Explore BrainMass

Explore BrainMass

    Correlation & Regression

    This content was COPIED from BrainMass.com - View the original, and get the already-completed solution here!

    Pearson correlation regression equation for predicting Y from the X values

    This needs to be done in excel.
    Chapter 15:
    #2. What information is provided by the numerical value of the Pearson correlation?

    #6. For the following scores
    X Y
    3 12
    6 7
    3 9
    5 7
    3 10
    a. Compute the Pearson correlation.

    b. With a small sample, a single point can have a large effect on the on the magnitude of the correlation. Change the score X = 5 to X = 0 and compute the Pearson again. You should find the change has a dramatic effect on the value of the correlation.

    #18. Sketch a graph showing the line for the equation Y = -2X + 4. On the same graph, show the line for Y = X - 4.

    #20. A set of n = 20 pairs of scores (X and Y values) has SSx = 25, SSy = 16 and SP = 12.5. If the mean for X values is M = 6 and the mean for the Y values is M = 4.
    a. Calculate the Pearson correlation for the scores.
    b. Find the regression equation for predicting Y from the X values.

    #22 For the following data:
    X Y
    1 2
    4 7
    3 5
    2 1
    5 14
    3 7
    a. Find the regression equation for predicting Y from the X values.
    b. Use the regression equation to find a predicted Y for each X.
    c. Find the difference between the actual Y value and the predicted Y value for each individual, square the differences and add the squared values to obtain SSresidual.
    d. Calculate the Pearson correlation for these data. Use r2and SSy to compute SSresidual. with Equation 15.18. You should obtain the same values as in part c.

    © BrainMass Inc. brainmass.com October 10, 2019, 12:42 am ad1c9bdddf


    Solution Summary

    The solution provides step by step method for the calculation of correlation and regression analysis. Formula for the calculation and Interpretations of the results are also included.