Consider the following three data sets, in which the vari-ables of interest are x = commuting distance andy = commuting time. Based on a scatterplot and the val-ues of s and r², in which situation would simple linearregression be most (least) effective, and why?Data Set123xyxyxy154216351745505163210508164415221842204520231949256325312046501155060SEX17.501270.83331270.8333STV29.502722.51431.6667βι1.6857142.1422951.126557Bo13.6666727.8688523.196729SST114.835897.51627.33SSE65.1065.1014.48

Answered: Consider the following three data sets,…

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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3.3 An electronics company is looking to develop a regression model to predict the number of units sold for a special running watch. Data is provided below Sales (units) Price ($) Advertising ($) Holiday 500 100 50 Yes 480 120 40 Yes 485 110 45 No 510 103 55 Yes 490 108 40 No 488 109 30 No 496 106 45 Yes (a) Determine the relationship between price and sales i.e. what is the effect of a unit change in price on sales and is it statistically significant? (b) Determine the relationship between advertising and sales i.e. what is the effect of a unit change in advertising on sales and is it statistically significant? (c) Determine the relationship between Holiday and sales i.e. what is the difference in sales between a holiday and a non-holiday and is it statistically significant? (c) Determine the relationship between Holiday and sales i.e. what is the difference in sales between a holiday and a non-holiday and is it statistically significant
Mike's Stables has two different possible cost drivers (trainer hours and number of horses) for budgeting the cost of horses training. The most recent results of the two regression analysis are as follows: Trainer's hours: SUMMARY OUTPUT – Regression Results Regression Statistics R Square 0.56 Coefficients t Stat Intercept 1,004.65 4.61 X Variable 1 $22.99 7.11 Number of horses: SUMMARY OUTPUT – Regression Results Regression Statistics R Square 0.63 Coefficients t Stat Intercept $5,240.95 4.44 X Variable 1 $951.48 3.50 What is the estimated total training horses cost for the coming year if 15,000 trainer hours are incurred and the stable has 350 horses to be trained, based upon the best cost driver?
The table shows the national expenditures for health care in billions, in a country for the selected years from 1990 to 2020. Given the projection cost for the year 2002, identify the type of regression point. Year Exprenditures ($billions) 1991 1020 1994 1356 1998 1984 2004 2984 2009 3374 2016 4765 2020 5937 Interpolation Extrapolation Neither Both
2. Find the least linear fit to the following data: * * 2 3 2 -3 -3 -1 55
The value of a sports franchise is directly related to the amount of revenue that a franchise can generate. The following data represents the value in 2014 (in $millions) and the annual revenue (in $millions) for the 30 Major League Baseball franchises. Suppose you want to develop a simple linear regression model to predict franchise value based on annual revenue generated. Team Revenue Value Baltimore 245 1000 Boston 370 2100 Chicago White Sox 227 975 Cleveland 207 825 Detroit 254 1125 Houston 175 800 Kansas City 231 700 Los Angeles Angels 304 1300 Minnesota 223 895 New York Yankees 508 3200 Oakland 202 725 Seattle 250 1100 Tampa Bay 188 625 Texas 266 1220 Toronto 226 870 Arizona 211 840 Atlanta 267 1150 Chicago Cubs 302 1800 Cincinnati 227 885 Colorado…
The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: N Video Get more help - # 3 Using the least-squares regression method, the trend equation for forecasting is (round your responses to two decimal places): ŷ=0+0x 30 A E D $ 4 R F % 5 T Month Jan Number of Accidents 25 G MacBook Air Y H U Feb 48 8 Mar 64 1 9 Apr 90 K O Clear all P
Consider the following data on x = rainfall volume (m³) and y = runoff volume (m³) for a particular location. 7 12 14 17 23 30 40 49 55 67 72 83 96 112 127 4 10 13 15 15 25 27 46 38 46 53 70 82 99 103 Use the accompanying Minitab output to decide whether there is a useful linear relationship between rainfall and runoff. The regression equation is runoff = -2.00 + 0.841 rainfall Predictor Coef Stdev t-ratio Constant -2.000 2.194 -0.91 0.379 rainfall 0.84079 0.03369 24.96 0.000 s = 4.823 R-sg = 98.0% R-sq (adj) = 97.8% State the appropriate null and alternative hypotheses. O Ho: B1 = 0 H: B, 0 O Hoi Bq # 0 H: B1 = 0 Compute the test statistic value and find the P-value. (Round your test statistic to two decimal places and your P-value to three decimal places.) t = P-value = State the conclusion in the problem context. (Use a = 0.05.) O Reject H.. There is a useful linear relationship between runoff and rainfall at the 0.05 level. O Reject H.. There is not a useful linear relationship…
The registrar at State University believes that decreases in thenumber of freshman applications that have been experiencedare directly related to tuition increases. They have collectedthe following enrollment and tuition data for the past decade: a. Develop a linear regression model for these data andforecast the number of applications for State Universityif tuition increases to $10,000 per year and if tuition islowered to $7000 per year.b. Determine the strength of the linear relationship betweenfreshman applications and tuition using correlation. c. Describe the various planning decisions for State Univer-sity that would be affected by the forecast for freshman applications.
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.

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