Examine the following two quantitative variables. Create a quadratic model topredict y using x as the predictor variable.X Y0.82.12.12.93.43.54.5 7.25.8 10.26.2 10.1(Hint: copy-and-paste the data into Excel or statistical software and use thecomputer to calculate the correlation.)y =2.977-0.856x +0.31x2y = -0.126+1.154x +0.079r2y = 3.989- 1.573x +0.397ry = 1.662 0.203r + 0.24xy 1.939 - 0.121r+ 0. 249r

Answered: camine the following two quantitative…

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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Prehistoric pottery vessels are usually found as sherds (broken pieces) and are carefully reconstructed if enough sherds can be found. Information taken from Mimbres Mogollon Archaeology by A. I. Woosley and A. J. McIntyre (University of New Mexico Press) provides data relating x = body diameter in centimeters and y = height in centimeters of prehistoric vessels reconstructed from sherds found at a prehistoric site. The following Minitab printout provides an analysis of the data. Predictor Coef SE Coef T P Constant -0.212 2.429 -0.09 0.929 Diameter 0.7527 0.1686 5.33 0.013 S = 4.16190 R-Sq = 81.2% (c) The formula for the margin of error E for a c% confidence interval for the slope ? can be written as E = tc(SE Coef). The Minitab display is based on n = 12 data pairs. Find the critical value tc for a 90% confidence interval in the relevant table. Then find a 90% confidence interval for the population slope ?. (Use 3 decimal places.) tc lower limit upper…
D. Calculate a linear regression using Stabley. Write the equation below. E. What is the correlation coefficient? f. What is the slope of your LSRL? Interpret the slope using context. G. What is the Y- intercept of your LSRL? Interpret the Y-intercept using context
KidsFeet Regression Line y-Intercept The KidsFeet dataframe contains data collected on 39 fourth grade students in Ann Arbor, MI, in October 1997. Two of the measurements taken on the children were the length in centimeters, (length), and width in centimeters, (width), of their longest foot. This data could be used to answer the following Research Question: How is the width of a fourth-grade student's foot related to the length? Which of the following is the y-intercept of the regression line? ## ## Simple Linear Regression## ## Correlation coefficient r = 0.6411 ## ## Equation of Regression Line:## ## length = 9.817 + 1.658 * width ## ## Residual Standard Error: s = 1.025 ## R^2 (unadjusted): R^2 = 0.411 ( ) 1.0248 ( ) 1.6576 ( ) 22.8153 ( ) 0.411 ( ) 9.8172 ( )0.6411
1.)fill in the blanks. Based on the physician's study, the predictor variable, X is_____and the response variable, Y, is________. 2.) As described in the article, the relation between age and peak heart is a______. - positive relation - negative relation - no relation 3.) Provide the regression line, ŷ=a+bx. Show steps/equations used to get answer. 4.) Suppose a 40 year old person is randomely selected. Use your Model to predict their peak heart rate. 5.) Based on your model, as a person ages one year, how much would you expect peak heart rate to change?
èstion 1 Provide an appropriate response. In an area of the Great Plains, records were kept on the relationship between the rainfall (in inches) and the yield of wheat (bushels per acre). Calculate the linear correlation coefficient. 173 79 122 | 105 53.5 142 | 205 85.4 522 349 Rainfall (in inches), x 15.1 12 8.7 17.7 Yield (bushels per acre), y O 0.900 O 0.981 O 0.998 49.2 618 62 818 O 0.899 A Moving to another question will save this response. Question 1 of 20>> 11:33 AM (ゆッ 5/19/2021 PrtSen Home PgUP End E D B Alt
Need help with d-h
1945 is paired with cancer rates for 1975. The cigarette consumption data is from 1945 to 1975 in 5-year increments. There is a strong positive linear association between annual cigarette consumption and lung cancer rates. The correlation is 0.81. If we use the line to make predictions of the number of lung cancer deaths per 100,000 people, which of the following is an example of extrapolation? a. Predict Y when cigarette consumption is 3,500 cigarettes per person. b. Predict Y when cigarette consumption is 3,800 cigarettes per person. c. Predict Y when cigarette consumption is 4,600 cigarettes per person.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. x 11.9 8.4 6.6 3.8 2.6 2.3 2.2 0.9 y 14.2 11.1 9.6 7 6.2 6.1 5.8 5 x = thousands of automatic weaponsy = murders per 100,000 residentsThis data can be modeled by the equation y=0.85x+4.03. Use this equation to answer the following; Special Note: I suggest you verify this equation by performing linear regression on your calculator.A) How many murders per 100,000 residents can be expected in a state with 7.7 thousand automatic weapons?
A random sample of 136 adults were asked to report the number of hours per week the spent on a computer and their number of years of education. The linear model equation below describes the relationship between the mean computers hours and years of education. computers == 9.12 ++ 0.8 ×× education Based on this linear model, which of the following statements is correct? a) An adult who has no years of education is expected to spend 9.12 hours per week on a computer. b) An adult who has 1 more year of education than another is expected to spend 9.12 more hours per week on a computer. c) An adult who spends 1 more hour per week on a computer than another is expected to have had 9.12 more years of education. d) An adult who spend zero hours per week on a computer is expected to have 9.12 years of education.
After gathering data about the number of starfish and measuring the pollution in areas of the ocean you find a negative linear correlation between pollution levels and number of starfish. What can you conclude based on this information? a. There is a confounding variable that is affecting both pollution and starfish. b. As pollution rises the number of starfish falls c. That pollution is causing starfish to die, leading to the negative correlation d. That pollution is supporting starfish, leading to the negative correlation
find t reject or fail to reject
Y 5 10 15 20 25 30 -0.112 0.001 0.091 0.942 0 Based on the plot displayed here, what is a possible correlation between the variables and y?

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