- 12.4. 62E: Consider the regression model fit to the nisin extraction data in E...
- 12.4.49E: Using the regression model from Exercise 12-1,(a) Find a 95% confid...
- 12.4.50E: Using the regression from Exercise 12-2,(a) Find a 95% confidence i...
- 12.4.51E: Referring to the regression model from Exercise 12-3,(a) Find a 95%...
- 12.4.52E: Use the second-order polynomial regression model from Exercise 12-4...
- 12.4.53E: Consider the regression model fit to the shear strength of soil in ...
- 12.4.54E: Consider the soil absorption data in Exercise 12-6.(a) Find 95% con...
- 12.4.55E: Consider the semiconductor data in Exercise 12-13.(a) Find 99% conf...
- 12.4.56E: Consider the electric power consumption data in Exercise 12-10.(a) ...
- 12.4.57E: Consider the bearing wear data in Exercise 12-23.(a) Find 99% confi...
- 12.4.58E: Consider the wire bond pull strength data in Exercise 12-12.(a) Fin...
- 12.4.59E: Consider the regression model fit to the X-ray inspection data in E...
- 12.4.60E: Consider the regression model fit to the arsenic data in Exercise 1...
- 12.4.61E: Consider the regression model fit to the coal and limestone mixture...
- 12.4.62E: Consider the regression model fit to the nisin extraction data in E...
- 12.4.63E: Consider the regression model fit to the gray range modulation data...
- 12.4.64E: Consider the stack loss data in Exercise 12-20.(a) Calculate 95% co...
- 12.4.65E: Consider the NFL data in Exercise 12-21.(a) Find 95% confidence int...
- 12.4.66E: Consider the heat-treating data from Exercise 12-14.(a) Find 95% co...
- 12.4.67E: Consider the gasoline mileage data in Exercise 12-11.(a) Find 99% c...
- 12.4.68E: Consider the NHL data in Exercise 12-22.(a) Find a 95% confidence i...
Solutions for Chapter 12.4: Applied Statistics and Probability for Engineers 6th Edition
Full solutions for Applied Statistics and Probability for Engineers | 6th Edition
A formula used to determine the probability of the union of two (or more) events from the probabilities of the events and their intersection(s).
In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.
All possible (subsets) regressions
A method of variable selection in regression that examines all possible subsets of the candidate regressor variables. Eficient computer algorithms have been developed for implementing all possible regressions
A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study
An effect that systematically distorts a statistical result or estimate, preventing it from representing the true quantity of interest.
A distribution with two modes
Conditional probability mass function
The probability mass function of the conditional probability distribution of a discrete random variable.
When a factorial experiment is run in blocks and the blocks are too small to contain a complete replicate of the experiment, one can run a fraction of the replicate in each block, but this results in losing information on some effects. These effects are linked with or confounded with the blocks. In general, when two factors are varied such that their individual effects cannot be determined separately, their effects are said to be confounded.
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.
Continuous random variable.
A random variable with an interval (either inite or ininite) of real numbers for its range.
See Control chart.
Formulas used to determine the number of elements in sample spaces and events.
Cumulative normal distribution function
The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.
A subset of effects in a fractional factorial design that deine the aliases in the design.
A probability distribution for a discrete random variable
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.
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.
Gamma random variable
A random variable that generalizes an Erlang random variable to noninteger values of the parameter r
Geometric random variable
A discrete random variable that is the number of Bernoulli trials until a success occurs.