- Chapter 1: Probability
- Chapter 10: Summarizing Data
- Chapter 11: Comparing Two Samples
- Chapter 12: The Analysis of Variance
- Chapter 13: The Analysis of Categorical Data
- Chapter 14: Linear Least Squares
- Chapter 2: Random Variables
- Chapter 3: Joint Distributions
- Chapter 4: Expected Values
- Chapter 5: Limit Theorems
- Chapter 6: Distributions Derived from the Normal Distribution
- Chapter 7: Survey Sampling
- Chapter 8: Estimation of Parameters and Fitting of Probability Distributions
- Chapter 9: Testing Hypotheses and Assessing Goodness of Fit
Mathematical Statistics and Data Analysis 3rd Edition - Solutions by Chapter
Full solutions for Mathematical Statistics and Data Analysis | 3rd Edition
a-error (or a-risk)
In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).
In hypothesis testing, a region in the sample space of the test statistic such that if the test statistic falls within it, the null hypothesis cannot be rejected. This terminology is used because rejection of H0 is always a strong conclusion and acceptance of H0 is generally a weak conclusion
In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.
See Arithmetic mean.
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.
A chart used to organize the various potential causes of a problem. Also called a ishbone diagram.
A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.
The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.
Components of variance
The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.
Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.
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
Another name for factors that are arranged in a factorial experiment.
Cumulative distribution function
For a random variable X, the function of X deined as PX x ( ) ? that is used to specify the probability distribution.
Defects-per-unit control chart
See U chart
A concept in parameter estimation that uses the variances of different estimators; essentially, an estimator is more eficient than another estimator if it has smaller variance. When estimators are biased, the concept requires modiication.
Error of estimation
The difference between an estimated value and the true value.
Fraction defective control chart
See P chart
Fractional factorial experiment
A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.
Gamma random variable
A random variable that generalizes an Erlang random variable to noninteger values of the parameter r
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