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Regression Analysis And Multiple Regression

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Introduction on regression Regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. Regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quintile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution. (Wikipedia, 2014) Simple Vs. Multiple Simple regression analysis is a very useful technique for examining the relationship between two variables. It is not nearly useful as multiple regression analysis. Multiple regression employs a linear function of two or more independent variables to explain the variation in a dependent variable. Unlike simple regression where one predicts the observed values of the dependent variable but in multiple regression we can predict the observed values of two or more independent variables R-squared is a measurement of how cl... ... middle of paper ... ...t listing each team and the variables. Where the data was collected there it had a value already added into each team so that data was added also to see how far the model was off. When all that data was entered the regression was put into play. From the excel spreadsheet one can go to “Data” then from there go to “Data Analysis”. Once that is done then click regression and input the “Y Range” and “X Range”. To keep things organized click the button for labels and put a confidence level at 95%. By clicking new worksheet one can just go navigate through each worksheet instead of working all on one worksheet. After all is finished, hit ok and the regression would be finished. It would give you the coefficients, standard error, and the T-stats. When setting up the regression one can click to have the residual output and see the predicted value along with the residuals.
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