- Chapter 1:
- Chapter 1.4:
- Chapter 2:
- Chapter 2.1:
- Chapter 2.2:
- Chapter 2.3:
- Chapter 3:
- Chapter 3.1:
- Chapter 3.2:
- Chapter 3.3:
- Chapter 3.4:
- Chapter 4:
- Chapter 4.1:
- Chapter 4.2:
- Chapter 4.3:
- Chapter 4.4:
- Chapter 4.5:
- Chapter 5:
- Chapter 5.1:
- Chapter 5.2:
- Chapter 5.3:
- Chapter 5.4:
- Chapter 6:
- Chapter 6.1:
- Chapter 6.2:
- Chapter 6.3:
- Chapter 6.4:
- Chapter 7:
- Chapter 7.1:
- Chapter 7.2:
- Chapter 7.3:
- Chapter 7.4:
- Chapter 8:
Elementary Statistics: A Step By Step Approach 9th Edition - Solutions by Chapter
Full solutions for Elementary Statistics: A Step By Step Approach | 9th Edition
Elementary Statistics: A Step By Step Approach | 9th Edition - Solutions by ChapterGet Full Solutions
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).
Attribute control chart
Any control chart for a discrete random variable. See Variables control chart.
An attribute control chart that plots the total number of defects per unit in a subgroup. Similar to a defects-per-unit or U chart.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.
See Control chart.
A method to derive the probability density function of the sum of two independent random variables from an integral (or sum) of probability density (or mass) functions.
A square matrix that contains the variances and covariances among a set of random variables, say, X1 , X X 2 k , , … . The main diagonal elements of the matrix are the variances of the random variables and the off-diagonal elements are the covariances between Xi and Xj . Also called the variance-covariance matrix. When the random variables are standardized to have unit variances, the covariance matrix becomes the correlation matrix.
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
An expression sometimes used for nonlinear regression models or polynomial regression models.
Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.
Defect concentration diagram
A quality tool that graphically shows the location of defects on a part or in a process.
Distribution free method(s)
Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).
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.
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.
In statistical quality control, that portion of a number of units or the output of a process that is defective.
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .
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 .