- Chapter Chapter 1: The Role of Statistics and the Data Analysis Process
- Chapter Chapter 10: Hypothesis Testing Using a Single Sample
- Chapter Chapter 11: Comparing Two Populations or Treatments
- Chapter Chapter 12: The Analysis of Categorical Data and Goodness-of-Fit Tests
- Chapter Chapter 13: Simple Linear Regression and Correlation: Inferential Methods
- Chapter Chapter 14: Multiple Regression Analysis
- Chapter Chapter 15: Analysis of Variance
- Chapter Chapter 2: Collecting Data Sensibly
- Chapter Chapter 3: Graphical Methods for Describing Data
- Chapter Chapter 4: Numerical Methods for Describing Data
- Chapter Chapter 5: Summarizing Bivariate Data
- Chapter Chapter 6: Probability
- Chapter Chapter 7: Random Variables and Probability Distributions
- Chapter Chapter 8: Sampling Variability and Sampling Distributions
- Chapter Chapter 9: Estimation Using a Single Sample
Introduction to Statistics and Data Analysis (with CengageNOW Printed Access Card) (Available Titles CengageNOW) 3rd Edition - Solutions by Chapter
Full solutions for Introduction to Statistics and Data Analysis (with CengageNOW Printed Access Card) (Available Titles CengageNOW) | 3rd Edition
Introduction to Statistics and Data Analysis (with CengageNOW Printed Access Card) (Available Titles CengageNOW) | 3rd Edition - Solutions by ChapterGet Full Solutions
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 qualitative characteristic of an item or unit, usually arising in quality control. For example, classifying production units as defective or nondefective results in attributes data.
An equation for a conditional probability such as PA B ( | ) in terms of the reverse conditional probability PB A ( | ).
Bivariate normal distribution
The joint distribution of two normal random variables
Data consisting of counts or observations that can be classiied into categories. The categories may be descriptive.
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
An estimator that converges in probability to the true value of the estimated parameter as the sample size increases.
A correction factor used to improve the approximation to binomial probabilities from a normal distribution.
See Control chart.
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 .
Cumulative sum control chart (CUSUM)
A control chart in which the point plotted at time t is the sum of the measured deviations from target for all statistics up to time t
Used in statistical quality control, a defect is a particular type of nonconformance to speciications or requirements. Sometimes defects are classiied into types, such as appearance defects and functional defects.
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.
Erlang random variable
A continuous random variable that is the sum of a ixed number of independent, exponential random variables.
An analysis of how the variance of the random variable that represents that output of a system depends on the variances of the inputs. A formula exists when the output is a linear function of the inputs and the formula is simpliied if the inputs are assumed to be independent.
A type of experimental design in which every level of one factor is tested in combination with every level of another factor. In general, in a factorial experiment, all possible combinations of factor levels are tested.
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
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.