Introduction to Quantitative Methods
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Textbook:
Pages : 395;
Paperback;
210 X 275 mm approx.
Workbook:
Pages :
276; Paperback;
210 X 275 mm approx, Sample Applied Theory Questions
Sample Multiple Choice Questions (Online Quiz)
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Textbook Price: Rs. 900;
Workbook Price: Rs. 700;
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Correlation and Regression Analysis : Chapter 18
SUMMARY: Correlation is a statistical tool used to measure the degree to which two variables fluctuate with reference to one another. These variables should have cause and effect relationship. The concept of correlation can be classified into several broad categories like positive and negative, simple and multiple, partial and total, and linear and non-linear. This chapter discussed the various types of correlation, Karl Pearson’s Coefficient of Correlation and Spearman’s Rank Correlation Coefficient. Regression analysis is a mathematical measure of the average relationship between two or more variables in terms of original units of data. Once the relationship between two variables is established, managers can use it to make estimates or to arrive at forecasts for the value of one variable on the basis of other variables. Regression analysis offers several advantages and is based on certain assumptions. This chapter discussed the concepts of regression line |
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using the method of least squares. This chapter also discussed the concepts of regression coefficient and the way in which the coefficients are calculated.
Standard error of estimate is a parameter used to measure the reliability of the estimating equation. The measure of variation of the observations around the computed regression line is called the standard error of estimate.
This chapter also discusses multiple regression and multicollinearity. To increase the accuracy of the estimate, we can use more than one independent variable to estimate the dependant variable. This is called multiple regression analysis. The principle advantage of multiple regression is that it allows us to use more of the information available to estimate the dependent variable.
In multiple regression analysis, the regression co-efficient often become less reliable as the degrees of correlation between the independent variables increases. This statistical problem is known as multicollinearity.
