Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.1 mm. How does the result compare to the actual height of 1776 mm?Foot Length 282.0 278.0 252.7 259.0 278.9 257.8 274.1 262.3Height1785.0 1770.9 1676.3 1646.0 1859.3 1710.1 1789.3 1737.2The regression equation is ŷ = + (x.(Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.)←

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Answered: Height 1785.0 1770.9 1676.3 1646.0…

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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A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-0.7 b=32.521 r2=0.758641 r=-0.871 Use this to predict the number of situps a person who watches 2 hours of TV can do (to one decimal place)
A data set whose original x values ranged from 120 through 351 was used to generate a regression equation of ŷ=0.06x + 14.2. Use the regression equation to predict the value of y when x=110.
Find the equation of the regression line Y= ?+(-?)X Round the constant three decimal places as needed. Round the coefficient to six decimal places as needed The best for predicted crash fatality rate for a year in which there are 425 metric tons of lemon imports is ? Fatalities per 100,000 population.(round two one decimal place as needed)
please show calc functions ti-84
Please only answer “d” and “e” as I already have the other parts filled out. Thank you!
Please fill in the blanks and select the correct answers over the image provided. Thank you
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=a+bx a=20.222 b=-0.911 r2=0.966289 r=-0.983 Use this to predict the number of situps a person who watches 10 hours of TV can do (to three decimal places) situps
Please solve the following problem to fill in the blanks
The file Galton on D2L contains the 928 observations Francis Galton used in 1885 to estimate the relationship between the heights of parents and the heights of their children. The column Children refers to the height (in inches) of a child, and the column Mid-Parents refers to the average height (in inches) of the mother and father of that child. You can download this file into Excel and Minitab. a. Calculate the regression Height of Children = a +b (Height of Mid-Parents). b. Calculate the average for Height of Children, and calculate the average Height of Mid-Parents. c. Create a new variable in Minitab which is the Height of Children measured in terms of deviations from its mean. Call this new variable y. Also, create a new variable in Minitab with is the Height of Mid-Parents measured in terms of deviations from its mean. Call this new variable x. Calculate the regression y = a + bx. You can create the new y and x variables in Excel of Minitab, whichever you find more convenient.…
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 85 mm Hg. Use a signicance level of 0.05. Right Arm 102 101 93 76 77 Left Arm 177 170 147 148 148
Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed lemon/crash data, where lemon imports are in metric tons and the fatality rates are per 100,000 people, find the best predicted crash fatality rate for a year in which there are 425 metric tons of lemon imports. Is the prediction worthwhile? Lemon Imports Crash Fatality Rate 226 270 16 15.8 364 481 521 15.5 15.4 14.9
Fifty matched pairs of magnitude/depth measurements randomly selected from 10,594 earthquakes recorded in one year from a location in southern California. Find the best predicted depth of an earthquake with a magnitude of 0.7. Use a significance level of 0.05.

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Height 1785.0 1770.9 1676.3 1646.0 1859.3 1710.1 1789.3 1737.2 regression equation is y=+x und the y-intercept to the nearest integer as needed. Round the slope to two decimal places s needed.)