- 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
In hypothesis testing, a region in the sample space of the test statistic such that if the test statistic falls within it, the null hypothesis cannot be rejected. This terminology is used because rejection of H0 is always a strong conclusion and acceptance of H0 is generally a weak conclusion
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
A study in which a sample from a population is used to make inference to a future population. Stability needs to be assumed. See Enumerative study
A distribution with two modes
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.
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
The probability 1?a associated with a conidence interval expressing the probability that the stated interval will contain the true parameter value.
Another term for the conidence coeficient.
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 dimensionless measure of the linear association between two variables, usually lying in the interval from ?1 to +1, with zero indicating the absence of correlation (but not necessarily the independence of the two variables).
W. Edwards Deming (1900–1993) was a leader in the use of statistical quality control.
The response variable in regression or a designed experiment.
Another name for a cumulative distribution function.
Extra sum of squares method
A method used in regression analysis to conduct a hypothesis test for the additional contribution of one or more variables to a model.
Finite population correction factor
A term in the formula for the variance of a hypergeometric random variable.
A function that is used to determine properties of the probability distribution of a random variable. See Moment-generating function
Goodness of fit
In general, the agreement of a set of observed values and a set of theoretical values that depend on some hypothesis. The term is often used in itting a theoretical distribution to a set of observations.
In multiple regression, the matrix H XXX X = ( ) ? ? -1 . This a projection matrix that maps the vector of observed response values into a vector of itted values by yˆ = = X X X X y Hy ( ) ? ? ?1 .