An article in the Journal of Sound and Vibration (Vol. 151, 1991, pp. 383-394) described a study investigating the relationship betweennoise exposure and hypertension. The following data are representative of those reported in the article.уху х160 5 85063 4 891 65 6 902 70 8 905 70 4 901 70 5 90480 7 946 90 9 1002 80 7 1003 80 6 100Fit a linear regression model relating blood pressure rise in millimeters of mercury (y) to sound pressure level in decibels (x) using leastsquares.(a) Draw a scatter diagram of y versus x (using statistical software). Does a simple linear regression model seem reasonable in thissituation?(b) Calculate the least squares estimate of the slope. ß, =i(Round your answer to 3 decimal places.)(Round your answer to 3 decimal(c) Calculate the least squares estimate of the intercept./ =places.)(d) Calculate an estimate of o2.(Round your answer to 3 decimal places.)(e) Obtain the predicted mean rise in blood pressure level associated with a sound pressure of 67 decibels.μYXO(Round your answer to 3 decimal places.)ww

Answered: Image /qna-images/answer/ed4622a7-336c-4b50-bf75-047aeb100cdd.jpg

Answered: An article in the Journal of Sound and…

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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Bus and subway ridership in Washington D. C. during the summer months is believed to be heavily tied to thenumber 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 coefficient between the two variables.
The age (in weeks) and the number of hours slept in a day were recorded for arandom sample of 8 infants.Age (in weeks) 5 10 15 22 26 36 42 47Hours slept ina day14.9 14.5 14.7 14.2 13.4 14.1 13.4 13.7(a) Find the regression line that predicts the number of hours an infant sleeps ina day based on its age (in weeks).(b) How well does the regression line fit the data? Use words and statistics tosupport your answer.(c) Predict the number of hours that a 30-week-old infant would sleep in a day.Use words to state your answer.
The table contains data on vehicle speed (h) and fuel consumption (lt / 100km) of 5 randomly selected vehicles. Estimate the average fuel consumption of a vehicle traveling at 45 km / h using the simple linear regression equation between vehicle speed and fuel consumption. Speed 55 60 65 70 75 Consumption 13 12 11 10 9 a. 15 b. 8 c. 7 d. 20
Suppose the following data were collected from a sample of 1515 CEOs relating annual salary to years of experience and the economic sector their company belongs to. Use statistical software to find the following regression equation: SALARYi=b0+b1EXPERIENCEi+b2SERVICEi+b3INDUSTRIALi+eiSALARY�=�0+�1EXPERIENCE�+�2SERVICE�+�3INDUSTRIAL�+��. Is there enough evidence to support the claim that on average, CEOs in the service sector have lower salaries than CEOs in the financial sector at the 0.010.01 level of significance? If yes, write the regression equation in the spaces provided with answers rounded to two decimal places. Else, select "There is not enough evidence." Copy Data CEO Salaries Salary Experience Service (1 if service sector, 0 otherwise) Industrial (1 if industrial sector, 0 otherwise) Financial (1 if financial sector, 0 otherwise) 144225144225 1010 11 00 00 187765187765 2020 00 00 11 142500142500 66 11 00 00 169650169650 2828 11 00 00 167250167250 3131 00…
We have data from 209 publicly traded companies (circa 2010) indicating sales and compensation information at the firm-level. We are interested in predicting a company's sales based on the CEO's salary. The variable sales; represents firm i's annual sales in millions of dollars. The variable salary; represents the salary of a firm i's CEO in thousands of dollars. We use least-squares to estimate the linear regression sales; = a + ßsalary; + ei and get the following regression results: . regress sales salary Source Model Residual Total sales salary cons SS 337920405 2.3180e+10 2.3518e+10 df 1 207 208 Coef. Std. Err. .9287785 .5346574 5733.917 1002.477 MS 337920405 111980203 113066454 Number of obs F (1, 207) Prob > F R-squared t P>|t| = Adj R-squared = Root MSE 1.74 0.084 5.72 0.000 = = -.1252934 3757.543 = 209 3.02 0.0838 0.0144 0.0096 10582 [95% Conf. Interval] 1.98285 7710.291 This output tells us the regression line equation is sales = 5,733.917 +0.9287785 salary. Interpret the…
A company sets different prices for a particular DVD system in eight differentregions of the country. Table 1 shows the numbers of units sold and thecorresponding unit prices (in U.S. Dollars).Table 1: Sales DataRegion Sales (Units) Price (U.S. Dollars)1 420 1042 380 1953 350 1484 400 2045 440 966 380 2567 450 1418 420 109You are given the following regression model:Salesi = 1 + 2Pricei + uiwhere i is region, Sales are number of units sold, Price is unit price inU.S. Dollars, and u is a random error term.Based on the data:1. Estimate the regression model. 2. Interpret the estimated regression equation. 3. Compute the standard errors of the estimated coefficients. 4. Test the statistical significance of 2 at the 5% level of significance.5. Compute R2 and interpret it.
The following data shows memory scores collected from adults of different ages. Age (X) Memory Score (Y) 25 10 32 10 39 9 48 9 56 7 Use the data to find the regression equation for predicting memory scores from age. The regression equation is: Ŷ = 4.33X + 0.11 Ŷ = -0.11X + 4.33 Ŷ = -0.11X + 13.26 Ŷ = -0.09X + 5.4 Ŷ = -0.09X + 12.6 Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 28 For the calculations, leave two places after the decimal point and do not round: Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 43 For the calculations, leave two places after the decimal point and do not round: Use the regression equation you found in question 6 to find the predicted memory scores for the following age: 50 For the calculations, leave two places after the decimal point and do not round:
From the article “Association of cognitive functioning with retinal nerve fiber layer thickness” by van Koolwijk et al., in Investigative Ophthalmology & Visual Science, October 2009, Vol. 50, No. 10, below is table 2 showing the results of fitting several multiple linear regression models for different response variables. Write down the fitted model corresponding to the last row of the table. You can leave the intercept as hatB0. Interpret the coefficient values corresponding to the RNFL Thickness and Male variables.
A researcher is interested in examining the relationship between spousal abuse and child abuse. Specifically, they are interested in determining whether there is a predictive relationship between spousal abuse and child abuse in 5 county social services offices. Calculate the linear regression line for the following data. Note you have already calculated the first step to this analysis (Pearson's Correlation)
A researcher interested in explaining the level of foreign reserves for the country of Barbados estimated the following multiple regression model using yearly data spanning the period 2001 to 2016: ??=?+????+????+???? Where FR = yearly foreign reserves ($000’s), OIL = annual oil prices, EXP = yearly total exports ($000’s) and FDI = annual foreign direct investment ($000’s). The sample of data was processed using MINITAB and the following is an extract of the output obtained: Predictor Coef StDev t-ratio p-value Constant 5491.38 2508.81 2.1888 0.0491 OIL 85.39 18.46 4.626 0.0006 EXP -377.08 112.19 * 0.0057 FDI -396.99 160.66 -2.471 ** S = 2.45 R-sq = 96.3% R-sq(adj) = 95.3%…

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