- 5.3.8E: assume all variables are binomial. (Note: If values are not found i...
- 5.3.9E: assume all variables are binomial. (Note: If values are not found i...
- 5.3.10E: assume all variables are binomial. (Note: If values are not found i...
- 5.3.11E: assume all variables are binomial. (Note: If values are not found i...
- 5.3.13E: assume all variables are binomial. (Note: If values are not found i...
- 5.3.14E: assume all variables are binomial. (Note: If values are not found i...
- 5.3.15E: assume all variables are binomial. (Note: If values are not found i...
- 5.3.16E: assume all variables are binomial. (Note: If values are not found i...
- 5.3.19E: Social Security Recipients A study found that 1 % of Social Securit...
- 5.3.20E: Tossing Coins Find the mean, variance, and standard deviation for t...
- 5.3.22E: Federal Government Employee E-mail Use It has been reported that 83...
- 5.3.23E: Watching Fireworks A survey found that 21 % of Americans watch fire...
- 5.3.24E: Alternate Sources of Fuel Eighty-five percent of Americans favor sp...
- 5.3.25E: Survey on Bathing Pets A survey found that 25% of pet owners had th...
- 5.3.26E: Survey on Answering Machine Ownership In a survey, 63% of Americans...
- 5.3.27E: Poverty and the Federal Government One out of every three Americans...
- 5.3.28E: Internet Purchases Thirty-two percent of adult Internet users have ...
- 5.3.31E: Survey of High School Seniors Of graduating high school seniors, 14...
- 5.3.32E: Is this a binomial distribution? Explain.X0123P(X)0.0640.2880.4320.216
- 5.3.33EC: Children in a Family The graph shown here represents the probabilit...
- 5.3.34EC: Construct a binomial distribution graph for the number of defective...
Solutions for Chapter 5.3: Elementary Statistics: A Step By Step Approach 9th Edition
Full solutions for Elementary Statistics: A Step By Step Approach | 9th Edition
2 k factorial experiment.
A full factorial experiment with k factors and all factors tested at only two levels (settings) each.
a-error (or a-risk)
In hypothesis testing, an error incurred by failing to reject a null hypothesis when it is actually false (also called a type II error).
See Arithmetic mean.
Bivariate normal distribution
The joint distribution of two normal random variables
Box plot (or box and whisker plot)
A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).
When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable
A chart used to organize the various potential causes of a problem. Also called a ishbone diagram.
Central limit theorem
The simplest form of the central limit theorem states that the sum of n independently distributed random variables will tend to be normally distributed as n becomes large. It is a necessary and suficient condition that none of the variances of the individual random variables are large in comparison to their sum. There are more general forms of the central theorem that allow ininite variances and correlated random variables, and there is a multivariate version of the theorem.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.
Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.
Conditional probability distribution
The distribution of a random variable given that the random experiment produces an outcome in an event. The given event might specify values for one or more other random variables
Conditional probability mass function
The probability mass function of the conditional probability distribution of a discrete random variable.
In the most general usage, a measure of the interdependence among data. The concept may include more than two variables. The term is most commonly used in a narrow sense to express the relationship between quantitative variables or ranks.
Defect concentration diagram
A quality tool that graphically shows the location of defects on a part or in a process.
Another name for a probability density function
Discrete random variable
A random variable with a inite (or countably ininite) range.
Another name for a cumulative distribution function.
The variance of an error term or component in a model.
In statistical quality control, that portion of a number of units or the output of a process that is defective.
Fractional factorial experiment
A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.
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