- 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
A formula used to determine the probability of the union of two (or more) events from the probabilities of the events and their intersection(s).
The arithmetic mean of a set of numbers x1 , x2 ,…, xn is their sum divided by the number of observations, or ( / )1 1 n xi t n ? = . The arithmetic mean is usually denoted by x , and is often called the average
See Arithmetic mean.
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
In experimental design, a group of experimental units or material that is relatively homogeneous. The purpose of dividing experimental units into blocks is to produce an experimental design wherein variability within blocks is smaller than variability between blocks. This allows the factors of interest to be compared in an environment that has less variability than in an unblocked experiment.
Box plot (or box and whisker plot)
A graphical display of data in which the box contains the middle 50% of the data (the interquartile range) with the median dividing it, and the whiskers extend to the smallest and largest values (or some deined lower and upper limits).
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.
Chi-square (or chi-squared) random variable
A continuous random variable that results from the sum of squares of independent standard normal random variables. It is a special case of a gamma random variable.
A subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.
In regression, Cook’s distance is a measure of the inluence of each individual observation on the estimates of the regression model parameters. It expresses the distance that the vector of model parameter estimates with the ith observation removed lies from the vector of model parameter estimates based on all observations. Large values of Cook’s distance indicate that the observation is inluential.
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.
Defects-per-unit control chart
See U chart
A matrix that provides the tests that are to be conducted in an experiment.
Discrete random variable
A random variable with a inite (or countably ininite) range.
Distribution free method(s)
Any method of inference (hypothesis testing or conidence interval construction) that does not depend on the form of the underlying distribution of the observations. Sometimes called nonparametric method(s).
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.
Erlang random variable
A continuous random variable that is the sum of a ixed number of independent, exponential random variables.
An arrangement of the frequencies of observations in a sample or population according to the values that the observations take on
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
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 .
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