- 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
`-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).
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
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 method of variable selection in regression that begins with all of the candidate regressor variables in the model and eliminates the insigniicant regressors one at a time until only signiicant regressors remain
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.
The tendency of data to cluster around some value. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.
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
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria
A linear function of treatment means with coeficients that total zero. A contrast is a summary of treatment means that is of interest in an experiment.
A square matrix that contains the correlations among a set of random variables, say, XX X 1 2 k , ,…, . The main diagonal elements of the matrix are unity and the off-diagonal elements rij are the correlations between Xi and Xj .
A subset of effects in a fractional factorial design that deine the aliases in the design.
A model to relate a response to one or more regressors or factors that is developed from data obtained from the system.
A study in which a sample from a population is used to make inference to the population. See Analytic study
Error mean square
The error sum of squares divided by its number of degrees of freedom.
Fisher’s least signiicant difference (LSD) method
A series of pair-wise hypothesis tests of treatment means in an experiment to determine which means differ.
Fractional factorial experiment
A type of factorial experiment in which not all possible treatment combinations are run. This is usually done to reduce the size of an experiment with several factors.
Another name for the normal distribution, based on the strong connection of Karl F. Gauss to the normal distribution; often used in physics and electrical engineering applications
Effects in a fractional factorial experiment that are used to construct the experimental tests used in the experiment. The generators also deine the aliases.