Answered: The following table shows trip…

The following table shows trip generation data. Estimate b1 and b2 parameters using Regression method. Y=b1+b2 X HH No. Trips/day 4 3 10 4 13 16 17 20 8 19 20

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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Beth wants to determine a 90 percent confidence interval for the true proportion p of high school students in the area who attend their home basketball games. Out ofn randomly selected students she finds that that exactly half attend their home basketball games. About how large would n have to be to get a margin of error less than 0.04 for p?
The General Aviation Manufacturers Association has reported annual flying hours and fuel consumption for airplanes with a single, piston-driven engine as listed in file XR15057. Data are in millions of flying hours and millions of gallons of fuel, respectively. Determine the linear regression equation describing fuel consumption as a function of flying hours, then identify and interpret the slope, the coefficient of correlation, and the coefficient of determination. At the 0.05 level of significance, could the population slope and the population coefficient of correlation be zero? Determine the 95% confidence interval for the population slope Year Hours Gallons 1992 18400000 199100000 1993 17000000 184200000 1994 16400000 177200000 1995 17800000 192600000 1996 17600000 188400000 1997 18300000 196300000
A county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model, y = b₁x + bowhere y = appraised value of the house (in $thousands) and x = number of rooms. Using data collected for a sample of n=74 houses in East Meadow, the following results were obtained: y=74.80+ 17.80x Give a practical interpretation of the estimate of the slope of the least squares line. For each additional room in the house, we estimate the appraised value to increase $74,800. 1000 For each additional dollar of appraised value, we estimate the number of rooms in the house to increase by 17.80 rooms. For a house with 0 rooms, we estimate the appraised value to be $74,800. For each additional room in the house, we estimate the…
Management proposed the following regression model to predict sales at a fast-food outlet. y=B₁ + B₁x₁ + B₂x₂ + Bzx3 +6 where x₁ = number of competitors within one mile X₂= population within one mile (1,000s) x3 = [1 if drive-up window present lo otherwise y = sales ($1,000s). The following estimated regression equation was developed after 20 outlets were surveyed. 9-10.1-4.2x₁ + 6.8x₂ + 15.3x3 (a) What is the expected amount of sales (in dollars) attributable to the drive-up window? $ (b) Predict sales (in dollars) for a store with four competitors within one mile, a population of 8,000 within one mile, and no drive-up window. $ (c) Predict sales (in dollars) for a store with one competitor within one mile, a population of 3,000 within one mile, and a drive-up window. $
The following table shows worldwide sales and projected sales of a certain type of phone and their average selling prices in 2020, 2022, and 2024. Year 2020 2022 2024 Selling Price ($100) p 6 5 4.5 Sales (billions) q 1 1.2 1.3 Find the regression line. 9(p) = Use the regression line to estimate the demand (in millions of units sold) when the selling price was $250. X million 1.6 Need Help? Read It Master It
i. Determine the dependent and independent variable ii. Based on the output, state the simple linear regression model. ii. Interpret the linear regression model obtained in part (b).
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 163 1940 59 33 13 16 46 18 69 44 37 70 20 45 49 y 280 49 182 24 54 196 36 (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 colony density corresponding residual Predict colony density when surface area = 71 and calculate the corresponding residual. (Round your answers colony density corresponding residual How do the residuals compare? O The residuals for both points are negative. O The…
According to an article, one may be able to predict an individual's level of support for ecology based on demographic and ideological characteristics. The multiple regression model proposed by the authors was the following. y = 3.60-.01x₁+.01.₂-.07x3+.12x4+.02xs-.04x6-01-.04.xg-.02.xg+c The variables are defined as follows. y = ecology score (higher values indicate a greater concern for ecology) X₁ = age times 10 x₂ = income (in thousands of dollars) x3 = gender (1 = male, 0 = female) X4 = race (1 = white, 0 = nonwhite) X5 = education (in years) x6 = ideology (4 = conservative, 3 = right of center, 2 = middle of the road, 1 = left of center, and 0 = liberal) X7 = social class (4 = upper, 3 = upper middle, 2 = middle, 1 = lower middle, 0 = lower) xg = postmaterialist (1 if postmaterialist, 0 otherwise) x9 = materialist (1 if materialist, 0 otherwise) (a) Suppose you knew a person with the following characteristics: a 30 year old, white female with a college degree (20 years of…
Yummy Lunch Restaurant needs to decide the most profitable location for their business expansion. Marketing manager plans to use a multiple regression model to achieve their target. His model considers yearly revenue as the dependent variable. He found that number of people within 2KM (People), Mean household income(income), no of competitors and price as explanatory variables of company yearly revenue. The following is the descriptive statistics and regression output from Excel. Revenue People Income Competitors Price Mean 343965.68 5970.26 41522.96 2.8 5.68 Standard Error 5307.89863 139.0845281 582.1376385 0.142857 0.051030203 Median 345166.5 6032 41339.5 3 5.75 Mode #N/A 5917 #N/A 3 6 Standard Deviation 37532.51115 983.47613 4116.334718 1.010153 0.360838027 Sample Variance 1408689393 967225.2984 16944211.51 1.020408 0.130204082 Sum 17198284 298513 2076148…
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…
1) Draw the graph of the estimated regression below. Y₁ = Bo + B₁X₁B₂X² + B3X²