Research and Evaluation

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Submitted By MirelisDelgado
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Pages 4
Regression Analysis Team A has determined to execute a linear regression analysis, an equation that states the linear association linking two variables, to conclude if home prices are established on distance (Doane & Seward, 2007). The computation and tests are founded on the real estate data table provided by Lind, Marchal, & Wathen, (2008). The global opinion of the Team A is that the distance to the city center determines the price of the home. h: Distance establishes price h: Distance does not establish price for any regression analysis there ought to be two variables, a dependent (price) and an independent (distance). With the intention of measuring the strength and direction of correlation, one would use a regression equation (Doane, 2007).
According to the least squares standard, the equation for this is Y = a + bX. Y is the forecasted Y value, (a) is the y-intercept, and b is the slope of the line. Team A will be employing the data in the Data Set for the research:
With the aim of interpreting, the data of two variables must be recognized (1) coefficient of correlation (R) and (2) coefficient of determination (R2). The square root of R2 is clearly r, which is the correlation coefficient (Orris, 2007 p211). Coefficient of the correlation will be a number between 0 and 1. The nearer the number is to 1.00, the stronger the correlation, and the farther the number is to 0.00, the lower the correlation.
Given that the square root, r can also be a value as low as -1.00, representing a perfect negative correlation (Orris, 2007). The coefficient of determination is a measure of relative fit based on a comparison of SSR and SST (Doane, 2007 p508). The SSR is the sum of the squared differences between the conditional mean value of y (different for all xs), and the unconditional mean value of y (same for all xs). The SST is the total variation in y (Doane, 2007…...

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