×
Join StudySoup

×

Solutions for Chapter 2: The Practice of Statistics 4th Edition

Full solutions for The Practice of Statistics | 4th Edition

ISBN: 9781429245593

Solutions for Chapter 2

Solutions for Chapter 2
4 5 0 260 Reviews
27
2
ISBN: 9781429245593

The Practice of Statistics was written by and is associated to the ISBN: 9781429245593. This textbook survival guide was created for the textbook: The Practice of Statistics, edition: 4. This expansive textbook survival guide covers the following chapters and their solutions. Since 11 problems in chapter 2 have been answered, more than 33746 students have viewed full step-by-step solutions from this chapter. Chapter 2 includes 11 full step-by-step solutions.

Key Statistics Terms and definitions covered in this textbook
• 2 k factorial experiment.

A full factorial experiment with k factors and all factors tested at only two levels (settings) each.

• Alternative hypothesis

In statistical hypothesis testing, this is a hypothesis other than the one that is being tested. The alternative hypothesis contains feasible conditions, whereas the null hypothesis speciies conditions that are under test

• Average run length, or ARL

The average number of samples taken in a process monitoring or inspection scheme until the scheme signals that the process is operating at a level different from the level in which it began.

• Categorical data

Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.

• Comparative experiment

An experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The data from the experiment are used to evaluate the treatments.

• Conditional variance.

The variance of the conditional probability distribution of a random variable.

• Confounding

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.

• Conidence coeficient

The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.

• Conidence level

Another term for the conidence coeficient.

• Continuity correction.

A correction factor used to improve the approximation to binomial probabilities from a normal distribution.

• Counting techniques

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.

• Decision interval

A parameter in a tabular CUSUM algorithm that is determined from a trade-off between false alarms and the detection of assignable causes.

• Degrees of freedom.

The number of independent comparisons that can be made among the elements of a sample. The term is analogous to the number of degrees of freedom for an object in a dynamic system, which is the number of independent coordinates required to determine the motion of the object.

• Deining relation

A subset of effects in a fractional factorial design that deine the aliases in the design.

• Designed experiment

An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment

• Discrete uniform random variable

A discrete random variable with a inite range and constant probability mass function.

• Estimate (or point estimate)

The numerical value of a point estimator.

• Finite population correction factor

A term in the formula for the variance of a hypergeometric random variable.

• Forward selection

A method of variable selection in regression, where variables are inserted one at a time into the model until no other variables that contribute signiicantly to the model can be found.