Q. 3 Following data present the effects of gasoline fume exposure on respiratory illness among automotive workers. Disease No disease Total Exposed Unexposed Total 70 130 200 15 185 200 85 315 400 (a) Find the risk ratio and interpret the result. (b) The person-time-at-risk that each automotive worker contributed to study as follows:

Q. 3 Following data present the effects of gasoline fume exposure on respiratory illness among automotive workers. Disease No disease Total Exposed Unexposed Total 70 130 200 15 185 200 85 315 400 (a) Find the risk ratio and interpret the result. (b) The person-time-at-risk that each automotive worker contributed to study as follows:

Name: Q. 3 Following data present the effects of gasoline fume exposureon r
Uploaded: 2024-03-20T07:06:16.000Z
Channel: Bartleby
Description: Q. 3 Following data present the effects of gasoline fume exposureon respiratory illness among automotive workers.DiseaseNo diseaseTotalExposedUnexposedTotal701302001518520085315400(a) Find the risk ratio and interpret the result.(b) The person-time-

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

The following raw data were collected by a health an analyst from 50 smokers. It represents Risk Index (R.I.) (measured as concentration ratio of certain compounds in the blood) which is used as a strong indicator for getting a cardiovascular (CV) disease. We would like to study the effect of each of the three factors (age, mass, and No. of cigarettes) on the R.I. : Answer the following: 1. Classify the data into 3 groups according to age: Young: age < 35, Middle aged: between 36 and 59, and Old: age > 60. Then study if the Risk Index is affected by age class. 2. Classify the data into 3 groups according to body mass: regular mass <70, fat mass between 71 and 99, and obese mass above 100. Then study if the mass has an influence on the R.I. 3. Produce a linear mathematical model that can predict the R.I. based on age, mass and number of cigarettes per day. Use a confidence level of 99%. 4. Comment on your linear model. Is there an evidence that a nonlinear model shall be…
Need help asap pls!
QUESTION 4 A steel manufacturing company wishes to compare the strength of the wires produced by an existing and a new process. The manufacturer will use the existing process unless the inerease in the mean strength in the new process is significant The following data represents the strength of the steel wires made by the two processes: Process Strength of steel wires (megapascal, MPa) 1334 1330 13 10 1340 Existing 1365 1286 1320 1333 1300 1350 New 1300 1310 1340 1342 1390 1340 1350 Assuming that the strengths of the steel wire using the existing and new process are approximately normal a) Is there evidence that the steel manufacturing company should use the existing process to make the steel wires. Use the probability of Type I error at 0 01. Assume that the standard deviation does not change.
Pn.n.m.
Serum estradiol is an important risk factor for breast cancer in postmenopausal women. To better understand the etiology of breast cancer, serum-estradiol samples were collected from 25 premenopausal women (at about the same time period of the menstrual cycle) of whom 10 were Caucasian and 15 were African American. The data are shown in Table 7.3. The distribution of serum estradiol is usually highly skewed (especially for premenopausal women), and we are reluctant to assume normality. Table 7.3 Serum estradiol and ethnicity in 25 premenopausal women, where 0=Caucasian and 1=African-American ID Serum_estradiol Ethic 1 94 0 2 54 1 3 31 0 4 21 1 5 46 1 6 56 0 7 18 1 8 19 1 9 12 1 10 14 0 11 25 0 12 35 1 13 22 1 14 71 0 15 43 1 16 35 1 17 42 1 18 50 1 19 44 1 20 41 0 21 28 0 22 65 0 23 31 0 24…
The accompanying data were read from a plot (and are a subset of the complete data set) given in an article. The data represent the mean response times for a group of individuals with closed-head injury (CHI) and a matched control group without head injury on 10 different tasks. Each observation was based on a different study, and used different subjects, so it is reasonable to assume that the observations are independent. y = Study 1 2 3 4 5 6 7 8 9 10 Mean Response Time CHI 302 492 658 684 922 1044 1421 1329 1481 1815 t = Control 250 360 475 (a) Fit a linear regression model that would allow you to predict the mean response time for those suffering a closed-head injury from the mean response time on the same task for individuals with no head injury. (Round your answers to four decimal places.) 525 610 740 880 920 1010 1200 (b) Do the sample data support the hypothesis that there is a useful linear relationship between the mean response time for individuals with no head injury and the…
For each of the following epidemiological scenarios, draw a DAG that represents the relationships between the variables: A researcher is studying the association between experiencing childhood abuse (A) and the occurrence of depression (D) as an adult. She has reason to believe that alcohol consumption (C) may mediate the relationship between (A) and (D).
Serum estradiol is an important risk factor for breast cancer in postmenopausal women. To better understand the etiology of breast cancer, serum-estradiol samples were collected from 25 premenopausal women (at about the same time period of the menstrual cycle) of whom 10 were Caucasian and 15 were African American. The data are shown in Table 7.3. The distribution of serum estradiol is usually highly skewed (especially for premenopausal women), and we are reluctant to assume normality. Table 7.3 Serum estradiol and ethnicity in 25 premenopausal women, where 0=Caucasian and 1=African-American. ID Serum_estradiol Ethic 1 94 0 2 54 1 3 31 0 4 21 1 5 46 1 6 56 0 7 18 1 8 19 1 9 12 1 10 14 0 11 25 0 12 35 1 13 22 1 14 71 0 15 43 1 16 35 1 17 42 1 18 50 1 19 44 1 20 41 0 21 28 0 22 65 0 23 31 0 24…
QUESTION 2 An environmentalist wanted to determine if the mean acidity of rain differed among Ipoh, Johor Bahru and Kuantan. He randomly selected six rain dates at each city and obtained the following data: Table 2 Ipoh 5.10 5.00 4.91 5.12 4.78 5.20 Johor Bahru 4.80 4.15 4.42 4.65 4.87 4.10 Kuantan 5.45 6.23 5.55 5.10 5.42 5.25 Answer the following questions without using any output from any software. a) Construct a complete Analysis of Variance table. b) At <= 0.05, test whether the mean acidity of rain is different among the three cities. c) Based on the answer in (a) and (b), write a brief conclusion on the mean acidity of rain among Ipoh, Johor Bahru and Kuantan.
Johnson and Leone describe an experiment to investigate the warping of copper plates. The two factors studied were temperature and the copper content of the plates. The response variable was a measure of the amount of warping. The data are as follows: Temperature (°C) 50 75 100 Copper Content (%) 40 60 80 100 17 20 12 9 16 12 21 17 21 16 24 21 18 13 18 21 ZZFEHERRE 23 22 27 17 27 31 30 23 29 31 12 25 23 23 28 22 125 Source: Applied Statistics and Probability for Engineers, 6th Edition, Douglas, C. Montgomery & George, C. Runger. 1. Draw and interpret the interaction plot. ii. Is there any significant interaction between the factors? Use a = 0.05. ii. Can your interpretation in (i) be validated in (ii)? Twelve plants are randomly