STER.1. Wine Consumption. The table below gives the U.S. adult wine consumption, in gallons perperson per year, for selected years from 1980 to 2005.a) Create a scatterplot for the data. Graph the scatterplotYearWinebelow.Consumption2.6b) Determine what type of model is appropriate for the1980data.19852.3c) Use the appropriate regression on your calculator to find aGraph the regression equation in the same coordinateplane below.d) According to your model, in what year was wineconsumption at a minimum? Ae) Use your model to predict the wine consumption in2008.19902.019952.120002.520052.8

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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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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
The table below shows the approximate percent of students who sent applications to two colleges in various years since 1985. Years Since 1985 0 3 6 9 12 15 Percent 20 18 15 15 14 13 The scatter plot below is a representation of the table of data above. A regression equation for this line is What do the slope and y-intercept indicate? a. The slope indicates that the percent in 1985 should have been about 19%. The y-intercept indicates that the percent is falling at about 0.4% each year. b. The slope indicates that the percent is falling at about 0.4% each year. The y-intercept indicates the percent in 1985 should have been about 19%. c. The slope indicates tha the percent has remained the same for each year. The y-intercept indicates the percent in 1985 should have been 22%. d. There is not enough information to determine what the slope and y-intercept indicate. Please select the best answer from…
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 272.8 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.8 277.9 253.3 259.2 279.0 258.0 274.0 262.4 Height 1784.7 1771.2 1676.0 1645.9 1858.9 1710.1 1788.9 1737.0 The regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.8 mm is enter your response heremm. (Round to the nearest integer as needed.)
The following data show the brand, price ($), and the overall score for six stereo headphones that were tested by a certain magazine. The overall score is based on sound quality and effectiveness of ambient noise reduction. Scores range from 0 (lowest) to 100 (highest). The estimated regression equation for these data is ŷ = 22.054 and y = overall score. Brand Price ($) Score A 180 78 B 150 73 95 63 D 70 58 E 70 40 F 35 24 (a) Compute SST, SSR, and SSE. (Round your answers to three decimal places.) SST = SSR = SSE = (b) Compute the coefficient of determination . (Round your answer to three decimal places.) Comment on the goodness of fit. (For purposes of this exercise, consider a proportion large if it is at least 0.55.) O The least squares line provided a good fit as a small proportion of the variability in y has been explained by the least squares line. O The least squares line provided a good fit as a large proportion of the variability in y has been explained by the least squares…
The table shows the national expenditures for health care in billions, in a country for the selected years from 1990 to 2020. Given the projection cost for the year 2002, identify the type of regression point. Year Exprenditures ($billions) 1991 1020 1994 1356 1998 1984 2004 2984 2009 3374 2016 4765 2020 5937 Interpolation Extrapolation Neither Both
please use chart attatched. a. Explain the meaning of the slope of the regression line in this context. b. What is the predicted cost of maintenance for someone who racks up about 2,000 miles per month on a sedan? c. Suppose one of the observations used to get the data table above was a sedan owner who reported he drove 980 miles this month and spent about $450 on maintenance costs in the same month. What is the residual of this observation?
An instructor asked a random sample of eight students to record their study times at the beginning of a course. She then made a table for total hours studied (x) over 2 weeks and test score (y) at the end of the 2 weeks. The table is given below. Complete parts (a) through (f). x 10 13 10 18 6 15 16 21 y 93 79 81 74 85 81 85 80 a. Find the regression equation for the data points. b. Graph the regresson equation c. Describe the apparent relationship between the two variables. d. Identify the predictor and response variables. e. Identify outliers and potential influential observations. f.Predict the score for a student that studies for 17 hours.
Roller coasters get their speed as a result of dropping down a steep incline. The table below gives height of a drop and the speed achieved for different roller coasters around the world. Drop in Meters (m 25 30 28 58 55 40 90 100 Speed of the coaster (k.p.h) 80 90 78 93 90 93 105 128 Have your calculator find the linear regression for the roller coaster data above. Write the linear model, clearly define the variables and state correlation coefficient and clearly interpret the slope. Use the model to find the drop height that will result in a coaster speed of 150 kph. Have your calculator find the exponential regression for this data. Write the exponential model, clearly define the variables and state correlation coefficient. Use the model to find the drop height that will result in a…
Consider the following data: A. Find the equation of the regression line. B. Draw the graph of the regression equation on the scatter plot. x 1 2 3 4 5 6 7 y 15 10 20 5 25 20 35
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 272.7 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.3 277.8 252.8 258.7 279.0 258.4 274.1 261.7 Height 1785.0 1771.0 1675.7 1645.7 1859.3 1710.2 1789.2 1737.0 The regression equation is ŷ = + (x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.7 mm is (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? O A. The result is close to the actual height of 1776 mm. O B. The result is exactly the same as the actual height of 1776 mm. O C. The result is very different from the actual height of 1776 mm. O D. The result does not make sense given the context of the data. C mm.
The table below shows the number of state-registered automatic weapons and the murder rate for several Northwestern states. 11.8 8.2 6.9 3.6 2.5 2.8 2.6 0.4 14 11.3 10.2 7.4 6.5 6.3 5.9 4.5 x thousands of automatic weapons y = murders per 100,000 residents This data can be modeled by the equationý = 0.85x + 4.12. Use this equation to answer the following; %D A) How many murders per 100,000 residents can be expected in a state with 7.3 thousand automatic weapons? Answer = Nearest whole value. B) How many murders per 100,000 residents can be expected in a state with 1 thousand automatic weapons? Nearest whole value Answer =
The following is the recorded earthquakes on South Carolina from August, 2016 to February, 2017. Use the data to find the residuals. Then draw a residual plot by hand. Use the residual plot to determine if the linear model is the best regression model for this data.

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