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Factorial Analysis of Variance (ANOVA)
Many empirical studies involve two or more independent variables. Analysis of variance (ANOVA) can easily be extended to these situations, and in...
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One-Way Analysis of Variance (ANOVA)
The methods introduced so far test difference hypotheses for a maximum of two groups or conditions with a t-test. Many research designs will actually...
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Repeated-Measures Analysis of Variance (ANOVA)
Just as the one-way (i.e., single-factor) analysis of variance (ANOVA) was a generalization of the t-test for independent samples, the...
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Regression Analysis and the Analysis of Variance
In this chapter, we provide derivations of the formulas for simple and multiple linear regression. In obtaining these results, the partitioning of...
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A Variance-Based Sensitivity Analysis Approach for Identifying Interactive Exposures
Chemical mixtures can significantly affect human health, but understanding the interactions among various chemical exposures and identifying...
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Inference Using Analysis of Variance (ANOVA) for Comparing Multiple Means
This chapter discusses single-factor analysis of variance (ANOVA), which is mainly applied to compare three or more independent means. The term...
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Analysis of Variance and Chi-Square Tests
Often in statistics, we are interested in whether two or more samples have the same mean from the same population. Statistics uses the concept of...
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Variance Control Procedures
This chapter covers the concepts of variance and sources of variation for clinical trial data. Common metrics to quantify the extent of variability...
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Variance Reduction
The Monte Carlo methods described in the previous chapters are subject to variation due to the randomness inherent in sample generation. Ideally we...
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Basis expansion approaches for functional analysis of variance with repeated measures
The methodological contribution in this paper is motivated by biomechanical studies where data characterizing human movement are waveform curves...
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Fuzzy Analysis of Variance
The aim of this chapter is to expose a multi-ways fuzzy analysis of variance (Mult-FANOVA) approach when the fuzziness is taken into consideration....
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Change point in variance of fractionally integrated noise
This paper studies the quasi-maximum likelihood estimator (quasi-MLE) of a change point in variance for the fractionally integrated noise with memory...
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The estimated causal effect on the variance based on the front-door criterion in Gaussian linear structural equation models: an unbiased estimator with the exact variance
In this paper, we assume that cause–effect relationships between random variables can be represented by a Gaussian linear structural equation model...
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Variance estimation for average treatment effects estimated by g-computation
The average treatment effect is used to evaluate effects of interventions in a population. Under certain causal assumptions, such an effect may be...
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Simplicial and Minimal-Variance Distances in Multivariate Data Analysis
In this paper, we study the behaviour of the so-called k -simplicial distances and k -minimal-variance distances between a point and a sample. The...
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Mean and Variance for Count Regression Models Based on Reparameterized Distributions
We introduce a new regression model for count data where the response variable is mainly in the class of inflated-parameter generalized power series...
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The Analysis of Variance
In studying methods for the analysis of quantitative data, we first focused on problems involving a single sample of numbers and then turned to a...
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Oneway Analysis of Variance (ANOVA)
The purpose of this lesson is to demonstrate how the R environment supports Oneway Analysis of Variance (Oneway ANOVA), the empirical test used to...
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Variance matrix estimation in multivariate classical measurement error models
Measurement errors are often encountered in several continuous variables in a data set, and various methods have been proposed to handle these...
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Twoway Analysis of Variance (ANOVA)
The purpose of this lesson on Twoway Analysis of Variance (ANOVA) is to provide guidance on how R can be used to see if two or more group means...