Based on the scatterplot and the residual plot, is a linear model appropriate for predicting the Short Program Athletes at the 2018 World Figure Skating Championships are evaluated in two rounds of competition: the Short Program, and the Free Skate. The score from the Free Skate score? sum of the scores for each skater are then used to A linear model is appropriate because the residuals determine the medalists. For each of the 24 male are centered about zero. skaters at the 2018 World Championship, the scores for each round are displayed in a scatterplot. A linear model is not appropriate because there are outliers in the scatterplot. O A linear model is not appropriate because the residual plot does not show a linear pattern. Short Program vs. Free Skate 115 A linear model is appropriate because the scatterplot is roughiy linear, and the residual plot does not show a clear pattern. 110 105 100 95 80 75 120 130 140 150 160 170 180 190 200 210 220 Free Skate Residuais vs. Free Skate 15 10 -5 -10 -15 120 130 140 150 160 170 180 190 200 210 220 Free Skate Short Program

Based on the scatterplot and the residual plot, is a linear model appropriate for predicting the Short Program Athletes at the 2018 World Figure Skating Championships are evaluated in two rounds of competition: the Short Program, and the Free Skate. The score from the Free Skate score? sum of the scores for each skater are then used to A linear model is appropriate because the residuals determine the medalists. For each of the 24 male are centered about zero. skaters at the 2018 World Championship, the scores for each round are displayed in a scatterplot. A linear model is not appropriate because there are outliers in the scatterplot. O A linear model is not appropriate because the residual plot does not show a linear pattern. Short Program vs. Free Skate 115 A linear model is appropriate because the scatterplot is roughiy linear, and the residual plot does not show a clear pattern. 110 105 100 95 80 75 120 130 140 150 160 170 180 190 200 210 220 Free Skate Residuais vs. Free Skate 15 10 -5 -10 -15 120 130 140 150 160 170 180 190 200 210 220 Free Skate Short Program

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: River Falls Hudson Ellsworth Prescott Rock Elm Stillwater Select one: a. 78.39% b. 88.54% O c. 161.39% O d. 100% City What percentage of the total variation in the candy bar sales is explained by prices? e. 48.19% Price ($) 1.30 1.60 1.80 2.00 2.40 2.90 Sales 100 90 90 40 38 32
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…
Twelve husbands were asked how much housework they completed last week and how many children were in the home. Use the following data to complete the questions in this section. Write out the linear model. Children Husband’s Housework Children Husband’s Housework 2 4 4 6 1 1 1 3 3 0 3 5 5 4 2 3 1 5 2 1 5 7 4 3
Determine whether the study depicts an observational study or an experiment. A scientist was studying the effects of a new fertilizer on crop yield. She randomly assigned half of the plots on a farm to group one and the remaining plots to group two. On the plots in group one, the new fertilizer was used for a year. On the plots in group two, the old fertilizer was used. At the end of the year the average crop yield for the plots in group one was compared with the average crop yield for the plots in group two.
The graph shows the heights of mothers and daughters. (Source: StatCrunch: Mother and Daughter Heights.xls. Owner: craig slinkman) (a) As the data are graphed, which is the independent variable and which the dependent variable? (b) From the graph, approximate the predicted height of the daughter of a mother who is 60 inches (5feet) tall. (c) From the equation, determine the predicted height of the daughter of a mother who is 60 inches tall. (d) Interpret the slope. (e) What other factors besides the mother's height might influence the daughter’s height?
The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Which of the following is the best interpretation of the coefficient of determination r2? About 37% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 65% of the variation in foot length is accounted for by the linear relationship formed with the arm span. About 63% of the variation in arm span is accounted for by the linear relationship formed with the foot length. About 63% of the variation in foot length is accounted for by the linear relationship formed with the arm span.
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?
Range of ankle motion is a contributing factor to falls among the elderly. Suppose a team of researchers is studying how compression hosiery, typical shoes, and medical shoes affect range of ankle motion. In particular, note the variables Barefoot and Footwear2. Barefoot represents a subject's range of ankle motion (in degrees) while barefoot, and Footwear2 represents their range of ankle motion (in degrees) while wearing medical shoes. Use this data and your preferred software to calculate the equation of the least-squares linear regression line to predict a subject's range of ankle motion while wearing medical shoes, ?̂ , based on their range of ankle motion while barefoot, ? . Round your coefficients to two decimal places of precision. ?̂ = A physical therapist determines that her patient Jan has a range of ankle motion of 7.26°7.26° while barefoot. Predict Jan's range of ankle motion while wearing medical shoes, ?̂ . Round your answer to two decimal places. ?̂ = Suppose Jan's…
Advertising and Sales. Use the technology of your choice to do the following. a. Obtain the scatterplot matrix for the data. b. Use the scatterplot matrix to assess whether a multiple linear regression model might be appropriate for predicting sales. c. Determine the regression equation for the data. d. Apply the regression equation to predict total sales if the amounts spent on television, magazine, and radio advertising are $9.5 million, $4.3 million, and $5.2 million, respectively.
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.
A biologist was recording various temperatures (in degrees Celsius) and predicting the amount of glucose produced by a plant (in milligrams). Suppose the linear equation was the following: ?̂ = 12.7 − 0.223? d) Suppose the correlation coefficient was −0.12. How would you classify the correlation between temperature and amount of glucose?