The following histograms were derived using the same data on cycles to failure of aluminum testcoupons. Compare the histograms. Do both display similar information?Histogram 8 bins:Hatogram of Cycles to falre (0 binm)1000 12 100 0 2000 22seCycles te fahre of ahum test peHistogram 16 bins:istogram of Cycles to fallr (16 kim)30-00 10 200eof mtet pa. Somewhat similar information. However the shape is similar and both are not symmetricOb. Yes, both give similar information. Both graphs are also not symmetricO. Yes, both give similar information. The mean value is located in the class interval having the largest frequencyO d. Not similar information considering the number of bins are different. But both are not symmetric in shapeAuanbaAanba

Histogram: Histogram is one of the popularly used graph for the representation of frequency…

Answered: The following histograms were derived…

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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Suppose samples of six different brands of diet or imitation margarine were analyzed to determine the level of physiologically active polyunsaturated fatty acids (PAPUFA, in percent), resulting in the data shown in the accompanying table. Imperial 14.1 13.6 14.6 14.3 Parkay 12.8 12.5 13.6 13.0 12.3 Blue Bonnet 13.5 13.4 14.1 14.5 Chiffon 13.2 12.7 12.6 14 Mazola 16.8 17.3 16.4 17.3 18.0 Fleischmann's 18.1 17.2 18.8 18.4 (a) Test for differences among the true means for PAPUFA percentages among the different brands. Use ? = 0.05. Calculate the test statistic. (Round your answer to two decimal places.) F = Use technology to find the P-value. (Round your answer to four decimal places.) P-value = What can you conclude? Reject H0. There is convincing evidence that the mean PAPUFA percentages for the six brands are not all equal.Fail to reject H0. There is convincing evidence that the mean PAPUFA percentages for the six brands are not all equal. Reject…
1a.calculate the i. mean ii. Mode
The impact of a low-calorie diet on laboratory mice's longevity is being investigated by a medical researcher. Twenty mice are split into two groups (E and F) by random. A typical diet is given to Group E, while Group F is given a diet that only contains 70% as many calories as Group E's diet. The length of life (in days) of each mouse over the 36-month trial is documented. Table 2 provides the data that was obtained. The symbol of double star (**) signifies that the mouse was still alive at the end of the investigation. (a) (b) (c) Table 2: The length of life (in days) of each mouse Group E 900 907 751 833 920 787 830 877 848 901 Group F 1037 905 1023 988 1078 1011 ** 1063 898 1033 State a suitable null and alternative hypotheses for the problem. At 5% significance level, test the hypotheses in part (a) by using an appropriate nonparametric test. Write your conclusion.
Assume that the following data came from a case-control study. CasesControlsExposed4050Unexposed60150Based on this table, what proportion of exposed cases would have been eliminated if the cases had not been exposed (assuming that the exposure does contribute to the disease). 50% 100% 0% 33.3%.
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…
A sample of 250 employees of a company is selected, and the average age is found to be 35 years. This value is a statistic or parameter.
For the following data 4,4,6, 10 o = 2.4 Then C.V.= Select one: O a. 25% O b. 47% Ос. 22.4% d. 40%
For which of the following would it be most appropriate to use an ANOVA to analyze the data? A researcher is interested in determining the most effective format for advertising. He randomly assigns 60 people to one of three groups: (1) television commercial, (2) radio commercial, or (3) magazine advertisement. Group members review the commercial and then report whether or not they would buy the product. In order to determine the effectiveness of different diet plans, 90 people are randomly assigned to one of three groups: (1) low calorie diet, (2) low-fat diet, or (3) low-carb diet. At the end of 60 days, the number of pounds lost was recorded for each of the participants. Both A and B. Neither A nor B.
A food-frequency questionnaire is used to measure dietary intake. The respondent specifies the number of servings of various food items they consumed over the previous week. The dietary cholesterol is then quantified for each respondent. The researchers were interested in assessing if there was an association between dietary cholesterol intake and high blood pressure. In a large sample of individuals who had completed the questionnaire, 250 persons with a high dietary cholesterol intake (greater than 300 mg /day) were selected and 250 persons with a low dietary cholesterol intake (less than 300 mg/day) were selected. The 500 selected participants had their medical history taken and were classified as having normal or high blood pressure. The data are given here. Dietary Cholesterol Blood Pressure Total High Low High 159 91 250 Low 78 172 250 Total 237 263 500 Test 2: two-tailed test for comparing two independent proportions Clearly state…
The following is data about the hemoglobin concentrations of volunteers collected at sea level and at an altitude of 11000 feet. sea level concentrations = [14.70 , 15.22, 15.28, 16.58, 15.10 , 15.66, 15.91, 14.41, 14.73, 15.09, 15.62, 14.92]11000 feet concentrations = [14.81, 15.68, 15.57, 16.59, 15.21, 15.69, 16.16, 14.68, 15.09, 15.30 , 16.15, 14.76] There are two alternative scenarios about the way the data were obtained. In scenario 1, there are 12 volunteers who lived for a month at sea level, at which time blood was drawn and the data in "sea level concentrations" dataset were obtained. Subsequently, all 12 volunteers were moved to 11000 ft and after a month the data in the "11000 feet concentrations" dataset obtained. There is a one to one correspondence between the numbers in the two datasets, that is the first numbers correspond to volunteer1, the second numbers to volunteer2 etc. In scenario 2, the "sea level concentrations" dataset is a random sample obtained from 12…
Refer to the data presented in Exercise 2.86. Note that there were 50% more accidents in the 25 to less than 30 age group than in the 20 to less than 25 age group. Does this suggest that the older group of drivers in this city is more accident- prone than the younger group? What other explanation might account for the difference in accident rates?
The following is data about the hemoglobin concentrations of volunteers collected at sea level and at an altitude of 11000 feet. sea level concentrations = [14.70 , 15.22, 15.28, 16.58, 15.10 , 15.66, 15.91, 14.41, 14.73, 15.09, 15.62, 14.92]11000 feet concentrations = [14.81, 15.68, 15.57, 16.59, 15.21, 15.69, 16.16, 14.68, 15.09, 15.30 , 16.15, 14.76] There are two alternative scenarios about the way the data were obtained. In scenario 1, there are 12 volunteers who lived for a month at sea level, at which time blood was drawn and the data in "sea level concentrations" dataset were obtained. Subsequently, all 12 volunteers were moved to 11000 ft and after a month the data in the "11000 feet concentrations" dataset obtained. There is a one to one correspondence between the numbers in the two datasets, that is the first numbers correspond to volunteer1, the second numbers to volunteer2 etc. In scenario 2, the "sea level concentrations" dataset is a random sample obtained from 12…

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The following histograms were derived using the same data on cycles to failure of aluminum test coupons. Compare the histograms. Do both display similar information?