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# Solutions for Chapter 6.2: THE BINOMIAL PROBABILITY DISTRIBUTION

## Full solutions for Statistics: Informed Decisions Using Data | 4th Edition

ISBN: 9780321757272

Solutions for Chapter 6.2: THE BINOMIAL PROBABILITY DISTRIBUTION

Solutions for Chapter 6.2
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##### ISBN: 9780321757272

Chapter 6.2: THE BINOMIAL PROBABILITY DISTRIBUTION includes 122 full step-by-step solutions. Statistics: Informed Decisions Using Data was written by and is associated to the ISBN: 9780321757272. This expansive textbook survival guide covers the following chapters and their solutions. Since 122 problems in chapter 6.2: THE BINOMIAL PROBABILITY DISTRIBUTION have been answered, more than 153402 students have viewed full step-by-step solutions from this chapter. This textbook survival guide was created for the textbook: Statistics: Informed Decisions Using Data , edition: 4.

Key Statistics Terms and definitions covered in this textbook
• Alias

In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.

• Analysis of variance (ANOVA)

A method of decomposing the total variability in a set of observations, as measured by the sum of the squares of these observations from their average, into component sums of squares that are associated with speciic deined sources of variation

• 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.

• Chi-square (or chi-squared) random variable

A continuous random variable that results from the sum of squares of independent standard normal random variables. It is a special case of a gamma random variable.

• 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.

• Components of variance

The individual components of the total variance that are attributable to speciic sources. This usually refers to the individual variance components arising from a random or mixed model analysis of variance.

• Conditional mean

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

• Contrast

A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.

• Convolution

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.

• Correction factor

A term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ? . The correction factor can also be written as nx 2 .

• 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.

• Curvilinear regression

An expression sometimes used for nonlinear regression models or polynomial regression models.

• 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.

• Discrete random variable

A random variable with a inite (or countably ininite) range.

• Error of estimation

The difference between an estimated value and the true value.

• Exhaustive

A property of a collection of events that indicates that their union equals the sample space.

• Finite population correction factor

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

• First-order model

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

• Fixed factor (or fixed effect).

In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.

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