Q1: find the Reliability of component in the system in fig(1) by minimal cut method. Q2: A component A with constant failure rate 1.5 per 1000 h, B per to 2 in 1000h, A and B in parallel, find the Reliability system? [ by exponential distribution]. Q3: Give an example to find the minimal path and estimate the reliability of this block diagram. Q4: By Tie set method find the Reliability of fig (2) FUZ
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![Q1: find the Reliability of component in the system in fig(1) by minimal cut method.
Q2: A component A with constant failure rate 1.5 per 1000 h, B per to 2 in 1000h, A and B
in parallel, find the Reliability system? [ by exponential distribution].
Q3: Give an example to find the minimal path and estimate the reliability of this block
diagram.
Q4: By Tie set method find the Reliability of fig (2)
FUZ](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F0b438e26-5670-4582-adeb-c9c1fcb1f59f%2F5ba882a6-bb01-41f2-a41e-80ea792c7e35%2Fca3vd83_processed.jpeg&w=3840&q=75)
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- If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?2. (a) Find the line of best fit for the data points (-1,-4), (0, -1), (1, 2) and (3,8) and the least square error.iris is a built-in R data set (data frame). This data set contains gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica. We are interested in some descriptive statistics related to the Sepal.Width column of the data frame. We can access the data directly by using the assignment x <- iris$Sepal.Width (In R use ?iris for info on this dataset.)Remember: x <- iris$Sepal.Widtha.Calculate the sample median of x. b. Using the R quantile function, find the .80 quantile of x.(80th percentile) c. Calculate the interquartile range of x using R. d. Calculate the sample mean of x. e. Calculate a 4% trimmed mean for x. f. Calculate the sample variance of x. g. Calculate the sample standard deviation of x. h. What proportion of the x values are within 2.0 sample standard deviations from the sample mean i. Calculate the minimum…
- Interpret the least squares regression line of this data set. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The correct least squares regression line for the data set is: y = 8.116x + 273.273 Use it to complete the following sentence: The least squares regression line predicts an additional annual rainfall if the average temperature of coastal waters increases by one degree millimetres of Celsius.Suppose the least squares regression line for predicting weight (in pounds) from height (in inches) is given by Weight= -110+3.5*(height) Which of the following statements is correct? l. A person who is 61 inches tall will weigh 103.5 pounds ll. For each additional inch of height, weight will decrease on average by 3.5 pounds. lll. There is a negative linear relationship between height and weight. a) l and lll only b) l and ll only c) ll only d) l only e) ll and lll onlyThe authors of a paper compared two different methods for measuring body fat percentage. One method uses ultrasound, and the other method uses X-ray technology. The table gives body fat percentages for 16 athletes using each of these methods (a subset of the data given in a graph that appeared in the paper). For purposes of this exercise, you can assume that the 16 athletes who participated in this study are representative of the population of athletes. Athlete Ho: Md = 0 Ha: Ha 0 = 0 Ho: Md < 0 Ha: Md P-value = X-ray 4.75 7.00 9.25 12.00 5 17.25 6 29.50 7 5.50 8 6.00 9 8.00 10 8.50 11 9.25 12 11.00 13 12.00 14 14.00 15 17.00 16 18.00 = 0 1 2 3 4 USE SALT Ultrasound 4.75 4.00 9.00 11.75 17.00 27.75 6.50 6.75 8.75 9.75 9.50 12.00 12.25 15.75 18.00 18.50 Find the test statistic and P-value. (Use a table or SALT. Round your test statistic to one decimal place and your P-value to three decimal places.) t = 0.534 0.000 X X State the conclusion in the problem context. We fail to reiect H.…
- Heights (om) and weights (kg) are measured for 100 randomly selected adult males, and range from heights of 138 to 188 cm and weights of 41 to 150 kg. Let the predictor variable x be the first variable given. The 100 paired measurements yield x 167.74 cm, y 81.46 ko, r0.239, Pvalue 0.017, and y - 106 + 1,15x. Find the best predicted value of y (weight) given an adult male who is 153 cm tall. Use a 0.01 significance level. The best predicted value of y for an adult male who is 153 cm tall is kg. (Round to two decimal places as needed.)There are three controllable factors A (humidity), B (pressure) and c (temperature) in a Taguchi parameter design. The goal is to minimize the impurity of a chemistry product. the values of the mean response and SN ratio has been calculated from the observation and are displayed in the table below Outer array Run A SN 4. 3. 2 2. 4. 3. 6. 7. 6. 8. 3. 9. Use Taguchi Empirical optimization approach to identify the best design 6 6. 4. 4) 2) 2 2. 2. 3. 2. 2 2. 2] 3) 2. 4.Use the least squares regression line of this data set to predict a value. Meteorologists in a seaside town wanted to understand how their annual rainfall is affected by the temperature of coastal waters. For the past few years, they monitored the average temperature of coastal waters (in Celsius), x, as well as the annual rainfall (in millimetres), y. Rainfall statistics • The mean of the x-values is 11.503. • The mean of the y-values is 366.637. • The sample standard deviation of the x-values is 4.900. • The sample standard deviation of the y-values is 44.387. • The correlation coefficient of the data set is 0.896. The least squares regression line of this data set is: y = 8.116x + 273.273 How much rainfall does this line predict in a year if the average temperature of coastal waters is 15 degrees Celsius? Round your answer to the nearest integer. millimetres
- A scientist is calibrating a laboratory apparatus that will be used to measure the concentration of ozone in air samples. To check the calibration, samples of known concentration are measured. The true concentrations in ppm (x) and the measured concentrations in ppm (y) data points are: (0, 1), (10, 11), (20, 21), (30, 28), (40, 37), (50, 48), (60, 56), (70, 68), (80, 75), (90, 86), (100, 96). Because of random error, repeated measurements on the same sample will vary. The apparatus is considered to be in calibration if its mean response is equal to the true concentration. To check the calibration, the linear model y = ß0 + ß1 + ε is fit. Ideally, the value of ß0 should be 0 and the value of ß1 should be 1. A scientist is calibrating a laboratory apparatus that will be used to measure the concentration of ozone in air samples. To check the calibration, samples of known concentration are measured. The true concentrations in ppm (x) and the measured concentrations in ppm (y) data…A scientist is calibrating a laboratory apparatus that will be used to measure the concentration of ozone in air samples. To check the calibration, samples of known concentration are measured. The true concentrations in ppm (x) and the measured concentrations in ppm (y) data points are: (0, 1), (10, 11), (20, 21), (30, 28), (40, 37), (50, 48), (60, 56), (70, 68), (80, 75), (90, 86), (100, 96). Because of random error, repeated measurements on the same sample will vary. The apparatus is considered to be in calibration if its mean response is equal to the true concentration. To check the calibration, the linear model y = ß0 + ß1 + ε is fit. Ideally, the value of ß0 should be 0 and the value of ß1 should be 1. The least-squares estimate of the error standard deviation σ is closest to:2. Two different statistics. Statistic A and Statistic B. are used to estimate the same population parameter. Statistic A has less variability than Statistic B and Statistic A has less bias than Statistic B. Using the axes below, draw parallel dotplots showing 10 values of each statistic that are consistent with these characteristics. Assume that the parameter value is at the arrow on the axes. Statistic A Statistic B
