Portfolio managementTopic: Regression and correlation analysis in

forecasting revenues and expenses

Presented to

venkatesh sir Faculty of commerce Dos in commerce

ContentsIntroductionMeaning and definitionAssumptionFormula conclusionreferences

Introduction Regression analysis is one of the most commonly used

statistical techniques in social and behavioral sciences as well as in physical sciences. Its main objective is to explore the relationship between a dependent variable and one or more independent variables (which are also called predictor or explanatory variables). Linear regression explores relationships that can be readily described by straight lines or their generalization to many dimensions.

A surprisingly large number of problems can be solved by regression, and even more by means of transformation of the original variables that result in linear relationships among the transformed variables

Meaning of Regression analysisRegression analysis the use of regression to

make quantitative predictions of one variable from the value

The dictionary meaning of regression is “the act of returning or going back”;

First used in 1877 by Francis Galton; Regression is the statistical tool with the help of

which we are in a position to estimate (predict) the unknown values of one variable from the known values of another variable;

It helps to find out average probable change in one variable given a certain amount of change in another; s of another

Definition“Regression analysis is a mathematical measure of the average relationship between two or more variables in terms of the original units of data”

- M. M. Blair

AssumptionsThe regression model is based on the

following assumptions.The relationship between X and Y is linear.The expected value of the error term is zeroThe variance of the error term is constant for

all the values of the independent variable, X. This is the assumption of homoscedasticity.

There is no autocorrelation. E (e ie j) =0.The independent variable is uncorrelated

with the error term.The error term is normally distributed.

Formula

Y = a + bx + εWhere: Y = dependent variable; X = independent variable, a = intercept of regression line; b = slope of regression line, ε = error term

The horizontal line is called the X-axis and the vertical line the Y-axis.

Regression analysis looks for a relationship between the X variable (sometimes called the “independent” or “explanatory” variable) and the Y variable (the “dependent "variable).

For exampleX might be the aggregate level of personal

disposable income in the United States and Y would represent personal consumption expenditures in the United States, an example used in Guerard and Schwartz (2007).

By looking up these numbers for a number of years in the past, we can plot points on the graph. More specifically, regression analysis seeks to find the “line of best fit” through the points.

Example RegressionSituation Company A wants to know the

relationship between the Man Hour of their sales force and their sales number

They have collected their sales data and the man hour put in during the collection period

Company A Data Company Sales

Man Hour

6 3

8 4

9 6

5 4

4.5 2

9.5 5

Finding the Regression CompanyA is trying to predict its sales from the man

hour spent Y = Sales X = Man The line in is the one that minimizes the

errors Hour Error = (Actual value) – (Predicted

value)

REGRESSION ANALYSIS USING SPSSThe REGRESSION command is called in SPSS as

follows:

Cont…..Selecting the following options will command the program to do a

simple linear regression and create two new variables in the data editor: one with the predicted values of Y and the other with the

residuals.

The output from the preceding includes the correlation coefficient and standard error of estimate

The regression coefficients are also given in the output.

The optional save command generates two new variables in the data file.

conclusionIf you've ever wondered how two or more things

relate to each other, or if you've ever had your boss ask you to create a forecast or analyze relationships between variables, then learning regress on would be worth your time. In the field of business regression is widely used. Businessman is interested in predicting future production, consumption, investment, prices, profits, sales etc. So the success of a businessman depends on the correctness of the various estimates that he is required to make. It is also use in sociological study and economic planning to find the projections of population, birth rates. Death rates etc.

Referenceswww.google.comSECURITY ANALYSIS AND PORTFOLIO MANAGEMENT

By S. KEVIN

PrakashaMfm 1s year

THANK

YOU….