It is believed that the annual repair cost for the sporty automobile Jeep is related to its age. A sample of 11 automobiles revealed the results in the table at the right. Car age (xi) 2 3 1 7 5 8 1 2 6 9 4 Repair cost (yi) in £ 72 99 65 138 67 140 83 101 170 121 114 Define the terms regression and correlation analysis

It is believed that the annual repair cost for the sporty automobile Jeep is related to its age. A sample of 11 automobiles revealed the results in the table at the right. Car age (xi) 2 3 1 7 5 8 1 2 6 9 4 Repair cost (yi) in £ 72 99 65 138 67 140 83 101 170 121 114 Define the terms regression and correlation analysis

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 owners of a chain of ice cream stores have the business objective of improving the forecast of daily sales so that staffing shortages can be minimized during the summer season. As a starting point, the owners decide to develop a simple linear regression model to predict daily sales based on atmospheric temperature. They select a sample of 15 consecutive days. The results are shown in the accompanying table. Complete parts (a) through (f) below. Click the icon to view the data table. E Click the icon to view a table of critical values of the Durbin-Watson statistic. a. Assuming a linear relationship, use the least-squares method to compute the regression coefficients b, and b,. b, = - 2.7630 (Round to four decimal places as needed.) %3D Ice cream sales b, = (Round to four decimal places as needed.) %3D Day Sales ($1,000) Temperature (°F) 63 1 2 1.52 1.68 71 75 3 1.92 1.79 74 76 4 2.02 2.36 6. 81 2.16 2.69 81 8 84 2.91 88 10 3.17 90 11 12 3.02 3.27 90 93 99 13 3.42 2.84 88 83 14 15…
Listed below are paired data consisting of movie budget amounts and the corresponding amounts that the movies grossed. Find the regression equation, letting the budget be the predictor (x) variable. Find the best predicted amount that a movie will gross if its budget is $125 million. Use a significance level of alpha = 0.05. %3D Budget ($) in Millions 38 116 71 67 9. 60 120 154 Gross ($) in 108 103 129 110 54 96 212 287 Millions O A. The regression equation is y = 30.9 + 1.3x; Best predicted gross of $137.4 million В. The regression equation is y = 1.3 + 30.9x; Best predicted gross of $3863.8 million C. The regression equation is y = 1.3 + 30.9x; Best predicted gross of $137.4 million D. The regression equation is y = 30.9 + 1.3x; Best predicted gross of $193.4 million Click to select your answer. ?
It is considered that the number of employees in the enterprise affects the number of production. Data are given below. Which of the following is the simple linear regression equation?
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A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: River Falls Hudson Ellsworth Prescott Rock Elm Stillwater Select one: a. 78.39% b. 88.54% O c. 161.39% O d. 100% City What percentage of the total variation in the candy bar sales is explained by prices? e. 48.19% Price ($) 1.30 1.60 1.80 2.00 2.40 2.90 Sales 100 90 90 40 38 32
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The invasive diatom species Didymosphenia geminata has the potential to inflict substantial ecological and economic damage in rivers. An article described an investigation of colonization behavior. One aspect of particular interest was whether y = colony density was related to x = rock surface area. The article contained a scatterplot and summary of a regression analysis. Here is representative data. x 50 71 55 50 33 58 79 26 y 157 1934 53 27 7 10 40 12 x 69 44 37 70 20 45 49 y 274 43 176 18 48 190 30 (a) Fit the simple linear regression model to this data. (Round your numerical values to three decimal places.) y = Predict colony density when surface area = 70 and calculate the corresponding residual. (Round your answers to the nearest whole number.) colony density corresponding residual Predict colony density when surface area = 71 and calculate the corresponding residual. (Round your answers to the nearest whole number.) colony…
The mortality rate of a sample of the population as a function of daily tobacco consumption can be seen below: Number of cigarettes "x": 3 5 6 15 20 Mortality rate "y" : 0.2 0.3 0.4 0.5 0.7 With these data, solve the following: a. Determine and interpret the correlation coefficient. b. Find the regression line of y on x. c. What will be the mortality rate for a consumer of 40 cigarettes per day?
The following table contains ACT scores and the GPA for eight college students. Estimate the relationship between GPA (yi) and ACT (x;) using OLS regression by hand. Report the intercept and slope estimates: Student 1 2 3 4 5 6 7 8 GPA 2.8 3.4 3.0 3.5 3.6 3.0 2.7 3.7 ŷ₁ =B₁ + B₁x₁ ACT 21 24 26 27 29 25 25 30 y X ŷ₁
The total stopping distance (in feet) was measured for a midsize four-door sedan driving in dry conditions at various speeds. The resulting data are presented in the table below. Speed (in mph) 10 20 30 40 50 60 70 80 Total Stopping Distance 27 61 104 168 235 297 386 473 (a) Determine the linear regression model that will best predict the total stopping distance for a midsize four-door sedan driving dry conditions based on the speed of the vehicle. (b) How well does the linear regression model fit this sampe data? (c) Predict the total stopping distance for a midsize four-door sedan driving at a speed of 65 mph in dry conditions. Please no excel. I like seeing the work done so I can understand what I am doing.
Use the following table and information to answer questions 21 and 22: A company wants to build anew site with the number of bathrooms commensurate with the number of employees who will workthere. The analytics team decides to analyze a linear model of these data from extant sites to inform thisdecision before construction. Number of employees 650 730 810 900 102 107 1150 Number of bathrooms 40 50 54 61 82 110 121 21. The linear regression equation is:a. y-hat = 79.96 + 0.0094xb. y-hat = 0.0094 – 79.96xc. y-hat = 0.0094 – 79.96xd. y-hat = 79.96 – 0.0094x22. Predict how many bathrooms a new site with 1150 employees should have.a. 91b. 70c. 121d. The linear regression equation should not be used to make predictions.