Regression

                                                Regression

Ins statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed.

·                 Regression analysis is widely used for prediction and forecasting

·          Regression analysis can be used to infer causal effect relationships between the independent and dependent           variables. However this can lead to illusions or false relationships, so caution is advisable.

 Types of Regression

The two basic types of regression are linear regression and multiple linear regression, although there are non-linear regression methods for more complicated data and analysis. Linear regression uses one independent variable to explain or predict the outcome of the dependent variable Y, while multiple regression uses two or more independent variables to predict the outcome.

Regression can help finance and investment professionals as well as professionals in other businesses. Regression can help predict sales for a company based on weather, previous sales, GDP growth or other conditions. The capital asset pricing model (CAPM) is an often-used regression model in finance for pricing assets and discovering costs of capital. The general form of each type of regression is;

·         Linear Regression: Y = a + bX

·         Multiple Regression: Y = a + b₁X₁ ⁺ b₂X₂ + b₃X₃ + ... + b_tX_t + u

Where:

Y = the variable that you are trying to predict (dependent variable)

X = the variable that you are using to predict Y (independent variable

Linear regression Microsoft excel; steps

Step 1; install the data analysis Toolpak

Step 2; type your data in two columns in excel. For example, type your x data into column A and your y data in column B. do not leave any blank cell between your entries.

Step 3; click data analysis tab on excel toolbar.

Step 4; click regression in the pop up window and then click ok.

Step 5; select your input x range by selecting the data in the worksheet or typing the location in your data into x range box. For example, if your x data is in A2 through A10 then type A2:A10 in to the x output range box.

Step 6; selecting your input y range box by selecting the data in the worksheet or typing the location in your data into y range box.

Step 7; select your location where you are want your output range to go by selecting to the data in to the worksheet or typing the location of where you want your data go in the output range box.

Step 8; click ok excel will calculate linear regression and populate your worksheet with the result.