.
- A physician collected blood sample from 50 infants on pulmonary blood flow (PBF) and pulmonary blood volume (PBV) to examine if there is any relationship between PBF and PBV. A linear regression analysis seems appropriate for purpose to see if there is any such relationship.

### What is standard deviation in regression model?

This is common question that everybody asked about regression. In regression model the differences between the regression line and the data at each value of the independent variable is called standard deviation.

And more simply

standard deviation= Root of Mean Square Error(RMSE)

How to analyze data using SPSS (for beginners)-part 1

How to analyze data using SPSS (part-2), how to input data in SPSS

How to analyze data using SPSS (part-3), how to import data in SPSS from excel file__How to analyze data using SPSS (part-4), how to sort data in SPSS____How to analyze data using SPSS ( part-5) how to merge file in SPSS?__

How to analyze data using SPSS ( part-6), how to merge file in SPSS

How to analyze data using SPSS (part-7), Finding missing values, Replacing missing values, Coding missing values in SPSS

How to analyze data using SPSS (part-8), variable transformation, Recoding variables in spssUnivariate Analysis (data analysis using spss part-9)

Bivariate analysis| How to analyze data using spss (part-10)

Normality check| how to analyze data using spss (part-11)