Suppose the manager of a gas station monitors how many bags of ice he sells daily along with recording the highest temperatureeach day during the summer. The data are plotted with temperature, in degrees Fahrenheit (°F), as the explanatory variable andthe number of ice bags sold that day as the response variable. The least squares regression (LSR) line for the data isY = -151.05 + 2.65 X.On one of the observed days, the temperature was 82 °F and 68 bags of ice were sold. Determine the number of bags of icepredicted to be sold by the LSR line, Y, when the temperature is 82 °F. Enter your answer as a whole number, rounding ifnecessary.ice bagsUsing the predicted value you just found, compute the residual at this temperature.residual =ice bags

Answered: Image /qna-images/answer/2964f159-a9d3-4fc1-8702-df21caa28518.jpg

Answered: Suppose the manager of a gas station…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 58 inches. Is the result close to the actual weight of 572 pounds? Use a significance level of 0.05. Chest size (inches) 46 57 53 41 40 40 Weight (pounds) 384 580 542 358 306 320 LOADING... Click the icon to view the critical values of the Pearson correlation coefficient r. What is the regression equation? y=nothing+nothingx (Round to one decimal place as needed.)
You make the bold assumption that there is a linear relationship between number of ads and sales revenues. Find the intercept, b0, and the slope, b1, of the estimated regression equation that will minimize the sum of square residuals.
Biologist Theodore Garland, Jr. studied the relationship between running speeds and morphology of 49 species of cursorial mammals (mammals adapted to or specialized for running). One of the relationships he investigated was maximal sprint speed in kilometers per hour and the ratio of metatarsal-to-femur length. A least-squares regression on the data he collected produces the equation ŷ = 37.67 + 33.18x where x is metatarsal-to-femur ratio and y is predicted maximal sprint speed in kilometers per hour. The standard error of the intercept is 5.69 and the standard error of the slope is 7.94. Construct a 96% confidence interval for the slope of the population regression line. Give your answers precise to at least two decimal places. contact us help 6:42 PM povecy polcy terms of use careers A E O 4») 18 -క90.4 58 12/14/2020 a 17 |耳即 delets prt sc insert 112 19 18 + 16 backspace f5 fA
The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…
Is It Getting Harder to Win a Hot Dog Eating Contest?Every Fourth of July, Nathan’s Famous in New York City holds a hot dog eating contest. The table below shows the winning number of hot dogs and buns eaten every year from 2002 to 2015, and the data are also available in HotDogs. The figure below shows the scatterplot with the regression line. Year Hot Dogs 2015 62 2014 61 2013 69 2012 68 2011 62 2010 54 2009 68 2008 59 2007 66 2006 54 2005 49 2004 54 2003 45 2002 50 Winning number of hot dogs in the hot dog eating contest Winning number of hot dogs and buns Click here for the dataset associated with this question. (a) Is the trend in the data mostly positive or negative? Positive Negative (b) Using the figure provided, is the residual larger in 2007 or 2008?Choose the answer from the menu in accordance to item (b) of the question statement 20072008 Is the residual positive or…
The following regression model was estimated. Q is the number of meals served, P is the average price per meal (customer ticket amount, in dollars), Rxis the average price charged by competitors (in dollars), Ad is the local advertising budget for each outlet (in dollars), and I is the average income per household in each outlet's immediate service area. Least squares estimation of the regression equation on the basis of the 25 data observations resulted in the estimated regression coefficients and other statistics given in Table below. Variable Coefficient Standard Error of Coefficient Intercept 128832.240 69974.818 Price (P) Competitor Price (Px) | Advertising (Ad) Income () -19875.954 4100.856 15467.936 459.280 0.261 0.094 8.780 1.017 Coefficient of determination R =83.3% (a) Interpret the coefficients of independent variables. (b) Test the significance of independent variables at 5% level of Significance. (c) Interpret R? with the help of adjusted R2. (d) Test for the overall…
Report the equation of the regression line and interpret it in the context of the problem
Ap Stats
A least squares regression line was calculated to relate the length (cm) of newborn boys to their weight in kg. The line is weight = -5.25 +0.1696 length. A newborn was 48 cm long and weighed 3 kg. According to the regression model, what was his residual? What does that say about him?
You are studying how a penguin's bill length (in mm) explains its body mass (in grams) using linear regression. You choose a non-directional alternative to be safe. Given the information below, choose the formula for the least squares regression line. b₁ = 87.42 bo = 362.31 x = 43.92 y = 4202.0 O Bill Length = 87.42 Body mass + 362.31 O Bill Length = 87.42*4202.0 + 362.31 O 4202.0 = 362.31*43.92 +87.42 O Body mass = 87.42 * Bill Length + 362.31 O Body mass = 362.31 *Bill Length + 87.42 O Body mass = 362.31 43.92 + 87.42
The data shows a systolic and a diastolic blood pressure of certain patients. Find the linear regression eqation, using the first variable (systolic) as the independent variable. Find the best predicted diastolic blood pressure for a patient with a systolic blood pressure reading of 140. Use a significance level of x Systolic Diastolic 116 82 124 88 112 72 144 95 138 96 112 76 145 88 124 76 130 90 129 94 1)Find the correlation coefficient. 2)State the appropriate critical r value to use to test the claim that the correlation between systolic blood pressure and diastolic blood pressure is significantly different from 0 3)The correlation between diastolic blood pressure and systolic blood pressure is: significant, not significant, weak, none 4)The best predicted diastolic blood pressure for a patient with a systolic blood pressure reading of 140 is closest to 1pts
The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 58 inches. Is the result close to the actual weight of 662 pounds? Use a significance level of 0.05. Chest size (Inches) 46 57 53 41 40 40 Weight (Pounds) 384 580 542 358 306 320

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