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Regression and correlation analysis in forecasting revenues and expenses
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Regression and correlation analysis in forecasting revenues and expenses




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  • 1. Topic: Regression and correlation analysis in forecasting revenues and expenses Presented to venkatesh sir Faculty of commerce Dos in commerce
  • 2. Contents  Introduction  Meaning and definition  Assumption  Formula  conclusion  references
  • 3. 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
  • 4. Meaning of Regression analysis Regression 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
  • 5. 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
  • 6. Assumptions  The regression model is based on the following assumptions.  The relationship between X and Y is linear.  The expected value of the error term is zero  The 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.
  • 7. Formula Y = a + bx + ε Where: Y = dependent variable; X = independent variable, a = intercept of regression line; b = slope of regression line, ε = error term
  • 8. 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).
  • 9. For example X 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.
  • 10. Example Regression Situation 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
  • 11. Company A Data Company Sales Man Hour 6 3 8 4 9 6 5 4 4.5 2 9.5 5
  • 12. Finding the Regression Company A 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
  • 13. REGRESSION ANALYSIS USING SPSS The REGRESSION command is called in SPSS as follows:
  • 14. 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.
  • 15. The output from the preceding includes the correlation coefficient and standard error of estimate
  • 16. The regression coefficients are also given in the output.
  • 17. The optional save command generates two new variables in the data file.
  • 18. conclusion If 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.
  • 19. References   SECURITY ANALYSIS AND PORTFOLIO MANAGEMENT By S. KEVIN  Prakasha  Mfm 1s year