Ball bearing manufacturing is a highly precise business in which minimal part variability is critical. Large variances in the size of the ball bearings cause bearing failure and rapid wear out. Production standards call for a maximum variance of .0001 inches2. Gerry Liddy has gathered a sample of 15 bearings that shows a sample standard deviation of .014 inches. a. Use a = .10 to determine whether the sample indicates that the maximum acceptable variance is being exceeded. b. Compute the 90% confidence interval estimate of the variance of the ball bearings in the population.

Ball bearing manufacturing is a highly precise business in which minimal part variability is critical. Large variances in the size of the ball bearings cause bearing failure and rapid wear out. Production standards call for a maximum variance of .0001 inches2. Gerry Liddy has gathered a sample of 15 bearings that shows a sample standard deviation of .014 inches. a. Use a = .10 to determine whether the sample indicates that the maximum acceptable variance is being exceeded. b. Compute the 90% confidence interval estimate of the variance of the ball bearings in the population.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter10: Statistics

Section10.5: Comparing Sets Of Data

Problem 14PPS

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Ball bearing manufacturing is a highly precise business in which minimal part variability is critical. Large variances in the size of the ball bearings cause bearing failure and rapid wear out. Production standards call for a maximum variance of .0001 inches2. Gerry Liddy has gathered a sample of 15 bearings that shows a sample standard deviation of .014 inches.

a. Use a = .10 to determine whether the sample indicates that the maximum acceptable variance is being exceeded.

b. Compute the 90% confidence interval estimate of the variance of the ball bearings in the population.

Definition Definition Method in statistics by which an observation’s uncertainty can be quantified. The main use of interval estimating is for describing a range that is made by transforming a point estimate by determining the range of values, or interval within which the population parameter is likely to fall. This range helps in measuring its precision.

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