Compute the least-squares regression line for predicting the right foot temperature from the left foot temperature. Roundthe slope and y-intercept values to four decimal places. Foot temperatures: Foot ulcers are a common problem for people with diabetes. Higher skin temperatures on the foot indicatean increased risk of ulcers. In a study performed at the Colorado School of Mines, skin temperatures on both feet were measured,in degrees Fahrenheit, for 8 diabetic patients. The results are presented in the following table. Use a TI-84 calculator to answerthe following.Left FootRight Foot85858986787887828081808081838685Send data to Excel Compute the least-squares regression line for predicting the right foot temperature from the left foot temperature. Roundthe slope and y-intercept values to four decimal places.y =

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Answered: Compute the least-squares regression…

Compute the least-squares regression line for predicting the right foot temperature from the left foot temperature. Round the slope and y-intercept values to four decimal places. y =

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 scatter plot below shows the average cost of a designer jacket in a sample of years between 2000 and 2015. The least squares regression line modeling this data is given by yˆ=−4815+3.765x. A scatterplot has a horizontal axis labeled Year from 2005 to 2015 in increments of 5 and a vertical axis labeled Price ($) from 2660 to 2780 in increments of 20. The following points are plotted: (2003, 2736); (2004, 2715); (2007, 2675); (2009, 2719); (2013, 270). All coordinates are approximate. Interpret the slope of the least squares regression line. Select the correct answer below: 1.The average cost of a designer jacket decreased by $3.765 each year between 2000 and 2015. 2.The average cost of a designer jacket increased by $3.765 each year between 2000 and 2015. 3.The average cost of a designer jacket decreased by $4815 each year between 2000 and 2015. 4. The average cost of a designer jacket increased by $4815 each year between 2000 and…
Use the least squares regression line of this data set to predict a value. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The least squares regression line of this data set is: y = 8.116x + 273.273 How much rainfall does this line predict in a year if the average temperature of coastal waters is 15 degrees Celsius? Round your answer to the nearest integer. millimetres
3- Find the equation of the regression line and find the y_ value for the specified x value. Calories and Cholesterol Calories and cholesterol are as follows: Calories X 390 535 720 300 430 500 440 Cholesterol y 43 45 80 50 55 52 60 Find y-hat when x = 600 calories. LO LO
The following table gives the data for the grades on the midterm exam and the grades on the final exam. Determine the equation of the regression line, yˆ=b0+b1xy^=b0+b1x. Round the slope and y-intercept to the nearest thousandth. Grades on Midterm and Final Exams Grades on Midterm 78 63 85 92 82 89 69 89 83 61 Grades on Final 77 75 88 92 86 86 80 85 77 63
The table below shows the amounts of crude oil (in thousands of barrels per day) produced by a country and the amounts of crude oil (in thousands of barrels per day) imported by a country, for the last 7 years. Construct and interpret a 98% prediction interval for the amount of oil imported by the country and the amount of oil produced by the country is 5,483 thousand barrels per day. The equation of the regression line is y = -1.194x + 16,234.906
You run a linear regression to predict BMI (body mass index) from average minutes of physical activity per day. You calculate the slope of the regression to be -.054. Interpret the slope in terms of the independent and dependent variables.
write the regression equation that will predict the number of manatees killed given thenumber of powerboat registrations (in 1000's). Show all work usingformulas for slope, b1, and y-intercept, b0, calculations roundingto 4-decimal places, if necessary. Show all the calculations and math of slope, b1, and y-intercept, b0, calculations roundingto 4-decimal places, if necessary. SHOW ALL WORK! DO NOT USE EXCEL! I need to be able to see every single part and how you got it. attached is the question
The table below shows the average weekly wages (in dollars) for state government employees and federal government employees for 10 years, construct and interpret a 98% prediction interval for the average weekly wages of federal government employees when the average weekly wages of state government employees is $888 the equation of the regression line is y= 1.453x-47.386. Wages(state), X | 739|760|784|822|898|921|937|954|965| Wages (federal), Y |1,036|1,054|1,105|1,136|1,192|1,253|1,266|1,327|1,400|
The applet displays a scatterplot with the following points: (7,10),(27,35),(24,25),(8,12),(10,19),(15,22). Identify the slope of the regression line, intercept of the regression line, and correlation coefficient. Report your answers accurate to within two decimal places.
Researchers are interested in predicting the height of a child based on the heights of their mother and father. Data were collected, which included height of the child ( height), height of the mother ( mothersheight ), and height of the father (fathersheight ). The initial analysis used the heights of the parents to predict the height of the child (all units are inches). The results of the analysis, a multiple regression, are presented below. . regress height mothersheight fathersheight Source Model Residual Total height mothersheight fathersheight _cons SS 208.008457 314.295372 522.303829 df 2 104.004228 8.49446952 37 MS 39 13.3924059 Coef. Std. Err. .6579529 .1474763 .2003584 .1382237 9.804327 12.39987 t P>|t| 4.46 0.000 C 0.156 0.79 0.434 Number of obs = F( 2, 37) = Prob > F R-squared Adj R-squared = Root MSE = = .3591375 -.0797093 -15.32021 = 40 12.24 0.0001 0.3983 0.3657 2.9145 [95% Conf. Interval] .9567683 .4804261 34.92886 What are the null and alternative hypotheses…
Find the least-squares regression line treating square footage as the explanatory variable. y = (Round the slope to three decimal places as needed. Round the intercept to one decimal place as needed.)
Interpret the slope of the least square regression line in content