
From Jane Harvill
In this final topic, we consider using the estimated regression line to estimate the mean response at a given value of the explanatory (x) variable. The standard error… 
From Jane Harvill
In this lesson, the assumption of the normality of errors, and hence the normality of the responses is added to the simple linear regression model. The consequences are… 
From Jane Harvill
In this lesson, two models for paired data are introduced that are also called simple linear regression models. In the previous lesson, the estimators we derived… 
From Jane Harvill
Methods for estimating the coefficients in the simple linear regression model are investigated. The least squares method is a purely mathematical approach, requiring no… 
From Jane Harvill
In analysis of variance, we looked at how one factor (a discrete or a categorical variable) influenced the means of a response variable. We now turn to simple linear… 
From Jane Harvill
Linear combinations of means, and in particular contrasts, play an important role in analysis of variance (ANOVA). Through understanding and analyzing contrasts,… 
From Jane Harvill
In this lesson, we consider the "Classic ANOVA Hypothesis." Assumptions on the cell means model are described and used to devise an F test for the hypothesis. 
From Jane Harvill
Up until now, we have modeled random variables with a probability mass function or a probability density function. We then discussed in detail the theory behind… 
From Jane Harvill
In this lesson, some approximate and asymptotic versions of confidence sets are explored. The purpose here is to illustrate some methods that will be of use in more… 
From Jane Harvill
The lesson describes a few methods for deriving some tests in complicated problems in which no optimal test (such as the uniformly most powerful test) exists or is… 
From Jane Harvill
There are several properties to be considered when considering a point estimator from an asymptotic perspective. In this lesson, the concepts of consistency and… 
From Jane Harvill
Loss function for confidence sets (or credible sets) combines the maximum coverage probability and shortest interval criteria into one function  the loss function. The… 
From Jane Harvill
The goal of obtaining a smallest confidence set with a specified coverage probability can also be attained using Bayesian criteria. If we have a posterior distribution… 
From Jane Harvill
Since there is a onetoone correspondence between confidence sets and tests of hypotheses, there is some correspondence between the optimality of tests and the… 
From Jane Harvill
There are a number of methods for finding an interval estimator. In the frequentist domain, the three methods previously discussed are inverting the acceptance region…