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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….