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Submitted By AnikAhmed

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Definition: Regression is used to examine the relationship between one dependent and one independent variable. After performing an analysis, the regression statistics can be used to predict the dependent variable when the independent variable is known. Regression goes beyond correlation by adding prediction capabilities.

Types Of Regression Analysis: Most widely used two types of regression analysis are- I [pic]

Linear Regression Analysis: When the regression is conducted by two variables or factors then is called linear regression analysis.

Multiple regression analysis: Multiple regression analysis is a technique for explanation of occurrence and calculation of future actions. A coefficient of correlation among variables X also Y is a quantitative index of connection involving these two variables. In squared type, while a coefficient of purpose specifies the quantity of difference in the principle variable Y that is accounted for through the deviation in the analyst variable X.

[pic][pic][pic][pic]Examples for Linear Regression Analysis:

ABC a manufacturing co. where the production cost depends on their raw materials cost. Now, For the given set of x(tk in million) and y ( tk in thousand per unit) values, determine the Linear Regression and also find the slope and intercept and use this in a regression equation.

|X |Y |

|50 |4.2 |

|51 |3.1 |

|52 |5.1 |

Solution:

Step 1: let the numebr of values N = 3

Step 2: determine the values for XY, X2

|X |Y |X*Y |X*X |

|50 |4.2 |210 |2500 |

|51 |3.1 |158.1…...

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