# Prediction and Regression

## Prediction-

Prediction is the technique use for predict a desired value from desired data set The prediction used regression method for displaying result of predicted values. The predicted can define with 2 methods-In first method the predicted algorithm choose a descriptive data for predict a value and in second method the predicted algorithms select current data for predict a values. The lots of techniques are used for prediction techniques that are as follows-

- Nearest neighbour
- Natural network
- Bayes classifier
- Decision tree

## Regression-

The regulation means that calculating a predicted values with only on numeric data. Regression used the statistical method or technique for finding prediction values. The regression can use relationship between one or more independent variables and find a final result. The scalability of a regression is depend upon which type of data are in data set.

Several software packages are used in regression method for problem solving

**SAS****SPSS****SPLUS**

The regression analysis can done by using following methods

**Linear or multiple regression****Non linear regression**

### Linear or multiple regression-

**1. Linear Regression-**

In linear regression the data are model using straight line. It is the simplest form of regression for finding a prediction value.

Y=α + β

That equation are used for calculating linear or multiple relation.

**2. Multiple Regression-**

In multiple regression is the extension of linear regression program. They can involving more than one predicted variable based on two predicted distributed variables.

Y=α +β1X1+β2X2

That equation are used for calculating multiple regression method.

**3. Non-Linear Regression-**

In non linear regression the predicted value are not in fixed format. In that non-linear model they can be using polynomial functions for polynomial regression method for finding a prediction values.

Y=α +β1X1+β2X2+β3X3

That equation are used for calculating multiple regression method.