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This 3 page Class Notes was uploaded by Nikita Hendricks on Friday September 2, 2016. The Class Notes belongs to 2305 at Baylor University taught by Prof. Turner in Fall 2016. Since its upload, it has received 6 views. For similar materials see QBA in Business at Baylor University.
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Date Created: 09/02/16
QBA Prof Turner Chapter 1 continuation and important CONCEPTS TO REMEMBER simple linear regression model the relationship between the dependent variable (y) and the independent variable (x) can be approximated by a straight line slope of the simple linear regression model the change in the mean value of the dependent variable that is associated with a one unit increase in the value of the independent variable y intercept of the simple linear regression model the mean value of the dependent variable hen the value of the independent variable is 0 correlation coefficient measures the strength of the linear relationship between two variables. Indicated by r. a value near 1 indicates a direct or positive relationship a value near -1 indicates an inverse or negative relationship a value near 0 indicates there is little or no relationship between the variables regressive analysis developing an equation estimating the value of a dependent variable based on the value of an independent variable least squares principle: uses data to position a line with the objective of minimizing the sum of the squared vertical distances between the observed y values and predicted values of extrapolation the use of a formula deduced from an initial set of data for data values outside of the experimental region explained variation a quantity that measures the amount of the total variation in the observed values of y that is explained by the predictor variable residual the difference between the observed value of the dependent variable and the corresponding predicted value of the dependent variable total variation a quantity that measures the total amount of variation exhibited by the observed values of the dependent variable "y" unexplained variation a quantity that measures the amount of the total variation in the observed values of y that is not explained by the predictor "x" what is estimated by the mean square error? sigma squared difference between a confidence interval and a prediction interval a confidence interval- mean value of y a prediction interval- individual value of y total variation - unexplained variation = explained variation total variation = explained variation + unexplained variation H0: p = 0 no linear relationship between x and y Ha: p does not equal 0 there is a positive or negative relationship between x and y P-value: if the p value is less than the level of significance reject H0 -strong evidence that x and y have a strong relationship in the regression. The smaller the significance level alpha at which H0 can be rejected. The stronger the evidence that the regression relationship is significant
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