The equation of the least squares regression line is: Average Complaints = 0.825 + 14.376 × Rating Predicted Complaints = 14.376 +0.755 × Rating Predicted Rating = 14.376 +0.755 × Complaints Predicted Rating = 0.755 +14.376 × Complaints Average Rating = 14.376 + 0.825 × Complaints
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- Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.941 and the least squares regression line is y = 0.244x + 10.794. Complete parts a and b below. Height, x Head Circumference, y 27.00 25.75 26.25 17.3 25.75 27.50 17.5 26.25 17.1 26.00 27.00 17.4 17.4 17.1 17.1 17.1 (a) Compute the coefficient of determination, R?. R2 =% (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. % of the variation in height is explained by the least-squares regression model. (Round to one decimal place as needed.)A music critic was interested in whether particular variables measured on a song change over time. Two variables the critic considered were a song’s Tempo (in bpm) and a song’s Danceability. We will use the songs written before the year 2000 from the original SpotifySample data set. The data set that you will use to complete this investigation is called SpotifyB2000 and consists of 483 songs. Write the least-squares regression line equation describing Year and Danceability usingproper notation and values. Interpret the slope of the regression line for Year and Danceability in context. Would the interpretation of the y-intercept for Year and Danceability be meaningful? Ifso, interpret it. If not, state why not in one sentence. Calculate and record the coefficient of determination value r2for Year and Danceabilityand interpret this value in context. State the hypotheses for the test of the slope. Write the p-value found in the output from (n), and use the p-value provided in the…Suppose a doctor measures the height, x, and head circumference, y, of 11 children and obtains the data below. The correlation coefficient is 0.883 and the least squares regression line is y = 0.134x + 13.693. Complete parts (a) and (b) below. Height, x Head Circumference, y 17.4 27.75 25.25 26 25.25 27.25 26.75 26 27.25 27 27.25 26.75 17.1 17.3 17.0 17.4 17.2 17.2 17.3 17.3 17.4 17.3 ..... (a) Compute the coefficient of determination, R?. R? % (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. Approximately % of the variation in is explained by the least-squares gression model. (Round to one decimal place as needed.)
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- 19. You might think that increasing the resources available would elevate the number of plant spe- cies that an area could support, but the evidence suggests otherwise. The data in the accompany- ing table are from the Park Grass Experiment at Rothamsted Experimental Station in the U.K., where grassland field plots have been fertilized annually for the past 150 years (collated by Harpole and Tilman 2007). The number of plant species recorded in 10 plots is given in response to the number of different nutrient types added Plot 1 2 3 4 5 6 7 8 9 10 Number of nutrients added 0 0 0 3144 E2 3 Number of plant species 36 36 32 34 33 30 20 23 21 16The multivariate analysis considers more than one factor of independent variables that influence the variability of dependent variables. a. True b. FalseStudents in an AP Statistics class wanted to determine if a person's hand span is associated with the number of candies he or she can pick up from a bowl of candy. For the fourteen students in the class, each student's hand span and the number of candies he or she was able to pick up was recorded. A least- squares analysis was conducted on the number of candies versus hand span. The conditions for inference were checked and deemed reasonable. The regression analysis output is given in the table below. Predictor Constant Coef -8.74 SE Coef 10.81 T -0.81 P 0.434 Hand span 1.26 0.52 2.44 0.031 S=3.34 R-Sq = 33.1% 39. The estimate of the slope of the least-squares regression line using a 95% confidence interval is (A) 1.26 ± 2.18(0.52) (B) 1.26 ± 2.44(0.52) (C) 1.26 ± 2.18 ( (0.521 (D) 1.26 ± 1.96(0.52) (E) 1.26 ± 3.34 (0.52) √14
- Which of the following is the equation of the relationship between the predictor variable (x) and the response variable (y)? A y = 25.707 + 0.9887xB y = 29.796 + 0.5897xC y = 25.707 + 0.5897xD y = 29.796 + 0.9887xYou plan to fit a regression model that will be used to predict first-year college GPA (FYGPA) from high-school GPA (HSGPA), ACT score (ACT), first-generation status (Yes or No), socioeconomic class (lower class, lower to middle class, middle to upper class, and upper class), and school type (public or private). What is the total number of estimated regression coefficients? If the sample size is n = 250 students, what are the degrees of freedom for the following sources of variation: Regression Error Totala. Develop the least squares estimated regression equation that relates labor hours to house square footage and type of flooring. b. Use the regression equation developed in part (a) to predict labor hours when the house size is 3350 square feet and the type of flooring is wood.