Exercise:a) Derive the maximum likelihood estimate (MLE) and estimate its variance for theWeibull distribution with right-censored data.=b) Simulate a data set of n 100 from the Weibull distribution with λ = 1, K = 2, andA kuniform (0,7) censoring. Play with values of 7 so that about 20% of the observations arecensored.c) Find an R function/package that can calculate the MLE of the Weibull distributionwith right-censored data [Hint: try mle() in package ‘stats4', or you may write your ownfunction], apply it to the data you generated in part b); be sure to provide the standarderror of your estimate.

a) To derive the maximum likelihood estimate (MLE) and estimate its variance for the Weibull…

Answered: Exercise: a) Derive the maximum…

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. Five 2 cm thick experimental pieces of an insulation material were tested atrandom. Resulted compression (y) was measured recorded for the given amount ofmeasured pressure. The data are listed below:Pressure Compression1 12 23 24 35 5Fit a simple linear model relating compression y to the applied pressure x and find:a) error variance.b) the coefficient of correlation.
In a production process, the time (in minutes) taken (run time) for a production run and the number of items produced (run size) for 15 randomly selected orders are analyzed using Minitab statistical software. The Minitab output is as follows: Regression Analysis: Run time versus Run size Analysis of Variance Source DF Adj SS Adj MS F-Value Critical value Regression 1 8737.1 8737.1 29.35 ……… Error 13 3870.5 297.7 Total 14 12607.6 Model Summary S R-sq R-sq(adj) R-sq(pred) 17.2549 ……. 66.94% 61.32% Coefficients Term Coef SE Coef T-Value P-Value VIF Constant 148.4 11.3 13.13 0.000 Run size 0.2627 0.0485 ……. ……. 1.00 1- Write down the least square regression equation to predict the run time for run size. 2- Interpret the coefficients of the fitted model. 3- At 5% significance level, test if run size is a good…
An economist studying inflation in electricity prices in 2018 and 2019 believes that the average price of electricity, even after adjusting for inflation, changed between these two years. To test his claim, he samples 9 different counties and records the average price of electricity in each county from each year. He then adjusts the prices for inflation. His results are given in the following table. Test the economist's claim at the 0.05 level of significance assuming that the population distribution of the paired differences is approximately normal. Let prices in 2018 be Population 1 and prices in 2019 be Population 2. Average Residential Retail Prices of Electricity ($/kWh) 2018 2019 12.64 13.86 14.22 12.45 14.45 12.85 17.58 15.69 13.42 11.64 15.07 14.72 15.12 14.53 11.20 10.51 18.15 16.70 Copy Data Step 2 of 3: Compute the value of the test statistic. Round your answer to three decimal places.
An economist studying inflation in electricity prices in 2018 and 2019 believes that the average price of electricity, even after adjusting for inflation, changed between these two years. To test his claim, he samples 9 different counties and records the average price of electricity in each county from each year. He then adjusts the prices for inflation. His results are given in the following table. Test the economist’s claim at the 0.10 level of significance assuming that the population distribution of the paired differences is approximately normal. Let prices in 2018 be Population 1 and prices in 2019 be Population 2. Average Residential Retail Prices of Electricity ($/kWh) 2018 2019 19.99 18.65 15.30 17.29 14.40 16.17 17.31 18.68 12.85 13.53 12.41 13.11 14.74 13.13 19.15 19.36 11.84 13.69 Step 3 of 3 : Draw a conclusion and interpret the decision. 1. We fail to reject the null hypothesis and conclude that there is insufficent evidence at a…
Many properties of the fabric used in different textiles, such as sheets and clothing, are influenced by whether the diameter of yarn used in the creation of the textile is consistent. Therefore, various methods can be used to measure the diameter of yarn at specified intervals, such as every 2mm, to determine the consistency of the diameter. These measurements are normally distributed. Suppose that one textile manufacturer will not use any yarn in which the variance of the diameters is greater than 0.0009mm. In order to ensure that the yarn is usable, the diameter of a length of yarn is measured at 400 random intervals. The variance of those measurements is found to be 0.000992mm. Does this evidence provide support that the batch of yarn is unusable by the manufacturer? Use a 0.005 level of significance. Step 2 of 3 : Compute the value of the test statistic. Round your answer to three decimal places.
An economist studying inflation in electricity prices in 2018 and 2019 believes that the average price of electricity, even after adjusting for inflation, changed between these two years. To test his claim, he samples 9 different counties and records the average price of electricity in each county from each year. He then adjusts the prices for inflation. His results are given in the following table. Test the economist’s claim at the 0.05 level of significance assuming that the population distribution of the paired differences is approximately normal. Let prices in 2018 be Population 1 and prices in 2019 be Population 2. Average Residential Retail Prices of Electricity ($/kWh) 2018 2019 19.32 18.19 15.00 13.69 11.20 12.72 14.48 14.51 14.15 12.23 19.17 17.21 13.13 11.29 17.62 15.89 19.06 17.71 Step 1 of 3: State the null and alternative hypotheses for the test. Fill in the blank below Step 2 of 3: Compute the value of the test statistic. Round your…
An economist studying inflation in electricity prices in 2018 and 2019 believes that the average price of electricity, even after adjusting for inflation, changed between these two years. To test his claim, he samples 9 different counties and records the average price of electricity in each county from each year. He then adjusts the prices for inflation. His results are given in the following table. Test the economist's claim at the 0.02 level of significance assuming that the population distribution of the paired differences is approximately normal. Let prices in 2018 be Population 1 and prices in 2019 be Population 2. Average Residential Retail Prices of Electricity ($/kWh) 2018 2019 12.62 11.24 16.01 17.99 12.59 14.44 19.94 21.43 17.75 19.24 15.91 17.39 18.89 19.94 15.85 16.67 13.90 15.31 Copy Data Step 2 of 3: Compute the value of the test statistic. Round your answer to three decimal places.

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