Overview

After careful study of this chapter, you should be able to do the following:1.Explain the general concepts of estimating the parameters of a population or a probability distribution2.Explain the important role of the normal distribution as a sampling distribution and the central limit theorem3.Explain important properties of point estimators, including bias, variances, and mean square error4.Construct point estimators using the method of moments and the method of maximum likelihood.5.Compute and explain the precision with which a parameter is estimated6.Construct a point estimator using the Bayesian approach8- After careful study of this chapter, you should be able to do the following:1.Construct confidence intervals on the mean of a normal distribution, using normal distribution or t distribution method2.Construct confidence intervals on the variance and standard deviation of normal distribution3.Construct confidence intervals on a population proportion4.Use a general method for constructing an approximate confidence interval on a parameter5.Construct a prediction interval for a future observation6.Construct a tolerance interval for a normal population7.Explain the three types of interval estimates: confidence intervals, prediction intervals, and tolerance intervals9- After careful study of this chapter, you should be able to do the following:1.Structure engineering decision-making problems as hypothesis tests2.Test hypotheses on the mean of a normal distribution using a Z-test or a t-test3.Test hypotheses on the variance or standard deviation of a normal distribution4.Test hypotheses on a population proportion5.Use the P-value approach for making decisions in hypothesis tests6.Compute power & Type II error probability and make sample size selection decisions for tests on means, variances and proportions7.Explain & use the relationship between confidence intervals & hypothesis tests8.Use the chi-square goodness-of-fit test to check distributional assumptions9.Apply contingency table tests10.Apply nonparametric tests11.Use equivalence testing12.Combine P-values10- After careful study of this chapter, you should be able to do the following:1.Structure comparative experiments involving two samples as hypothesis tests2.Test hypotheses and construct confidence intervals on the difference in means of two normal distributions3.Test hypotheses and construct confidence intervals on the ratio of the variances or standard deviations of two normal distributions4.Test hypotheses and construct confidence intervals on the difference in two population proportions5.Use the P-value approach for making decisions in hypothesis tests6.Compute power, Type II error probability, and make sample size decisions for two-sample tests on means, variances & proportions7.Explain and use the relationship between confidence intervals and hypothesis tests11- After careful study of this chapter, you should be able to do the following:1.Use simple linear regression for building empirical models to engineering and scientific data2.Understand how the method of least squares is used to estimate the parameters in a linear regression model3.Analyze residuals to determine if the regression model is an adequate fit to the data or to see if any underlying assumptions are violated4.Test the statistical hypotheses and construct confidence intervals on the regression model parameters5.Use the regression model to make a prediction of a future observation and construct an appropriate prediction interval on the future observation6.Apply the correlation model7.Use simple transformations to achieve a linear regression model

Skills

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