2. Zerox SERVICE TIMEThe Service Time Data and the Least Squares LineNumber of CopiersServiced, xNumber of MinutesServiceCallCopiers Line Fit PlotRequired, y2001092581505138471891001378250Minutes = 11.4641 + 24.6022 Copiers4103134268Copiers1011211154Zerox-Copiers, Ic., sells and services the Accu-500 copying machine. As part of its standard servicecontract, the company agrees to perform routine service on this copier. To obtain information about thetime it takes to perform routine service, Zerox Copiers has collected data for 11 service calls. The dataand Excel output from fitting a least squares regression line to the data are given on the Table above.b. Use the least squares line to compute a point estimate of the mean time to service four copiersand a point prediction of the time to service four copiers on a single call.Minutes

Consider the data that contains the information of the time to service the copiers. The least-square…

Answered: Zerox-Copiers, Inc., sells and services…

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 height and grade point average of 50 randomly selected students are recorded. The following Python code is used to fit a simple linear regression model using data collected from the sample that is stored in the gpa.csv file. Which of the two variables, “gpa” or “height”, is the response variable? Which is the predictor variable? Select one. import pandas as pdimport statsmodels.formula.api as smfscores = pd.read_csv('http://data-analytics.zybooks.com/gpa.csv')model = smf.ols(gpa ~ height', scores).fit() Question 4 options: gpa and height are both response variables. height is the response variable and gpa is the predictor variable. gpa is the response variable and height is the predictor variable. gpa and height are both predictor variables.
Based on the data presented in the table below, please calculate the values of b0 and b1 using OLS (ordinary least squares) and for the equation: q1 = β0 + β1pi + ui. qi pi 1 2 2 3 3 5
In a study, nine tires of a particular brand were driven on a track under identical conditions. Each tire was driven a particular controlled distance (measured in thousands of miles) and the tread depth was measured after the drive. Tread depth is measured in "mils." Here, 1 mil is 0.001 inch. The equation of the least-squares regression line is: y-hat 360.64 - 11.39x Also, r = 0.9762. For every 1,000 miles driven, the decrease in tread depth (in mils) can be estimated as: 246.74 mils. 11.39 mils. 275.6 mils. O 360.64 mils.
College Graduation Rates. Data from the College Results Online website compared the 2011 graduation rate and median SAT score for 92 similar-sized public universities and colleges in the United States. The scatterplot below shows the relationship between these two variables along with the least squares fit. Round all calculated results to 4 decimal places I need help with 3, 4 and 5 plz.
Use the following information to answer questions 6 and 7. In a study, nine tires of a particular brand were driven on a track under identical conditions. Each tire was driven a particular controlled distance (measured in thousands of miles) and the tread depth was measured after the drive. Tread depth is measured in "mils." Here, 1 mil is 0.001 inch. The least-squares regression line was computed and added to a scatterplot of these data. On the plot, one data point is marked with an "X." The equation of the least-squares regression line is: Tread depth = 360.64 - 11.39 Miles The data value marked with "X" in the provided scatterplot has Tread Depth (Mils) 60 80 150 0 5 O a negative value for the residual. O a positive value for the residual. O a zero value for the residual. O a zero value for the correlation. 10 15 Miles (x 1000) 20 25 30
College Graduation Rates. Data from the College Results Online website compared the 2011 graduation rate and median SAT score for 92 similar-sized public universities and colleges in the United States. The scatterplot below shows the relationship between these two variables along with the least squares fit. Round all calculated results to 4 decimal places. I ONLY need help with question 6.
Explain the concept and use of the method of Least Squares
In a study of 1991 model cars, a researcher computed the least-squares regression line of price (in dollars) on horsepower. He obtained the following equation for this line. Price = – 6677 + 175× Horsepower Based on the least-squares regression line, what would we predict the cost to be of a 1991 model car with horsepower equal to 200? If the actual cost of a 1991 car with 200 horsepower is $27500, what is the residual? Is the predictionan underestimate or an overestimate? What does the slope of 175 and y intercept of (0,-6677) mean in the context of the problem? The coefficient of determination is ?2=84%. Interpret in the context of the problem. Find the correlation and interpret.
Biologist Theodore Garland, Jr. studied the relationship between running speeds and morphology of 49 species of cursorial mammals (mammals adapted to or specialized for running). One of the relationships he investigated was maximal sprint speed in kilometers per hour and the ratio of metatarsal-to-femur length. A least-squares regression on the data he collected produces the equation ŷ = 37.67 + 33.18x %3D where x is metatarsal-to-femur ratio and ŷ is predicted maximal sprint speed in kilometers per hour. The standard error of the intercept is 5.69 and the standard error of the slope is 7.94. Construct an 80% confidence interval for the slope of the population regression line. Give your answers precise to at least two decimal places. Lower limit: Upper limit:
Table 7 gives the number of visitors per year at YosemiteNational Park.(a) Find the least-squares line for these data.(b) Estimate the number of visitors in 2017.
An engineer is testing a new car model to determine how its fuel efficiency, measured in L/(100 km), is related to its speed, which is measured in km/hour. The engineer calculates the average speed for 30 trials. The average speed is an example of a (statistic or parameter) The engineer would like to find the least squares regression line predicting fuel used (y) from speed (x) for the 30 cars he observed. He collected the data below. Speed 62 65 80 82 85 87 90 96 98 100 Fuel 12 13 14 13 14 14 15 15 16 15 Speed 100 102 104 107 112 114 114 117 121 122 Fuel 16 17 16 17 18 17 18 17 18 19 Speed 124 127 127 130 132 137 138 142 144 150 Fuel 18 19 20 19 21 23 22 23 24 26 The regression line equation is Round each number to four decimal places.
During oil drilling operations, components of the drilling assembly may suffer from sulfide stress cracking. An article reported on a study in which the composition of a standard grade of steel was analyzed. The following data on y = threshold stress (% SMYS) and x = yield strength (MPa) was read from a graph in the article (which also included the equation of the least squares line). X 634 643 712 709 y S = 100 92 87 83 835 78 (b) Compute the estimated standard deviation s 820 810 75 74 870 63 856 923 878 937 57 55 ΣΥ, = 10,575, ΣΥ, = 892, Σχιζ = 8,739,877, = 8,739,877, Σy? L x = 65,852, ExY, = 701,865 (a) What proportion of observed variation in stress can be attributed to the approximate linear relationship between the two variables? (Round your answer to four decimal places.) (Round your answer to four decimal places.) SB₁ 47 Does it appear that this true average change has been precisely estimated? O This is a fairly wide interval, so has been precisely estimated. 0 O This is a…

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