It was not always the case that the end of the planned set of slides was reached in each class, so the last slides in one deck may be repeated in the next deck.Ĭonditional probability, Bayes' theorem (PDF)ĭiscrete random variables, expectation (PDF) The post-class version of the slides contains the solutions to the board problems, clicker questions, and discussion questions that were posed to the students during class. Listed in the following table are the in-class slides and post-class materials for each of the class sessions.
#HOGG PROBABILITY AND STATISTICAL INFERENCE SOLUTION PDF SERIES#
įor a random variable X, the function of X deined as PX x ( ) ? that is used to specify the probability distribution.Ī parameter in a tabular CUSUM algorithm that is determined from a trade-off between false alarms and the detection of assignable causes.Ī model to relate a response to one or more regressors or factors that is developed from data obtained from the system.Ī series of tests in which changes are made to the system under studyĪ random variable that generalizes an Erlang random variable to noninteger values of the parameter rĪ discrete random variable that is the number of Bernoulli trials until a success occurs.Arrow_back browse course material library_books The correction factor can also be written as nx 2. Large values of Cook’s distance indicate that the observation is inluential.Ī term used for the quantity ( / )( ) 1 1 2 n xi i n ? = that is subtracted from xi i n 2 ? =1 to give the corrected sum of squares deined as (/ ) ( ) 1 1 2 n xx i x i n ? = i ?. 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.
In regression, Cook’s distance is a measure of the inluence of each individual observation on the estimates of the regression model parameters. A contrast is a summary of treatment means that is of interest in an experiment. The data from the experiment are used to evaluate the treatments.Īnother term for the conidence coeficient.Īn estimator that converges in probability to the true value of the estimated parameter as the sample size increases.Ī linear function of treatment means with coeficients that total zero. Central tendency is usually expressed by a measure of location such as the mean, median, or mode.Ī subset selected without replacement from a set used to determine the number of outcomes in events and sample spaces.Īn experiment in which the treatments (experimental conditions) that are to be studied are included in the experiment. The tendency of data to cluster around some value. The estimator is obtained from the posterior distribution.Ī 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). See ProbabilityĪn estimator for a parameter obtained from a Bayesian method that uses a prior distribution for the parameter along with the conditional distribution of the data given the parameter to obtain the posterior distribution of the parameter. Eficient computer algorithms have been developed for implementing all possible regressionsĪ set of rules that probabilities deined on a sample space must follow. Key Statistics Terms and definitions covered in this textbookĪ fractional factorial experiment with k factors tested in a 2 ? p fraction with all factors tested at only two levels (settings) eachĪ method of variable selection in regression that examines all possible subsets of the candidate regressor variables.
Chapter 8.7: Tests of Statistical Hypotheses.Chapter 8.6: Tests of Statistical Hypotheses.Chapter 8.5: Tests of Statistical Hypotheses.Chapter 8.4: Tests of Statistical Hypotheses.Chapter 8.3: Tests of Statistical Hypotheses.Chapter 8.2: Tests of Statistical Hypotheses.Chapter 8.1: Tests of Statistical Hypotheses.Chapter 5.9: Distributions of Functions of Random Variables.Chapter 5.8: Distributions of Functions of Random Variables.Chapter 5.7: Distributions of Functions of Random Variables.Chapter 5.6: Distributions of Functions of Random Variables.Chapter 5.5: Distributions of Functions of Random Variables.Chapter 5.4: Distributions of Functions of Random Variables.Chapter 5.3: Distributions of Functions of Random Variables.Chapter 5.2: Distributions of Functions of Random Variables.
Chapter 5.1: Distributions of Functions of Random Variables.
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