Pterosaur Data Humerus, x 26 31 25 23 19 Wingspan, y Wingspan, y 685 690 702 717 681 646 631 622 614 508 1.4 40 1.3 33 1.2 30 1.0 28 0.8 26 (a) Find the equation of the least-squares regression line for the data. Round constants to the nearest hundredth. (b) Use the equation from part (a) to determine, to the nearest centimeter, the projected wingspan of a pterosaur if its humerus is 52 centimeters. cm Humerus, x 29 35 24 21 16

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 number of stories of six notable buildings in a city. height 775 619 519 508 491 474  ​(a) x=503 feet ​(b) x=644 feet ​Stories, y 53 47 44 42 39 38   ​(c) x=798 feet ​(d) x=734 feet

Biochemical oxygen demand (BOD) measures organic pollutants in water by measuring the amount of oxygen consumed by microorganisms that break down these compounds. BOD is hard to measure accurately. Total organic carbon (TOC) is easy to measure, so it is common to measure TOC and use regression to predict BOD. A typical regression equation for water entering a municipal treatment plant is BOD = -55.47 + 1.505 TOC Both BOD and TOC are measured in milligrams per liter of water. (a) What does the slope of this line say about the relationship between BOD and TOC? TOC rises (falls) by 1.505 mg/l for every 55.47 mg/l increase (decrease) in BOD BOD rises (falls) by 55.47 mg/l for every 1 mg/l increase (decrease) in TOC TOC rises (falls) by 1.505 mg/l for every 1 mg/l increase (decrease) in BOD BOD rises (falls) by 1.505 mg/l for every 1 mg/l increase (decrease) in TOC

Length, x Girth, y 136 107 168 131 151 117 145 106 159 125 160 120 123 103 138 103 156 119 147 109 146 108 145 110

I need help to interpret the slope of the scatter plot and to do the linear regression equation

In baseball, two statistics, the ERA (Earned Run Average) and the WHIP (Walks and Hits per Inning Pitched), are used to measure the quality of pitchers. For both measures, smaller values indicate higher quality. The following computer output gives the results from predicting ERA by using WHIP in a least-squares regression for the 2017 baseball season. Variable DF Estimate SE T Intercept 1 -5.0 0.26 - 19.3 WHIP 1 6.8 0.14 47.4 Which of the following statements is the best interpretation of the value 6.8 shown in the output? ERA is predicted to increase by 6.8 units for each 1 unit increase of WHIP. WHIP is predicted to increase by 6.8 units for each 1 unit increase of ERA. For a pitcher with 0 units of WHIP, the ERA is predicted to be approximately 6.8 units. For a pitcher with 0 units of ERA, the WHIP is predicted to be approximately 6.8 units. Approximately 6.8% of the variability in ERA is due to its linear relationship with WHIP.

A scatter plot shows the relationship between two quantitative variables. Each of the six scatter plots pictured shows the least-squares regression line for the corresponding data set. Classify each scatter plot according to whether the least-squares regression line has a meaningful ?y-intercept value.

Height, x 772 628 518 508 496 483 stories, y 51 48 44 43 37 35 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 number of stories of six notable buildings in a city. (a) x = 498 feet  (b) x = 640 feet  (c) x = 345 feet  (d) 735 feet Find the regression equation.Now construct a scatter plot of the data and draw the regression line. ​(a) Now use the regression equation to predict the value of y for each of the given​ x-values, if meaningful. Because the correlation between x and y is​ significant, the equation of the regression line can be used to predict​ y-values. However, prediction values are meaningful only for​ x-values in the range of the data.  Begin with x = 498. Since x = 498…

A simple regression model for 10 pair of data resulted in a standard error of 3.95 (i.e., Se = 3.95), and the. The sum of squares of error (SSE) is ______.   a. 187.23   b. 171.63   c. 156.03   d. 140.42   e. 124.82

Use the pizza cost and the subway fare in the table below to find the regression equation, letting pizza cost be the predictor (x) variable. (Pizza cost is in dollars per slice, subway fare and CPI are in dollars.) What is the best predicted subway fare when pizza costs $3.98 per slice? Year Pizza Cost Subway Fare CPI 1960 1973 1986 1995 2002 2003 2009 2013 2015 2019 0.152 0.353 1.000 1.251 1.753 2.003 2.254 2.300 2.747 2.997 0.151 0.346 1.000 1.347 1.497 2.004 2.253 2.551 2.747 2.750 29.6 44.4 109.6 152.4 180.0 184.0 214.5 233.0 237.0 252.2 The regression equation is y=+x. (Round the y-intercept to four decimal places as needed. Round the slope to three decimal places as needed.) The best predicted subway fare when pizza costs $3.98 per slice is $ (Round to the nearest cent as needed.)