- Chapter Chapter 1: Picturing Distributions with Graphs
- Chapter Chapter 10: Introducing Probability
- Chapter Chapter 11: Sampling Distributions
- Chapter Chapter 12: General Rules of Probability
- Chapter Chapter 13: Binomial Distributions
- Chapter Chapter 14: Confidence Intervals: The Basics
- Chapter Chapter 15: Tests of Significance: The Basics
- Chapter Chapter 16: Inference in Practice
- Chapter Chapter 17: From Exploration to Inference: Part II Review
- Chapter Chapter 18: Inference about a Population Mean
- Chapter Chapter 19: Two-Sample Problems
- Chapter Chapter 2: Describing Distributions with Numbers
- Chapter Chapter 20: Inference about a Population Proportion
- Chapter Chapter 21: Comparing Two Proportions
- Chapter Chapter 22: Inference about Variables: Part III Review
- Chapter Chapter 23: Two Categorical Variables: The Chi-Square Test
- Chapter Chapter 24: Inference for Regression
- Chapter Chapter 25: One-Way Analysis of Variance: Comparing Several Means
- Chapter Chapter 26: Nonparametric Tests
- Chapter Chapter 27: Statistical Process Control
- Chapter Chapter 28: Multiple Regression
- Chapter Chapter 3: The Normal Distributions
- Chapter Chapter 4 : Scatterplots and Correlation
- Chapter Chapter 5: Regression
- Chapter Chapter 6: Two-Way Tables
- Chapter Chapter 7: Exploring Data: Part I Review
- Chapter Chapter 8: Producing Data: Sampling
- Chapter Chapter 9: Producing Data: Experiments
The Basic Practice of Statistics 4th Edition - Solutions by Chapter
Full solutions for The Basic Practice of Statistics | 4th Edition
`-error (or `-risk)
In hypothesis testing, an error incurred by rejecting a null hypothesis when it is actually true (also called a type I error).
Adjusted R 2
A variation of the R 2 statistic that compensates for the number of parameters in a regression model. Essentially, the adjustment is a penalty for increasing the number of parameters in the model. Alias. In a fractional factorial experiment when certain factor effects cannot be estimated uniquely, they are said to be aliased.
Attribute control chart
Any control chart for a discrete random variable. See Variables control chart.
Axioms of probability
A set of rules that probabilities deined on a sample space must follow. See Probability
An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).
Binomial random variable
A discrete random variable that equals the number of successes in a ixed number of Bernoulli trials.
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.
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 mass function
The probability mass function of the conditional probability distribution of a discrete random variable.
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.
Formulas used to determine the number of elements in sample spaces and events.
In hypothesis testing, this is the portion of the sample space of a test statistic that will lead to rejection of the null hypothesis.
Cumulative normal distribution function
The cumulative distribution of the standard normal distribution, often denoted as ?( ) x and tabulated in Appendix Table II.
Deming’s 14 points.
A management philosophy promoted by W. Edwards Deming that emphasizes the importance of change and quality
The response variable in regression or a designed experiment.
A matrix that provides the tests that are to be conducted in an experiment.
A concept in parameter estimation that uses the variances of different estimators; essentially, an estimator is more eficient than another estimator if it has smaller variance. When estimators are biased, the concept requires modiication.
A property of a collection of events that indicates that their union equals the sample space.
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
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