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week11.pptx4-5 Inference on the Mean of a Population,
Variance Unknown
4-5.1 Hypothesis Testing on the Mean
4-5 Inference on the Mean of a Population,
Variance Unknown
4-5.1 Hypothesis Testing on the Mean
4-5 Inference on the Mean of a Population,
Variance Unknown
4-5.1 Hypothesis Testing on the Mean
4-5 Inference on the Mean of a Population,
Variance Unknown
4-5.1 Hypothesis Testing on the Mean
Calculating the P-value
4-5 Inference on the Mean of a Population,
Variance Unknown
4-5.1 Hypothesis Testing on the Mean
4-5 Inference on the Mean of a Population,
Variance Unknown
4-5.1 Hypothesis Testing on the Mean
4-5 Inference on the Mean of a Population,
Variance Unknown
4-5.1 Hypothesis Testing on the Mean
4-5 Inference on the Mean of a Population,
Variance Unknown
4-5.1 Hypothesis Testing on the Mean
4-5 Inference on the Mean of a Population,
Variance Unknown
4-5.2 Type II Error and Choice of Sample Size
Fortunately, this unpleasant task has already been done, and the results are summarized in a series of graphs in Appendix A Charts Va, Vb, Vc, and Vd that plot for the t-test against a parameter d for various sample sizes n.
4-5 Inference on the Mean of a Population,
Variance Unknown
4-5.2 Type II Error and Choice of Sample Size
These graphics are called operating characteristic (or OC) curves. Curves are provided for two-sided alternatives on Charts Va and Vb. The abscissa scale factor d on these charts is defined as
4-5 Inference on the Mean of a Population,
Variance Unknown
4-5.3 Confidence Interval on the Mean
4-5 Inference on the Mean of a Population,
Variance Unknown
4-5.3 Confidence Interval on the Mean
4-5 Inference on the Mean of a Population,
Variance Unknown
4-5.4 Confidence Interval on the Mean
4-6 Inference on the Variance of a
Normal Population
4-6.1 Hypothesis Testing on the Variance of a
Normal Population
4-6 Inference on the Variance of a
Normal Population
4-6.1 Hypothesis Testing on the Variance of a
Normal Population
4-6 Inference on the Variance of a
Normal Population
4-6.1 Hypothesis Testing on the Variance of a
Normal Population
4-6 Inference on the Variance of a
Normal Population
4-6.1 Hypothesis Testing on the Variance of a
Normal Population
4-6 Inference on the Variance of a
Normal Population
4-6.1 Hypothesis Testing on the Variance of a
Normal Population
4-6 Inference on the Variance of a
Normal Population
4-6.1 Hypothesis Testing on the Variance of a
Normal Population
4-6 Inference on the Variance of a
Normal Population
4-6.2 Confidence Interval on the Variance of a
Normal Population
4-7 Inference on Population Proportion
4-7.1 Hypothesis Testing on a Binomial Proportion
We will consider testing:
4-7 Inference on Population Proportion
4-7.1 Hypothesis Testing on a Binomial Proportion
4-7 Inference on Population Proportion
4-7.1 Hypothesis Testing on a Binomial Proportion
4-7 Inference on Population Proportion
4-7.1 Hypothesis Testing on a Binomial Proportion
4-7 Inference on Population Proportion
4-7.2 Type II Error and Choice of Sample Size
4-7 Inference on Population Proportion
4-7.2 Type II Error and Choice of Sample Size
4-7 Inference on Population Proportion
4-7.3 Confidence Interval on a Binomial Proportion
4-7 Inference on Population Proportion
4-7.3 Confidence Interval on a Binomial Proportion
4-7 Inference on Population Proportion
4-7.3 Confidence Interval on a Binomial Proportion
Choice of Sample Size
4-8 Other Interval Estimates for a
Single Sample
4-8.1 Prediction Interval
4-8 Other Interval Estimates for a
Single Sample
4-8.2 Tolerance Intervals for a Normal Distribution
4-10 Testing for Goodness of Fit
So far, we have assumed the population or probability distribution for a particular problem is known.
There are many instances where the underlying distribution is not known, and we wish to test a particular distribution.
Use a goodness-of-fit test procedure based on the chi-square distribution.
