statistical procedure for estimating the average relationship between the dependent variable (sales, for example) and one or more independent variables (price and advertising, for example). It is a popularly used method for estimating the cost-volume formula(y = a + bx). simple regression involves one independent variable, e.g., direct labor-hours or machine-hours alone, whereas multiple regression involves two or more independent variables. Assuming a linear relationship, the simple regression model indicates that the relationship is y = a + bx, where a, and b are unknown constants, called regression coefficients. The multiple regression model is y = a0 + a1x1 + a2x2 + ... + akxk, where a's are coefficients and x's represent the number of independent variables.
In estimating the cost-volume formula, regression analysis attempts to find a line of best fit. To find the line of best fit, a technique called the least-squares method is widely used.
statistical techniques that quantify the relationship between two or more variables. The objective is quantitative prediction or forecasting to find out whether or not the variables being examined can be expected to be closely related to a larger population group. Regression analysis is a frequently used statistical tool in Asset-Liability Management, credit scoring, and econometrics Much econometric theory relates to theoretical problems arising from the application of various regression models, based on certain assumptions about economic activity.
Regression analysis allows a bank to set credit criteria for the general public, and build a statistical formula predicting how new accounts will perform in the future, that is, their ability to repay debt, by studying a sample of existing accounts. The banker wants to know the credit characteristics of consumers who are good payers, and those who are poor payers. Consumer credit ideally is suitable for statistical tools such as regression analysis because consumer behavior in handling credit is fairly predictable, based on a study of previous credit experience; even the credit losses are predictable.
statistical technique used to establish the relationship of a dependent variable, such as the sales of a company, and one or more independent variables, such as family formations, gross national product, per capita income, and other economic indicators. By measuring exactly how large and significant each independent variable has historically been in its relation to the dependent variable, the future value of the dependent variable can be predicted. Essentially, regression analysis attempts to measure the degree of correlation between dependent and independent variables, thereby establishing the latter's predictive value.
statistical technique used to establish the relationship of a dependent variable, such as the sales of a company, and one or more independent variables, such as family formations, Gross Domestic Product, per capita income, and other economic indicators.Bring exactly how large and significant each independent variable has historically been in its relation to the dependent variable, the future value of the dependent variable can be predicted. Essentially, regression analysis attempts to measure the degree of correlation between the dependent and independent variables, thereby establishing the latter's predictive value. For example, a manufacturer of baby food might want to determine the relationship between sales and housing starts as part of a sales forecast. Using a technique called a scatter graph, it might plot on the X and Yaxes the historical sales for ten years and the historical annual housing starts for the same period. Aline connecting the average dots, called the regression line, would reveal the degree of correlation between the two factors by showing the amount of unexplained variation-represented by the dots falling outside the line. Thus, if the regression line connected all the dots, it would demonstrate a direct relationship between baby food sales and housing starts, meaning that one could be predicted on the basis of the other. The proportion of dots scattered outside the regression line would indicate, on the other hand, the degree to which the relationship was less direct, a high enough degree of unexplained variation meaning there was no meaningful relationship and that housing starts have no predictive value in terms of baby food sales. This proportion of unexplained variations is termed the coefficient of determination,, and its square root the correlation coefficient. The correlation coefficient is the ultimate yardstick of regression analysis: a correlation coefficient of 1 means the relationship is direct-baby food and housing starts move together; -1 means there is a negative relationship- the more housing starts there are, the less baby food is sold; a coefficient of zero means there is no relationship between the two factors.
Regression analysis is also used in securities' markets analysis and in the risk-return analyses basic to portfolio theory
