- Chapter Part I: Exploring and Understanding Data
- Chapter 1: Stats Starts Here
- Chapter 10: Understanding Randomness
- Chapter 11: Sample Surveys
- Chapter 12: Experiments and Observational Studies
- Chapter 13: From Randomness to Probability
- Chapter 14: Probability Rules!
- Chapter 15: Random Variables
- Chapter 16: Probability Models
- Chapter 17: Sampling Distribution Models
- Chapter 18: Confidence Intervals for Proportions
- Chapter 19: Testing Hypotheses About Proportions
- Chapter 2: Displaying and Describing Categorical Data
- Chapter 20: More About Tests and Intervals
- Chapter 21: Comparing Two Proportions
- Chapter 22: Inferences About Means
- Chapter 23: Comparing Means
- Chapter 24: Paired Samples and Blocks
- Chapter 25: Comparing Counts
- Chapter 26: Inferences for Regression
- Chapter 27: Analysis of Variance
- Chapter 28: Multiple Regression
- Chapter 3: Displaying and Summarizing Quantitative Data
- Chapter 4: Understanding and Comparing Distributions
- Chapter 5: The Standard Deviation as a Ruler and the Normal Model
- Chapter 6: Scatterplots, Association, and Correlation
- Chapter 7: Linear Regression
- Chapter 8: Regression Wisdom
- Chapter 9: Re-expressing Data: Get It Straight!
- Chapter Part II: Exploring Relationships Between Variables
- Chapter Part III: Gathering Data
Stats Modeling the World 4th Edition - Solutions by Chapter
Full solutions for Stats Modeling the World | 4th Edition
2 k factorial experiment.
A full factorial experiment with k factors and all factors tested at only two levels (settings) each.
Additivity property of x 2
If two independent random variables X1 and X2 are distributed as chi-square with v1 and v2 degrees of freedom, respectively, Y = + X X 1 2 is a chi-square random variable with u = + v v 1 2 degrees of freedom. This generalizes to any number of independent chi-square random variables.
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
Asymptotic relative eficiency (ARE)
Used to compare hypothesis tests. The ARE of one test relative to another is the limiting ratio of the sample sizes necessary to obtain identical error probabilities for the two procedures.
An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.
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.
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.
The mean of the conditional probability distribution of a 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 graphical display used to monitor a process. It usually consists of a horizontal center line corresponding to the in-control value of the parameter that is being monitored and lower and upper control limits. The control limits are determined by statistical criteria and are not arbitrary, nor are they related to speciication limits. If sample points fall within the control limits, the process is said to be in-control, or free from assignable causes. Points beyond the control limits indicate an out-of-control process; that is, assignable causes are likely present. This signals the need to ind and remove the assignable causes.
A matrix that provides the tests that are to be conducted in an experiment.
An experiment in which the tests are planned in advance and the plans usually incorporate statistical models. See Experiment
A probability distribution for a discrete random variable
Error mean square
The error sum of squares divided by its number of degrees of freedom.
A series of tests in which changes are made to the system under study
Fraction defective control chart
See P chart
Gamma random variable
A random variable that generalizes an Erlang random variable to noninteger values of the parameter r
The harmonic mean of a set of data values is the reciprocal of the arithmetic mean of the reciprocals of the data values; that is, h n x i n i = ? ? ? ? ? = ? ? 1 1 1 1 g .
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