bway ridership in Washington D. C. during the summer months is believed to be heavily tied to the number of tourists visiting the city. During the past 12 years, the following data have been obtained: Number of Tourists (in millions) 7 2 6 4 14 15 16 12 14 20 15 7 Ridership ( in 100,000) 15 10 13 15 25 27 24 20 27 44 34 17 a) Develop a linear regression model that would predict bus and subway ridership based on the number of tourists. b) Based on your model, predict the bus and subway ridership if there are eight million tourists in the city. c) Find the coefficient of determination and correlation co

bway ridership in Washington D. C. during the summer months is believed to be heavily tied to the number of tourists visiting the city. During the past 12 years, the following data have been obtained: Number of Tourists (in millions) 7 2 6 4 14 15 16 12 14 20 15 7 Ridership ( in 100,000) 15 10 13 15 25 27 24 20 27 44 34 17 a) Develop a linear regression model that would predict bus and subway ridership based on the number of tourists. b) Based on your model, predict the bus and subway ridership if there are eight million tourists in the city. c) Find the coefficient of determination and correlation co

Elementary Linear Algebra (MindTap Course List)

8th Edition

ISBN:9781305658004

Author:Ron Larson

Publisher:Ron Larson

Chapter2: Matrices

Section2.CR: Review Exercises

Problem 90CR

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A mail-order business selling personal computer supplies, software and hardware maintains a centralized warehouse. Management is currently examining the process of distribution from the warehouse and wants to study the factors that affect the warehouse distribution costs. Data collected over 24 random months contain the warehouse’s distribution cost (in thousands of Rands), the sales (in thousands of Rands) and the number of orders received. A multiple linear regression model was fitted to the data by using Stat1.2. Use the output to answer the questions that follow by typing only the letter of the correct option in the answer boxes. Variablesy: Warehouse Distribution Costx1: Salesx2: Number of Orders Model Fitting StatisticsR2 = 0.8504Adj R2: ? Regression Coefficients Beta Parameter Standard b Parameter Standard Estimates…
A sample of 20 automobiles was taken, and the miles per gallon (MPG), horsepower, and total weight were recorded. Develop a linear regression model to predict MPG, using horsepower as the only independent variable. Develop another model with weight as the independent variable. Which of these two models is better? Explain. MPG HORSEPOWER WEIGHT 44 67 1,844 44 50 1,998 40 62 1,752 37 69 1,980 37 66 1,797 34 63 2,199 35 90 2,404 32 99 2,611 30 63 3,236 28 91 2,606 26 94 2,580 26 88 2,507 25 124 2,922 22 97 2,434 20 114 3,248 21 102 2,812 18 114 3,382 18 142 3,197 16 153 4,380 16 139 4,036
Seven North American Green Frogs (Rana clamitans) had their jumping distance recorded (in mm) multiple times in a laboratory. The mean jumping distance for these frogs along with their length (measured from snout to vent in miMillimeters) are presented in the table below. Length of Frog 52 68 37 65 77 81 59 Mean Jumping Distance 546 673 415 659 793 814 563 (a) Determine the linear regression model that will best predit the mean jumping distance of a North American Green Frog based on the frog's length. (b) How well does the linear regression model fit this sample data? (c) Use the linear regression model to predict the mean jumping distance of a North American Green Frog that is 48 mm in length. No excel, please.
A statistics consulting center at a major university analyzed data on normal woodchucks for the university's veterinary school. The variables of interest were body weight in grams and heart weight in grams. It was desired to develop a linear regression equation in order to determine if there is a significant linear relationship between heart weight and total body weight. Use this information to answer the questions. Determine the test statistic. Body Weight Heart Weight4050 11.12435 10.13135 15.85740 10.92555 10.73665 11.42035 13.44260 13.92990 11.54935 15.83690 10.12880 12.82760 10.62160 15.42360 14.82040 13.62055 12.92650 15.62665 13.8
The following table displays the EPA fuel efficiency estimates (in miles per gallon) and the curbweight (in pounds) for a random sample of current year model vehicles.MPG 23 18 28 19 25 17 18 14Weight 3184 3598 2734 4082 2623 4685 4178 5488(a) Determine the linear regression model that will best predict the EPA fuel efficiency estimates(MPG) of a vehicle based on its curb weight.(b) How well does the linear regression model fit this sample data?(c) For a vehicle that weighs 4000 pounds, predict its EPA fuel efficiency estimate.
Carpet City wants to develop a means to forecast its carpet sales. The store manager believes that the store’s sales are directly related to the number of new housing starts in town. The manager has gathered data from county records on monthly house construction permits and from store records on monthly sales. These data are as follows: Monthly Carpet Monthly Construction Sales (1,000 yd.) Permits 5 21 10 35 4 10 3 12 8 16 2 9 12 41 11 15 9 18 14 26 a. Develop a linear regression model for these data and forecast carpet sales if 30 construction permits for new homes are filed. b. Determine the strength of the causal relationship between monthly sales and new home construction by using correlation.
The monthly premium quoted by an insurance company for a critical illness policy was collected from a sample of 6 adult male smokers at different age. The data for the sample are shown: Age 28 25 50 39 47 31 Premium ($) 75 40 175 125 250 105 Using Age to predict premium, the Linear Regression equation is given by: ŷ =6.556X−112 and r2=0.813y^=6.556X−112 and r2=0.813 a. Identify the independent and Dependent variables. Dependent: Age Premium Independent: Age Premium b. Determine the slope. Slope = Slope = Round to 3 decimal places c. Determine |r||r| . |r|=|r|= Round to 3 decimal places d. Interpret rr : and e. Determine critical r value at 5% significance level and determine if there is a significant linear correlation exists. |r| critical=|r| critical= Round to 3 decimal places Linear Correlation:Linear Correlation: Significant Not Significant f. Predict the monthly premium for a 40 years old adult male smoker.…
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.
Scores made by students in a statistics class in the midterm and final examination aregiven below: Student Mid-term Final 1 98 90 2 66 74 3 100 98 4 96 88 5 88 80 6 45 62 7 76 78 8 60 74 9 74 86 10 82 80 Develop a regression equation which may be used to predict final examination scoresfrom the mid-term score.
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 From the simple linear model , determine parameters and , with an interpretation of your linear model What would be the cost of repairing car that has been in use for 13 years
The owner of Showtime Movie Theaters, Inc. would like to predict weekly gross revenue as a function of advertising expenditures. Historical data for a sample of eight weeks follow. Weekly GrossRevenue($1000s) TelevisonAdvertising($1000s) NewspaperAdvertising($1000s) 96 5.0 1.5 90 2.0 2.0 95 4.0 1.5 92 2.5 2.5 95 3.0 3.3 94 3.5 2.3 94 2.5 4.2 94 3.0 2.5 Part A: Develop an estimated regression equation with the amount of television advertising as the independent variable. Part B: Develop an estimated regression equation with both television advertising and news paper advertising as independent variables. Part C: Is the estimated regression rquation coefficient for television advertising expenditures the same in part (a) and in part (b) ? Interpret the coefficient in each case. Part D : Predict Weekly gross revenue for a week $3500 is spent on television advertising and $1800 is spent on newspaper advertising? Please hurry
The owner of Showtime Movie Theaters, Inc., would like to predictweekly gross revenue as a function of advertising expenditures.Historical data for a sample of eight weeks follow. Weekly GrossRevenue($1000s) Television Advertising($1000s) Newspaper Advertising($1000s) 9690959295949494 5.02.04.02.53.03.52.53.0 1.52.01.52.53.32.34.22 .5 a. Develop an estimated regression equation with the amount oftelevision advertising as the independent variable.b. Develop an estimated regression equation with both televisionadvertising and newspaper advertising as the independent variables.c. Is the estimated regression equation coefficient for televisionadvertising expenditures the same in part (a) and in part (b)?Interpret the coefficient in each case.d. Predict weekly gross revenue for a week when $3500 is spent ontelevision advertising and $1800 is spent on newspaper advertising.