- Chapter 1: Introduction to Statistics
- Chapter 1.2: Statistical and Critical Thinking
- Chapter 1.3: Types of Data
- Chapter 1.4: Collecting Sample Data
- Chapter 10: Correlation and Regression
- Chapter 10.2: Correlation
- Chapter 10.3: Regression
- Chapter 10.4: Rank Correlation
- Chapter 11: Chi-Square and Analysis of Variance
- Chapter 11.2: Goodness-of-Fit
- Chapter 11.3: Contingency Tables
- Chapter 11.4: Analysis of Variance
- Chapter 2: Summarizing and Graphing Data
- Chapter 2.2: Frequency Distributions
- Chapter 2.3: Histograms
- Chapter 2.4: Graphs That Enlighten and Graphs That Deceive
- Chapter 3: Statistics for Describing, Exploring, and Comparing Data
- Chapter 3.2: Measures of Center
- Chapter 3.3: Measures of Variation
- Chapter 3.4: Measures of Relative Standing and Boxplots
- Chapter 4: Probability
- Chapter 4.2: Basic Concepts of Probability
- Chapter 4.3: Addition Rule
- Chapter 4.4: Multiplication Rule: Basics
- Chapter 4.5: Multiplication Rule: Complements and Conditional Probability
- Chapter 4.6: Counting
- Chapter 5: Discrete Probability Distributions
- Chapter 5.2: Probability Distributions
- Chapter 5.3: Binomial Probability Distributions
- Chapter 5.4: Parameters for Binomial Distributions
- Chapter 6: Normal Probability Distributions
- Chapter 6.2: The Standard Normal Distribution
- Chapter 6.3: Applications of Normal Distributions
- Chapter 6.4: Sampling Distributions and Estimators
- Chapter 6.5: The Central Limit Theorem
- Chapter 6.6: Assessing Normality
- Chapter 6.7: Normal as Approximation to Binomial
- Chapter 7: Estimates and Sample Sizes
- Chapter 7.2: Estimating a Population Proportion
- Chapter 7.3: Estimating a Population Mean
- Chapter 7.4: Estimating a Population Standard Deviation or Variance
- Chapter 8: Hypothesis Testing
- Chapter 8.2: Basics of Hypothesis Testing
- Chapter 8.3: Testing a Claim About a Proportion
- Chapter 8.4: Testing a Claim about a Mean
- Chapter 8.5: Testing a Claim About a Standard Deviation or Variance
- Chapter 9: Inferences from Two Samples
- Chapter 9.2: Two Proportions
- Chapter 9.3: Two Means: Independent Samples
- Chapter 9.4: Two Dependent Samples (Matched Pairs)
Essentials of Statistics 5th Edition - Solutions by Chapter
Full solutions for Essentials of Statistics | 5th Edition
2 k p - factorial experiment
A fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) each
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
Sequences of independent trials with only two outcomes, generally called “success” and “failure,” in which the probability of success remains constant.
When y fx = ( ) and y is considered to be caused by x, x is sometimes called a causal variable
Central composite design (CCD)
A second-order response surface design in k variables consisting of a two-level factorial, 2k axial runs, and one or more center points. The two-level factorial portion of a CCD can be a fractional factorial design when k is large. The CCD is the most widely used design for itting a second-order model.
Coeficient of determination
See R 2 .
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.
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
A tabular arrangement expressing the assignment of members of a data set according to two or more categories or classiication criteria
Continuous uniform random variable
A continuous random variable with range of a inite interval and a constant probability density function.
In the most general usage, a measure of the interdependence among data. The concept may include more than two variables. The term is most commonly used in a narrow sense to express the relationship between quantitative variables or ranks.
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 .
Formulas used to determine the number of elements in sample spaces and events.
An expression sometimes used for nonlinear regression models or polynomial regression models.
A parameter in a tabular CUSUM algorithm that is determined from a trade-off between false alarms and the detection of assignable causes.
The amount of variability exhibited by data
The distribution of the random variable deined as the ratio of two independent chi-square random variables, each divided by its number of degrees of freedom.
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.
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 .