- 7.2.1E: What are the properties of the t distribution?
- 7.2.2E: What is meant by degrees of freedom?
- 7.2.3E: (ans) Find the values for each.a. t?/2 and n = 18 for the 99% confi...
- 7.2.4E: When should the t distribution be used to find a confidence interva...
- 7.2.6E: Assume that all variable are approximately normally distributed.Dig...
- 7.2.7E: Assume that all variable are approximately normally distributed.Wom...
- 7.2.8E: Assume that all variable are approximately normally distributed.Sta...
- 7.2.10E: Assume that all variable are approximately normally distributed.Dan...
- 7.2.11E: Assume that all variable are approximately normally distributed.Dis...
- 7.2.13E: Assume that all variable are approximately normally distributed.Stu...
- 7.2.15E: Assume that all variable are approximately normally distributed.Chi...
- 7.2.16E: Assume that all variable are approximately normally distributed.Hos...
- 7.2.17E: Assume that all variable are approximately normally distributed.Cos...
- 7.2.21EC: A one-sided confidence interval can be found for a mean by using or...
Solutions for Chapter 7.2: Elementary Statistics: A Step By Step Approach 9th Edition
Full solutions for Elementary Statistics: A Step By Step Approach | 9th Edition
Asymptotic relative eficiency (ARE)
Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.
See Arithmetic mean.
A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.
Completely randomized design (or experiment)
A type of experimental design in which the treatments or design factors are assigned to the experimental units in a random manner. In designed experiments, a completely randomized design results from running all of the treatment combinations in random order.
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.
Another term for the conidence coeficient.
See Control chart.
Formulas used to determine the number of elements in sample spaces and events.
The value of a statistic corresponding to a stated signiicance level as determined from the sampling distribution. For example, if PZ z PZ ( )( .) . ? =? = 0 025 . 1 96 0 025, then z0 025 . = 1 9. 6 is the critical value of z at the 0.025 level of signiicance. Crossed factors. Another name for factors that are arranged in a factorial experiment.
An expression sometimes used for nonlinear regression models or polynomial regression models.
A model to relate a response to one or more regressors or factors that is developed from data obtained from the system.
Error mean square
The error sum of squares divided by its number of degrees of freedom.
Error sum of squares
In analysis of variance, this is the portion of total variability that is due to the random component in the data. It is usually based on replication of observations at certain treatment combinations in the experiment. It is sometimes called the residual sum of squares, although this is really a better term to use only when the sum of squares is based on the remnants of a model-itting process and not on replication.
Estimate (or point estimate)
The numerical value of a point estimator.
Estimator (or point estimator)
A procedure for producing an estimate of a parameter of interest. An estimator is usually a function of only sample data values, and when these data values are available, it results in an estimate of the parameter of interest.
A subset of a sample space.
A model that contains only irstorder terms. For example, the irst-order response surface model in two variables is y xx = + ?? ? ? 0 11 2 2 + + . A irst-order model is also called a main effects model
Fraction defective control chart
See P chart
Goodness of fit
In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.
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 .