he following data on exposure to alpha particles on cell abnormalities. Exposure             abnormalities 0.106                     2 0.193                     5 0.511                     13 0.527                     15 1.08                        25 1.62                        28 1.73                        36 2.36                        45 2.72                        56 3.12                        59 3.88                        63 4.18                        60   Construct the scatter plot and compute the regression line               Compute the residuals and graph the residual plot               Comment on any features you see in these data.

The following correlations were computed as part of a multiple regression analysis that used education, job, and age to predict income. Income Education Job Age Income 1.000 Education 0.677 1.000 Job 0.173 -0.181 1.000 Age 0.369 0.073 0.689 1.000 What is this table called? Multiple Choice Correlation matrix Net regression coefficients Coefficients of nondetermination Analysis of variance

10:12 AA www-awn.aleks.com O REGRESSION AND CORRELATION Linear relationship and the.. Shasia v Below are four bivariate data sets and the scatter plot for each. (Note that each scatter plot is displayed on the same scale.) Each data set is made up of sample values drawn from a population. 1.0 1.0 1.0 7.0 2.0 2.0 2.0 8.9 3.0 3.0 3.0 7.4 4.0 4.0 4.0 5.4 5.0 5.0 5.0 8.5 6.0 6.0 6.0 | 5.1 7.0 7.0 7.0 4.2 8.0 8.0 8.0 | 7.1 9.0 9.0 Figure 1 10.0 10.0 9.0 6.2 Figure 2 10.0 3.8 w m |1.0 8.0 2.0 5.1 3.0 10.0 1.0 3.5 2.0 4.5 3.0 3.2 4.0 5.1 4.0 | 6.3 5.0 4.5 5.0 2.3 6.0 | 7.4 6.0 | 4.7 7.0 6.1 7.0 9.3 8.0 3.7 9.0 9.1 Figure 4 8.0 7.6 9.0 6.6 Figure 3 10.0 8.3 10.0 6.8 Answer the following questions about the relationships between pairs of variables and the values ofr, the sample correlation coefficient. The same response may be the correct answer for more than one question. 1. Which data set indicates the strongest positive linear relationship between its two variables? Choose one 2. Which data…

7) Below is a multiple regression in which the dependent variable is market value of houses and the independent variables are the age of the house and square footage of the house. The regression was estimated for 42 houses. SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total df 2 39 41 0.745495 0.555762 0.532981 7211.848 42 SS 2537650171 2028419591 4566069762 Coefficients Standard Error MS 1.27E+09 52010759 F 24.39544 Significance F 1.3443E-07 Upper 95% t Stat P-value Lower 95% Intercept 47331.38 13884.34664 3.408974 0.001528 19247.6673 House Age -825.161 607.3128421 -1.35871 0.182046 -2053.5662 Square Feet 40.91107 6.696523994 6.109299 3.65E-07 27.3660835 7A. What is the estimated regression equation for determining the market value of houses? 7B. Discuss tests of significance of the regression 7C. What percentage of the variation in the dependent variable, Market Value, is explained by the regression…