- Chapter 1: Statistics: The Art and Science of Data
- Chapter 10: Inference in Practice
- Chapter 2: Describing Distributions of Data
- Chapter 3: Modeling Distributions of Data
- Chapter 4: Describing Relationships
- Chapter 5: Sampling and Surveys
- Chapter 6: Designing Experiments
- Chapter 7: Probability: What Are the Chances?
- Chapter 8: Probability Models
- Chapter 9: ntroduction to Inference
Statistics Through Applications 2nd Edition - Solutions by Chapter
Full solutions for Statistics Through Applications | 2nd Edition
The joint probability distribution of two random variables.
Bivariate normal distribution
The joint distribution of two normal random variables
In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.
When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable
A horizontal line on a control chart at the value that estimates the mean of the statistic plotted on the chart. See Control chart.
The portion of the variability in a set of observations that is due to only random forces and which cannot be traced to speciic sources, such as operators, materials, or equipment. Also called a common cause.
Any test of signiicance based on the chi-square distribution. The most common chi-square tests are (1) testing hypotheses about the variance or standard deviation of a normal distribution and (2) testing goodness of it of a theoretical distribution to sample data
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.
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.
Conditional probability density function
The probability density function of the conditional probability distribution of a continuous random variable.
If it is possible to write a probability statement of the form PL U ( ) ? ? ? ? = ?1 where L and U are functions of only the sample data and ? is a parameter, then the interval between L and U is called a conidence interval (or a 100 1( )% ? ? conidence interval). The interpretation is that a statement that the parameter ? lies in this interval will be true 100 1( )% ? ? of the times that such a statement is made
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.
A measure of association between two random variables obtained as the expected value of the product of the two random variables around their means; that is, Cov(X Y, ) [( )( )] =? ? E X Y ? ? X Y .
A subset of effects in a fractional factorial design that deine the aliases in the design.
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.
Erlang random variable
A continuous random variable that is the sum of a ixed number of independent, exponential random variables.
Finite population correction factor
A term in the formula for the variance of a hypergeometric random variable.
A function used in the probability density function of a gamma random variable that can be considered to extend factorials
Gamma random variable
A random variable that generalizes an Erlang random variable to noninteger values of the parameter r