 8.8.1.1: A sample of 31 data observations has a sample mean x =53.42 and a s...
 8.8.1.2: A random sample of 41 glass sheets is obtained and their thicknesse...
 8.8.1.3: The breaking strengths of a random sample of 20 bundles of wool ber...
 8.8.1.4: A random sample of 16 onekilogram sugar packets is obtained and th...
 8.8.1.5: A sample of 28 data observations has a sample mean x =0.0328. If an...
 8.8.1.6: The resilient moduli of 10 samples of a clay mixture are measured a...
 8.8.1.7: An experimenter feels that a population standard deviation is no la...
 8.8.1.8: An experimenter would like to construct a 99% twosided tinterval,...
 8.8.1.9: Consider the sample of 31 data observations discussed in 8.1.1. How...
 8.8.1.10: Consider the sample of 31 data observations discussed in 8.1.1. How...
 8.8.1.11: Consider the sample of 20 breaking strength measurements discussed ...
 8.8.1.12: A sample of 30 data observations has a sample mean x =14.62 and a s...
 8.8.1.13: A sample of 61 bottles of chemical solution is obtained and the sol...
 8.8.1.14: A sample of 19 data observations has a sample mean of x =11.80. If ...
 8.8.1.15: A sample of 29 measurements of radiation levels in a research labor...
 8.8.1.16: The pH levels of a random sample of 16 chemical mixtures from a pro...
 8.8.1.17: Chilled cast iron is used for mechanical components that need parti...
 8.8.1.18: Use the summary statistics that you calculated for the data sets to...
 8.8.1.19: Use the summary statistics that you calculated for the data sets to...
 8.8.1.20: Use the summary statistics that you calculated for the data sets to...
 8.8.1.21: Use the summary statistics that you calculated for the data sets to...
 8.8.1.22: Use the summary statistics that you calculated for the data sets to...
 8.8.1.23: The yields of nine batches of a chemical process were measured and ...
 8.8.1.24: Consider the data set34 45 27 33 38 41 45 29 30 39 34 40 28 33 36(a...
 8.8.1.25: A random sample of 14 chemical solutions is obtained, and their str...
 8.8.1.26: A boot manufacturer is testing the quality of leather provided by a...
 8.8.1.27: Suppose that a twosided tinterval for a population mean is obtain...
 8.8.2.1: A sample of n =18 observations has a sample mean of x =57.74 and a ...
 8.8.2.2: A sample of n =39 observations has a sample mean of x =5532 and a s...
 8.8.2.3: A sample of n =13 observations has a sample mean of x =2.879. If an...
 8.8.2.4: A sample of n =44 observations has a sample mean of x =87.90. If an...
 8.8.2.5: An experimenter is interested in the hypothesis testing problem H0 ...
 8.8.2.6: An experimenter is interested in the hypothesis testing problem H0 ...
 8.8.2.7: An experimenter is interested in the hypothesis testing problem H0 ...
 8.8.2.8: An experimenter is interested in the hypothesis testing problem H0 ...
 8.8.2.9: An experimenter is interested in the hypothesis testing problem H0 ...
 8.8.2.10: An experimenter is interested in the hypothesis testing problem H0 ...
 8.8.2.11: A machine is set to cut metal plates to a length of 44.350 mm. The ...
 8.8.2.12: A food manufacturer claims that at the time of purchase by a consum...
 8.8.2.13: A chemical plant is required to maintain ambient sulfur levels in t...
 8.8.2.14: A company advertises that its electric motors provide an efciency t...
 8.8.2.15: Recall 8.1.17 where a collection of n =10 samples of chilled cast i...
 8.8.2.16: Restaurant Service Times Consider the data set of service times giv...
 8.8.2.17: Telephone Switchboard Activity Consider the data set of calls recei...
 8.8.2.18: Paving Slab Weights Consider the data set of paving slab weights gi...
 8.8.2.19: Spray Painting Procedure Consider the data set of paint thicknesses...
 8.8.2.20: Plastic Panel Bending Capabilities Consider the data set of plastic...
 8.8.2.21: An experimenter randomly selects n =16 batteries from a production ...
 8.8.2.22: A twosided tprocedure is performed. Use Table III to put bounds o...
 8.8.2.23: A company claims that its components have an average length of 82.5...
 8.8.2.24: A random sample of 25 components is obtained, and their weights are...
 8.8.2.25: A random sample of 28 plastic items is obtained, and their breaking...
 8.8.2.26: An experimenter measures the failure times of a random sample of 25...
 8.8.2.27: An experimenter is planning an experiment to assess whether it can ...
 8.8.2.28: Use Table III to indicate whether the pvalues for the following t...
 8.8.2.29: Toxicity of Salmon Fillets An experiment is conducted to investigat...
 8.8.2.30: In testing a hypothesis, if the pvalue is less than 1%, your decis...
 8.8.2.31: If your computer reports a pvalue of 1.205, then: A. The null hypo...
 8.8.2.32: In hypothesis testing: A. The null hypothesis is given the benet of...
 8.8.2.33: For a onesample problem, suppose that n =20, x =315.9, and s =22.9...
 8.8.2.34: For a twosided ttest, which of the following tstatistics would r...
 8.8.2.35: For a onesided ttest with HA : >10, which of the following tstat...
 8.8.2.36: Mercury Levels in Coal DS 6.7.19 shows the mercury levels of coal s...
 8.8.2.37: Natural Gas Consumption DS 6.7.20 contains data on the total daily ...
 8.8.6.1: In an experiment to investigate when a radar picks up a certain kin...
 8.8.6.2: A company is planning a large telephone survey and is interested in...
 8.8.6.3: A paper company sells paper that is supposed to have a weight of 75...
 8.8.6.4: Agroupofmedicalresearchersisinvestigatinghowartery disease affects ...
 8.8.6.5: Osteoporosis Patient Heights Consider the data set of osteoporosis ...
 8.8.6.6: Bamboo Cultivation Consider the data set in DS 6.7.5 of bamboo shoo...
 8.8.6.7: The breaking strengths of a random sample of 26 molded plastic hous...
 8.8.6.8: Composites are materials that are made by embedding a ber, such as ...
 8.8.6.9: Soil Compressibility Tests Recall the data set of soil compressibil...
 8.8.6.10: Condence Interval for a Population Variance For use with 8.6.118.6....
 8.8.6.11: A sample of n =18 observations has a sample standard deviation of s...
 8.8.6.12: Consider the data set of 41 glass sheet thicknesses described in 8....
 8.8.6.13: Consider the data set of breaking strengths of wool ber bundles des...
 8.8.6.14: Consider the data set of sugar packet weights described in 8.1.4. C...
 8.8.6.15: A twosided ttest is performed. Use Table III to put bounds on the...
 8.8.6.16: An experimenter measures the compressibility of 16 samples of clay ...
 8.8.6.17: A sample of 14 bers was tested. Their strengths had a sample averag...
 8.8.6.18: Consider the data set 34 54 73 38 89 52 75 33 50 39 42 42 40 66 72 ...
 8.8.6.19: Are the following statements true or false? (a) In hypothesis testi...
 8.8.6.20: A sample of 22 wires was tested. Their resistances had a sample ave...
 8.8.6.21: An engineer selects 10 components at random and measures their stre...
 8.8.6.22: A random sample of 10 items gives x =614.5 and s =42.9. (a) Use a h...
 8.8.6.23: Twelve samples of a metal alloy are tested. The exibility measureme...
 8.8.6.24: Flowrates in Urban Sewer Systems Flow meters are installed in urban...
 8.8.6.25: Polymer Compound Densities Eight samples of a polymer compound were...
 8.8.6.26: In a sample of size 33 a sample mean of 382.97 and a sample standar...
 8.8.6.27: Show how Table III can be used to put bounds on the pvalues for th...
 8.8.6.28: You can use the data sets referred to to practice condence interval...
 8.8.6.29: You can use the data sets referred to to practice condence interval...
 8.8.6.30: You can use the data sets referred to to practice condence interval...
 8.8.6.31: You can use the data sets referred to to practice condence interval...
 8.8.6.32: You can use the data sets referred to to practice condence interval...
 8.8.6.33: You can use the data sets referred to to practice condence interval...
 8.8.6.34: You can use the data sets referred to to practice condence interval...
 8.8.6.35: You can use the data sets referred to to practice condence interval...
 8.8.6.36: When using a condence interval for a population mean, which of thes...
 8.8.6.37: If your computer reports a pvalue of 0.005, then: A. The probabili...
 8.8.6.38: When deciding what to set as the null hypothesis and what to set as...
 8.8.6.39: For a onesided ttest with HA : <3, which of the following tstati...
 8.8.6.40: Hypothesis testing enables you to assess whether things that your d...
 8.8.6.41: Consider the design of a twosample experiment to compare two medic...
 8.8.6.42: Carbon Footprints Analyze the data in DS 6.7.15, which contains est...
 8.8.6.43: Data Warehouse Design Power consumption represents a large proporti...
 8.8.6.44: Customer Churn Customer churn is a term used for the attrition of a...
 8.8.6.45: Mining Mill Operations DS 6.7.18 contains daily data for the mill o...
 8.8.6.46: If your computer reports a pvalue of 0.764, then: A. The null hypo...
 8.8.6.47: In hypothesis testing: A. Accepting the null hypothesis implies tha...
 8.8.6.48: If your computer reports a pvalue of 0.25, then: A. The probabilit...
 8.8.6.49: In hypothesis testing: A. If the objective is to see whether there ...
Solutions for Chapter 8: Inferences on a Population Mean
Full solutions for Probability and Statistics for Engineers and Scientists  4th Edition
ISBN: 9781111827045
Solutions for Chapter 8: Inferences on a Population Mean
Get Full SolutionsChapter 8: Inferences on a Population Mean includes 113 full stepbystep solutions. This textbook survival guide was created for the textbook: Probability and Statistics for Engineers and Scientists, edition: 4. Probability and Statistics for Engineers and Scientists was written by and is associated to the ISBN: 9781111827045. Since 113 problems in chapter 8: Inferences on a Population Mean have been answered, more than 11778 students have viewed full stepbystep solutions from this chapter. This expansive textbook survival guide covers the following chapters and their solutions.

All possible (subsets) regressions
A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions

Bayes’ estimator
An estimator for a parameter obtained from a Bayesian method that uses a prior distribution for the parameter along with the conditional distribution of the data given the parameter to obtain the posterior distribution of the parameter. The estimator is obtained from the posterior distribution.

Bivariate distribution
The joint probability distribution of two random variables.

C chart
An attribute control chart that plots the total number of defects per unit in a subgroup. Similar to a defectsperunit or U chart.

Chisquare test
Any test of signiicance based on the chisquare distribution. The most common chisquare tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data

Conidence interval
If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made

Control chart
A graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the incontrol value of the parameter that is being monitored and lower and upper control limits. The control limits are determined by statistical criteria and are not arbitrary, nor are they related to speciication limits. If sample points fall within the control limits, the process is said to be incontrol, or free from assignable causes. Points beyond the control limits indicate an outofcontrol process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.

Cook’s distance
In regression, Cook’s distance is a measure of the inluence of each individual observation on the estimates of the regression model parameters. It expresses the distance that the vector of model parameter estimates with the ith observation removed lies from the vector of model parameter estimates based on all observations. Large values of Cook’s distance indicate that the observation is inluential.

Crossed factors
Another name for factors that are arranged in a factorial experiment.

Cumulative sum control chart (CUSUM)
A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t

Deming’s 14 points.
A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality

Designed experiment
An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

Discrete distribution
A probability distribution for a discrete random variable

Erlang random variable
A continuous random variable that is the sum of a ixed number of independent, exponential random variables.

Factorial experiment
A type of experimental design in which every level of one factor is tested in combination with every level of another factor. In general, in a factorial experiment, all possible combinations of factor levels are tested.

Firstorder model
A model that contains only irstorder terms. For example, the irstorder response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irstorder model is also called a main effects model

Fraction defective control chart
See P chart

Frequency distribution
An arrangement of the frequencies of observations in a sample or population according to the values that the observations take on

Gaussian distribution
Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications

Harmonic mean
The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .