- 12.4.1: Graph the line corresponding to the equationy 2x 1 by graphing the ...
- 12.4.2: Graph the line corresponding to the equationy 2x 1 by graphing the ...
- 12.4.3: Give the equation and graph for a line withy-intercept equal to 3 a...
- 12.4.4: Give the equation and graph for a line withy-intercept equal to 3 a...
- 12.4.5: What is the difference between deterministicand probabilistic mathe...
- 12.4.6: You are given five points with these coordinates:x 2 10 1 2y 1 13 5...
- 12.4.7: Six points have these coordinates:x 123456y 5.6 4.6 4.5 3.7 3.2 2.7...
- 12.4.8: Six points have these coordinates:x 1 2 3 456y 9.7 6.5 6.4 4.1 2.1 ...
- 12.4.9: Professor Asimov Professor Isaac Asimovwas one of the most prolific...
- 12.4.10: A Chemical Experiment Using achemical procedure called differential...
- 12.4.11: Sleep Deprivation A study wasconducted to determine the effects of ...
- 12.4.12: Sleep Deprivation II Refer to the data givenin the sleep deprivatio...
- 12.4.13: Achievement Tests The AcademicPerformance Index (API) is a measure ...
- 12.4.14: How Long Is It? How good are you atestimating? To test a subjects a...
- 12.4.15: Test Interviews Of two personnelevaluation techniques available, th...
- 12.4.16: 6 Test Interviews, continued Refer to Exercise12.15. Construct the ...
- 12.4.17: Armspan and Height Leonardoda Vinci (14521519) drew a sketch of a m...
- 12.4.18: Strawberries The following data wereobtained in an experiment relat...
Solutions for Chapter 12.4: An Analysis of Variance for Linear Regression
Full solutions for Introduction to Probability and Statistics 1 | 14th Edition
`-error (or `-risk)
In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I 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
Additivity property of x 2
If two independent random variables X1 and X2 are distributed as chi-square with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chi-square random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chi-square random variables.
Axioms of probability
A set of rules that probabilities deined on a sample space must follow. See Probability
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.
Central composite design (CCD)
A second-order response surface design in k variables consisting of a two-level factorial, 2k axial runs, and one or more center points. The two-level factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a second-order model.
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.
The portion of the variability in a set of observations that is due to only random forces and which cannot be traced to speciic sources, such as operators, materials, or equipment. Also called a common cause.
The variance of the conditional probability distribution of a random variable.
See Control chart.
A subset of effects in a fractional factorial design that deine the aliases in the design.
A probability distribution for a discrete random variable
Discrete uniform random variable
A discrete random variable with a inite range and constant probability mass function.
The amount of variability exhibited by data
Erlang random variable
A continuous random variable that is the sum of a ixed number of independent, exponential random variables.
The variance of an error term or component in a model.
A series of tests in which changes are made to the system under study
An arrangement of the frequencies of observations in a sample or population according to the values that the observations take on
A function that is used to determine properties of the probability distribution of a random variable. See Moment-generating function