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.2 5468 45 7y162357x 60 63 65 70 70 70 80 90 80 80 85 89 90 90 90 90 94 100 100 100(a) Test for significance of regression using a = 0.05. What is the P-value for this test? Round to 7 decimal places.(b) Estimate the standard errors of the slope and intercept.Standard error of the slope = i(Round to four decimal places.)Standard error of the intercept = i(Round to three decimal places.)(c) Test Ho: Bo = 0 versus H: Bo + 0 using a = 0.05. Find the P-value for this test.i(Round to six decimal places.)

Use EXCEL to determine the P-value. EXCEL procedure: Go to EXCEL Go to Data>Data Analysis.…

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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2. A study is conducted in patients with HIV. The primary outcome is CD4 cell count which is a measure of the stage of the disease. Lower CD4 counts are associated with more advanced disease. The investigators are interested in the association between vitamin and mineral supplements and CD4 count. A multiple regression analysis is performed relating CD4 count to use of supplements(coded as l ves, 0-no) and to duration of HIV, in years(ie., the number of years between the diagnosis of HIV and the study date). For the analysis, Y-CD4 count. Y 501.41 12.67 Supplements - 30.23 Duration of HIV A. What is the expected CD4 count for a patient taking supplements who has had HIV for 2.5 years? B. What is the expected CD4 count for a patient not taking supplements who was diagnosed with HIV at study enrollment? C. What is the expected CD4 count for a patient not taking supplements who has had HIV for 2.5 years
Managers of an outdoor coffee stand in Coast City are examing the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y, in dollars) and the maximum temperature (denoted by x, in degrees Fahrenheit) for each of fifteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. Also given are the products of the temperature values and coffee sales values for each of the fifteen days. (These products, written in the column labelled "xy," may aid in calculations.)
It is evaluated that there is a relationship between working experience in the business (X) and the number of daily errors (Y). Data are given below. What is the Regression Mean Square of Departure (RAKO) in the validity of the regression model?
Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y , in dollars) and the maximum temperature (denoted by x , in degrees Fahrenheit) for each of fifteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. Temperature, x(in degrees Fahrenheit) Coffee sales, y(in dollars) 53.1 2215.5 76.6 1982.7 70.1 1937.3 39.7 2251.1 72.5 1603.9 48.4 2024.2 46.7 2135.3 83.9 1536.5 58.4 1965.6 38.5 1944.3 68.2 1746.1 75.4 1472.1 65.6 1819.5 53.9 1627.4 45.1 1782.3 Send data to calculator Send data to Excel Coffee sales(in dollars) y 1200 1400 1600 1800 2000 2200 2400 x 40 50 60 70 80 90…
Consider the following data on y = number of songs stored on an MP3 player and x = number of months the user has owned the MP3 player for a sample of 15 owners of MP3 players. x y 22 485 34 748 2 81 28 581 5 117 32 728 23 445 10 128 4 61 26 476 1 35 8 121 13 266 9 126 5 141 What is the equation of the estimated regression line? (Round your numerical values to four decimal places.) y = Is the simple linear regression model useful for describing the relationship between x and y? Test the relevant hypotheses using a significance level of 0.05. Calculate the test statistic. (Round your answer to two decimal places.) t = find the P-value for this test? (Round your answer to four decimal places.) P-value=
Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y, in dollars) and the maximum temperature (denoted by x, in degrees Fahrenheit) for each of fifteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. Also given is the product of the temperature and the coffee sales for each of the fifteen days. (These products, written in the column labelled "xy", may aid in calculations.) Temperature, X 10 Coffee sales, y (in dollars) (in degrees Fahrenheit) 37.2 51.3 59.7 53.7 63.1 44.5 47.5 75.9 82.2 40.0 74.8 70.6 69.0 48.3 74.7 Send data to calculator V 2000.4 2205.9 1944.8 1579.3 1846.3 1797.3 2016.5 1563.3 1556.5 2249.4 1653.9 1923.0 1747.9 2154.6 1979.7 74,414.88 113,162.67 116,104.56 84,808.41…
Managers of an outdoor coffee stand in Coast City are examining the relationship between (hot) coffee sales and daily temperature, hoping to be able to predict a day's total coffee sales from the maximum temperature that day. The bivariate data values for the coffee sales (denoted by y, in dollars) and the maximum temperature (denoted by x, in degrees Fahrenheit) for each of sixteen randomly selected days during the past year are given below. These data are plotted in the scatter plot in Figure 1. Temperature, x (in degrees Fahrenheit) Coffee sales, y (in dollars) 71.5 1970.9 58.7 1953.9 53.7 1791.1 2400+ 83.0 1570.3 2200+ 62.8 1852.7 74.6 1633.6 2000- ** 40.4 1973.9 1800- 51.5 2250.9 44.6 1808.5 1600- 45.5 1977.3 1400- 45.6 2190.5 1200 69.4 1789.1 55.0 1598.2 50 70 80 90 40.6 2272.0 74.0 1937.0 Figure 1 76.6 1547.1 Send data to Excel Continue Submit Assignment O 2021 McGraw-Hill Education. All Rights Reserved. Terms of Use Privacy Accessibility here to search 99+ 5 4.
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)
In a certain type of metal test specimen, the normal stress on a specimen is known to be functionally related to the shear resistance. The following is a set of coded experimental data on the two variables: Normal Stress, x Shear Resistance, y 16 5 6. 12 8 9 4 15 7 7 (a) estimate the regression line (b) estimate the shear resistance for a normal stress of 10 (c) evaluate the unbiased estimate of the variance (d) a 95% confidence interval for the mean shear resistance when x = 10; (e) a 95% prediction interval for a single predicted value of the shear resistance when x = 10. WORK THIS ENTIRE PROBLEM WITH ALL 5 PARTS ,BEFORE PROCEEDING TO THE NEXT PROBLEM WRITE EVERYTHING DOWN ON YOUR ANSWER SHEETS. As.you proceed to the other problems, you will encounter this entire problem 4 more times. Each time you will be given a chance to answer a problem part. This time select your answer to part c)
The following data represent the speed at which a ball was hit (in miles per hour) and the distance it traveled (in feet) for a random sample of home runs in a Major League baseball game in 2018. Complete parts (a) through (f). Click here to view the data. Click here to view the critical values of the correlation coefficient. (a) Find the least-squares regression line treating speed at which the ball was hit as the explanatory variable and distance the ball traveled as the response variable. y = 5x+ (5 (Round to three decimal places as needed.) (b) Interpret the slope and y-intercept, if appropriate. Begin by interpreting the slope. A. The slope of this least-squares regression line says that the distance the ball travels increases by the slope with every 1 mile per hour increase in the speed that the ball was hit. O B. The slope of this least-squares regression line shows the increase in the speed that the ball was hit with every 1 foot increase in the distance that the ball was hit.…

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