Two different box-filling machines are used on an assembly line. The critical measurement influenced by these machines is the weight ofthe product in the boxes. Engineers are quite certain that the variance of the weight of product is o² =3 ounces. Experiments areconducted using both machines with sample sizes of 81 each. The sample averages for machines A and B are XA = 12.1 ounces and= 12.3 ounces. Engineers are surprised that the two sample averages for the filling machines are so different. Complete parts (a)XB =and (b) below.Click here to view page 1 of the standard normal distribution table.Click here to view page 2 of the standard normal distribution table.(a) Use the Central Limit Theorem to determine P(XB-XA20.2) under the condition that HA = HB.P(XB-XA20.2) =4Get more help -Clear allCheck answer(1)

Answer Given Sample size[n1] =81 sample size [n2] =81 Variance [A] =3 Variance [B] =3

Answered: Two different box-filling machines are…

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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In a human population, the proportion of individuals with a certain disease is 0.10. if we select a sample of size 1000 from this population, determine the value of the variance of the sample proportion.
Earlier this semester, we learned to use 2-Sample-TTest to compare the population means of two independent populations. One-way ANOVA is more powerful because it could compare the population means of three or more independent populations. However, use of one-way ANOVA also requires the assumption that the populations have the same variance. In this exercise, we will compare and contrast One-way ANOVA and 2-Sample-TTest. With the 2-Sample- TTEST, we will first choose 'No' for the pooled variances option, then re-run the test while choosing 'Yes' for the pooled variances option. Use the following data to complete these tasks. Depending on if you choose to do this by hand or using code, use the data format that suits you best - LONG form on top or WIDE form on bottom. Treatment One One One One One One One One Two Two Two Two Two Two Two Two Treatment Treatment One Two 5.6 4.7 3.6 3.9 5.2 4.3 4.1 4.8 Response 5.6 4.7 3.6 3.9 5.2 4.3 4.1 4.8 3.4 2.3 4.4 3.5 3.7 5.8 2.8 4.7 p-value= 3.4 2.3…
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I need help solving part a and b of the following question attached.
By how many times does the sample size have to be increased to decrease the margin of error by a facgtor of 1/9?
The engineer of large manufacturing company takes many measurements of a specific dimension of components from the production line. She finds that the distribution of dimensions is approximately normal, with a mean of 3.33 cm and a coefficient of variation of 3.60%. i. What percentage of measurements will be less than 3.50 cm? What percentage of dimensions will be between 3.10 cm and 3.50 cm? What percentage of measurements will be equal to 3.50 cm? What value of the dimension will be exceeded by 90% of the components? ii. iii. iv.
Earlier this semester, we learned to use 2-Sample-TTest to compare the population means of two independent populations. One-way ANOVA is more powerful because it could compare the population means of three or more independent populations. However, use of one-way ANOVA also requires the assumption that the populations have the same variance. In this exercise, we will compare and contrast One-way ANOVA and 2-Sample-TTest. With the 2-Sample- TTEST, we will first choose 'No' for the pooled variances option, then re-run the test while choosing 'Yes' for the pooled variances option. Use the following data to complete these tasks. Depending on if you choose to do this by hand or using code, use the data format that suits you best - LONG form on top or WIDE form on bottom. Treatment One One One One One One One Two Two Two Two Two Two Two Treatment One 7.1 8.5 7.6 7.7 6.9 8.5 7.8 Response 7.1 8.5 7.6 7.7 6.9 8.5 7.8 1.4 4.1 4.3 3.9 4 4 4.6 p-value= Treatment Two 1.4 4.1 4.3 3.9 4 4 4.6 1. Use…
Match the definition in column X with the correct term in column Y.
When you are testing the difference between two means, it is not important to distinguish whether the samples are independent of each other. True
A. 2.1

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