Introduction To The Practice Of Statistics 10th Edition David S. Moore, George P. McCabe, Bruce A. Craig - Solutions

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Compute the center line and control limits for a p chart and utilize the chart for process monitoring.
Distinguish when to use a p chart rather than an x¯ chart.
Explain why the percent of product meeting specifications is a poor measure of capability.
Estimate the percent of product that meets specifications using the Normal distribution.
Contrast the x¯ and s charts in terms of what they monitor and which should be interpreted first.
Compute the center line and control limits for an s chart.
Compute the center line and control limits for an x¯ chart.
Explain what is meant by a process being in control by distinguishing common cause variation and special cause variation.
Describe a process using a flowchart and a cause-and-effect diagram.
Describe the effect of the number of resamples on the variation in bootstrap distributions.
Describe the effect of the size of the original sample on the variation in bootstrap distributions.
Find and interpret an odds ratio.
Describe the statistical model for logistic regression.
Find the odds of an outcome from its probability.
Use software to generate diagnostic plots and numerical summaries to check the ANOVA conditions for valid inference.
Interpret software output for a two-way ANOVA.
Summarize how to use the two-way ANOVA table F tests to assess the statistical significance of main effects and interaction.
Construct the two-way ANOVA table in terms of sources and degrees of freedom.
Construct an interaction plot and determine whether it suggests that there is interaction between the factors.
Interpret the relationship between two factors in terms of main effects and interaction.
Describe the two-way ANOVA model and how it is used for inference.
Discuss the advantages of a two-way ANOVA.
Use software to determine the power of the ANOVA F test for a given set of group means and sample size n.
Interpret software output to draw conclusions regarding differences in means.
Describe the use of a multiple-comparisons method in terms of controlling false rejections.
Construct a confidence interval or perform a t significance test for a contrast and summarize the results.
Distinguish between the use of contrasts to examine particular versions of the alternative hypothesis and the use of a multiple-comparisons method to compare pairs of means.
Use software to check the one-way ANOVA conditions for valid inference using plots and numerical summaries.
Summarize what the ANOVA F test can tell you about the group means.
Use ANOVA output to derive the F test result and to calculate the coefficient of determination.
Identify the ANOVA table sources of variation and degrees of freedom from the description of a study.
Describe the one-way ANOVA F test in terms of the variation between group means and the variation within groups.
Interpret the ANOVA F test and the t tests for individual coefficients.
Use an ANOVA table to find the F statistic, regression standard error, and squared multiple correlation.
Outline an ANOVA table for a multiple regression involving p predictors in terms of degrees of freedom.
Compute the predicted response from an estimated population regression equation.
Describe a multiple linear regression model in terms of a response variable, the explanatory variables, and the number of cases.
Find the sample size needed for a significance test comparing two proportions.
Find the sample size needed for a desired margin of error for the difference in proportions.
Use the large-sample method to perform a significance test for comparing two proportions and interpret the results.
Use the large-sample method to find the confidence interval for a difference between two proportions and interpret the confidence interval.
Identify the counts and sample sizes for a comparison between two proportions, compute the sample proportions, and find their difference.
Find the sample size needed for a significance test of a single proportion.
Find the sample size needed for a desired margin of error.
Use the large-sample significance test to test a null hypothesis about a population proportion.
Construct the large-sample confidence interval for a single proportion.
Calculate the standard error of a sample proportion and the margin of error.
Identify the sample size, the count, and the sample proportion for a single sample.
Relate the two errors to the significance level and power of the test.
Define what is meant by the power of a test.
Describe the two types of possible errors when performing a test of significance.
Describe the two types of possible errors when performing a test that focuses on deciding between two hypotheses.
identifying and testing using the same data set.
Describe the consequences of searching solely for statistical significance, whether through the investigation of multiple tests or by
Identify poorly designed studies where formal statistical inference is suspect.
Discriminate between practical (or scientific) significance and statistical significance.
Explain why it is important to report the P-value and include a confidence interval rather than just reporting whether a test result is statistically significant.
Calculate the mean and standard deviation of X and perform probability calculations when X has the Poisson distribution with meanμ
Describe the sampling distribution of the sample proportion p^=X/n when X has the B(n, p)distribution.
Calculate the mean and standard deviation of X and perform probability calculations when X has the B(n, p) distribution.
Determine when a count X can be modeled using the binomial or Poisson distributions.
Identify ways to reduce bias and variability of a statistic.
Interpret and use a sampling distribution to describe the bias or variability of a statistic.
Describe how simulation can be used to study a sampling distribution.
Identify parameters, statistics, samples, and populations, as well as the relationships among them.
variables and for uncorrelated and for correlated random variables.
Find variances and standard deviations using the rules for variances and standard deviations for sums of and differences between two random
Find variances and standard deviations using the rules for variances and standard deviations for linear transformations.
Use a probability distribution to find the variance and the standard deviation of a discrete random variable.
Find means using the rules for means of linear transformations, sums, and differences.
Apply the law of large numbers to describe the behavior of the sample mean as the sample size increases.
Use a probability distribution to find the mean of a discrete random variable.
Find probabilities of events for the uniform and normal distributions.
Describe the probability distribution of a continuous random variable.
Use the distribution of a discrete random variable to calculate probabilities of events.
Use a probability histogram to provide a graphical description of the probability distribution of a discrete random variable.
Describe the probability distribution of a discrete random variable.
Identify random phenomena that have equally likely outcomes and distinguish them from those that do not.
Apply the four probability rules.
Describe a sample space from a description of a random phenomenon.
Evaluate a clinical trial from an ethics standpoint.
Determine when data have been kept confidential in a study.
Describe informed consent and evaluate whether or not it has been given in specific examples.
Describe the purpose of an institutional review board and describe what kinds of expertise its members require.
Identify voluntary response samples, simple random samples, stratified random samples, and multistage random samples.
Construct a stratified random sample using software or Table B to select the samples from the strata.
Use software or Table B to generate a simple random sample (SRS).
Report the response rate as an outcome of a survey.
Distinguish between a population and a sample.
Use software or a table of random digits to randomly assign experimental units to treatments in an experiment.
When carrying out an experiment, apply the basic principles of experimental design: compare, randomize, and repeat.
Explain the need for randomization in an experiment.
Explain the need for a control group in an experiment.
Identify bias in an experiment.
Describe a placebo effect in an experiment.
Recognize anecdotal data and use specific examples to explain why they have limited value.
Determine whether data are from observational studies that are sample surveys, from observational studies that are not sample surveys, or from experiments.Identify the treatment in an experiment.
Explain how data collected from experiments can be used in specific examples.
Explain how data collected from sample surveys can be used in specific examples.
Identify available data and explain how they can be used in specific examples.

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Introduction To The Practice Of Statistics 10th Edition David S. Moore, George P. McCabe, Bruce A. Craig - Solutions

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