- Chapter 1:
- Chapter 1: What Is Statistics?
- Chapter 10:
- Chapter 10: Hypothesis Testing
- Chapter 11:
- Chapter 11: Linear Models and Estimation by Least Squares
- Chapter 12:
- Chapter 12: Considerations in Designing Experiments
- Chapter 13:
- Chapter 13: The Analysis of Variance
- Chapter 14:
- Chapter 14: Analysis of Categorical Data
- Chapter 15:
- Chapter 15: Nonparametric Statistics
- Chapter 16:
- Chapter 16: Introduction to Bayesian Methods for Inference
- Chapter 2:
- Chapter 2: Probability
- Chapter 3:
- Chapter 3: Discrete Random Variables and Their Probability Distributions
- Chapter 4:
- Chapter 4: Continuous Variables and Their Probability Distributions
- Chapter 5:
- Chapter 5: Multivariate Probability Distributions
- Chapter 6:
- Chapter 6: Functions of Random Variables
- Chapter 7:
- Chapter 7: Sampling Distributions and the Central Limit Theorem
- Chapter 8:
- Chapter 8: Estimation
- Chapter 9:
- Chapter 9: Properties of Point Estimators and Methods of Estimation
Mathematical Statistics with Applications 7th Edition - Solutions by Chapter
Full solutions for Mathematical Statistics with Applications | 7th Edition
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
Conditional probability mass function
The probability mass function of the conditional probability distribution of a discrete random variable.
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.
Another name for factors that are arranged in a factorial experiment.
Cumulative distribution function
For a random variable X, the function of X deined as PX x ( ) ? that is used to specify the probability distribution.
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.
A subset of effects in a fractional factorial design that deine the aliases in the design.
The response variable in regression or a designed experiment.
The amount of variability exhibited by data
Error of estimation
The difference between an estimated value and the true value.
Error sum of squares
In analysis of variance, this is the portion of total variability that is due to the random component in the data. It is usually based on replication of observations at certain treatment combinations in the experiment. It is sometimes called the residual sum of squares, although this is really a better term to use only when the sum of squares is based on the remnants of a model-itting process and not on replication.
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.
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 signal from a control chart when no assignable causes are present
Fixed factor (or fixed effect).
In analysis of variance, a factor or effect is considered ixed if all the levels of interest for that factor are included in the experiment. Conclusions are then valid about this set of levels only, although when the factor is quantitative, it is customary to it a model to the data for interpolating between these levels.
A method of variable selection in regression, where variables are inserted one at a time into the model until no other variables that contribute signiicantly to the model can be found.
An arrangement of the frequencies of observations in a sample or population according to the values that the observations take on
The geometric mean of a set of n positive data values is the nth root of the product of the data values; that is, g x i n i n = ( ) = / w 1 1 .
Geometric random variable
A discrete random variable that is the number of Bernoulli trials until a success occurs.
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.
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