Regression methods were used to analyze the data from a study investigating the relationship between roadway surface temperature(x) and pavement deflection (y). The simple linear regression model is ŷ = 0.33 + 0.0042x.(a) Suppose that temperature is measured in °C rather than °F. Determine regression coefficients for the new model ŷ = ßo + B ¡x.Round your answer to two decimal places (e.g. 98.76).iRound your answer to four decimal places (e.g. 98.7654).(b) What change in expected pavement deflection is associated with a 1°C change in surface temperature?Round your answer to four decimal places (e.g. 98.7654).

Given: Regression methods to study the relationship between surface temperature and pavement…

Answered: Regression methods were used to analyze…

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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2. A high school track & field coach wanted to assess the relationship between an athletes height and how far they can jump in the long jump event (both in inches). They collect data on each athletes height and how far they can jump. Let the height in inches of the athlete be the explanatory variable (X) and the distance in inches of the jump be the response (Y). The scatterplot of the data based on 32 athletes is as follows: distance 88 86 84 82 80 78 00 O o 70 Scatter Plot 72 height 00 000 00 00 8 74 8 O ¥75 76 一念 78
Please answer Part 2: c, d and e
A study of the amount of rainfall and the quantity of air pollution removed produced the accompanying data. Complete parts (a) and (b) below. Click the icon to view the daily rainfall and air pollution data. (a) Find the equation of the regression line to predict the particulate removed from the amount of daily rainfall. The equation is y = 154.76 + (6.72 )x. (Round to two decimal places as needed.)
After entering data and doing linear regression on her calculator, Ellie got the screen shown below. Write the line of best fit in slope-intercept form to the nearest two decimal places. Do not use spaces in your answer. Linke3 y=ax+b a=-1.98062954 b=102.2009685 r2=.7685744609 r=-.8766837862
microorganisms that break down these compounds. BOD is hard to measure accurately. Total organic carbon (TOC) is easy to measure, so it is common to measure TOC and use regression to predict BOD. A typical regression equation for water entering a municipal treatment plant is BOD= -55.42 + 1.507 TOC Both BOD and TOC are measured in milligrams per liter of water. (a) What does the slope of this line say about the relationship between BOD and TOC? O TOC rises (falls) by 1.507 mg/l for every 1 mg/l increase (decrease) in BOD O BOD rises (falls) by 1.507 mg/l for every 1 mg/l increase (decrease) in TOC O TOC rises (falls) by 1.507 mg/l for every 55.42 mg/l increase (decrease) in BOD O BOD rises (falls) by 55.42 mg/l for every 1 mg/l increase (decrease) in TOC (b) What is the predicted BOD when TOC = 0? Values of BOD less than 0 are impossible. Why do you think the prediction gives an impossible value? This arises from extrapolation; the data used to find this regression formula must not…
The accompanying data are the number of wins and the earned run averages (mean number of earned runs allowed per nine innings pitched) for eight baseball pitchers in a recent season. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x = 5 wins (b) x = 10 wins (c) x = 19 wins (d) x = 15 wins E Click the icon to view the table of numbers of wins and earned run average. The equation of the regression line is y =x+ (Round to two decimal places as needed.) Construct a scatter plot of the data and draw the regression line. Choose the correct graph below. OA. OB. OC. OD. AERA 6- AERA AERA AERA 2- 2- 2- 0- 0- 12 18 24 12 18 24 12 18 24 12 18 24 Wins Wins Wins Wins (a) Predict the ERA for 5 wins, if it is meaningful. Select the correct choice below…
Use the regression line to make the appropriate prediction. A random sample of records of electricity usage of homes in the month of July gives the amount of electricity used and size (in square feet) of 135 homes. A regression was done to predict the amount of eleçtricity used (in kilowatt-hours) (y) from size (x). The residuals plot indicated that a inear model is appropriate. The model is y = 0.2x+ 1271. How much electricity would you predict would be used in a house that is 2471 square feet? 494.2 kilowatt-hours O 6000.00 kilowatt-hours O 1765.2 kilowatt-hours O 776.8 kilowatt-hours 3742.2 kilowatt-hours
The accompanying data are the number of wins and the earned run averages (mean number of earned runs allowed per nine innings pitched) for eight baseball pitchers in a recent season. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x = 5 wins (b) x = 10 wins (c) x = 19 wins (d) x = 15 wins Click the icon to view the table of numbers of wins and earned run average. The equation of the regression line is y = x+. (Round to two decimal places as needed.)
Explain the value of the coefficient of determination and the overall significance of the model. Write the resulting regression equation.
Multiple regression analysis was used to study how an individual's income (Y in thousands of dollars) is influenced by age (X1 in years), level of education (X2 ranging from 1 to 5), and the person's gender (X3 where 0 =female and 1=male). The following is a partial result of computer output that was used on a sample of 20 individuals. Present the estimated regression equation and compute the coefficient of determination. Explain it. Use the t test to determine the significance of each independent variable. Let α = 0.05. (For each test, give the null and alternative hypotheses, test statistic, and conclusion.) Use the F test to determine whether or not the regression model is significant. Let α = 0.05. (For the test, give the null and alternative hypotheses, test statistic, and conclusion.) Does the estimated regression equation provide a good fit for the observed data? Explain it. Suppose a new person with X1=40, X2=4, X3=0. Use the estimated regression equation in part (a)…
The accompanying data are the number of wins and the earned run averages (mean number of earned runs allowed per nine innings pitched) for eight baseball pitchers in a recent season. Find the equation of the regression line. Then construct a scatter plot of the data and draw the regression line. Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. If the x-value is not meaningful to predict the value of y, explain why not. (a) x = 5 wins (c) x = 19 wins (d) x = 15 wins (b)x= 10 wins Click the icon to view the table of numbers of wins and earned run average. .. The equation of the regression line is y=x+ X+ (Round to two decimal places as needed.) Construct a scatter plot of the data and draw the regression line. Choose the correct graph below. OA. B. O C. O D. AERA AERA 6+ Q AERA 6+ 4- 4- 2- 2- 0- 6 12 18 24 0 12 18 24 6 12 18 24 Wins 12 18 24 Wins Wins Wins (a) Predict the ERA for 5 wins, if it is meaningful. Select the correct…
A real estate company wants to study the relationship between house sales prices and some important predictors of sales prices. Based on data from recently sold homes in the space, the following variables are used in a multiple regression model. y = sales price (in thousands of dollars) x₁ = total floor area (in square feet) x₂ = number of bedrooms x3 distance to nearest high school (in miles) = The estimated model is as follows. =76+0.098x₁ +16x₂ - 8x3 Answer the questions below for the interpretation of the coefficient of X₂ in this model. (a) Holding the other variables fixed, what is the average change in sales price for each additional bedroom in a house? dollars (b) Is this change an increase or a decrease? O increase O decrease X

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