Multiple linear regression using ‘enter’ in SPSS differs from simple linear regression in that itA. includes more than one predictor variableB. determines which predictor variable(s) predict(s) variance each individuallyC. allows you to look at the contribution of each predictor, controlling for the otherD. all of the aboveE. only A and C

ab The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, y = bo + bix, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, In practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Answer How to enter your answer (opens in new window) esc 1 alt q a 360 m any angle. 9 Enjoy apps from Z 2- W S Even 2012 Hours Unsupervised 0.5 1 1.5 2.5 4 Overall Grades 90 85 83 80 77 OF 26 ال الــالـا Dll 6 V SPA hp b & 7. u ghj n * 8 4.5 5.5 73 64 N O i m ( 9 k O ) 0 1 alt Р…

Find the equation of the least-squares regression line ŷ and the linear correlation coefficient r for the given data. Round the constants, a, b, and r, to the nearest hundredth. {(1, 4.5), (2, 6.0), (4, 8.4), (6, 11.7), (8, 16.2)} ŷ =       r =

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the numbe of stories of six notable buildings in a city. Height, x Stories, y 778 621 519 510 494 473 (a) x = 500 feet (c) x = 318 feet (b) x = 649 feet (d) x = 727 feet 51 47 45 41 38 36 Find the regression equation.