Statistical hypothesis testing | Statistical tests
Omnibus tests are a kind of statistical test. They test whether the explained variance in a set of data is significantly greater than the unexplained variance, overall. One example is the F-test in the analysis of variance. There can be legitimate significant effects within a model even if the omnibus test is not significant. For instance, in a model with two independent variables, if only one variable exerts a significant effect on the dependent variable and the other does not, then the omnibus test may be non-significant. This fact does not affect the conclusions that may be drawn from the one significant variable. In order to test effects within an omnibus test, researchers often use contrasts. Omnibus test, as a general name, refers to an overall or a global test. Other names include F-test or Chi-squared test. It is a statistical test implemented on an overall hypothesis that tends to find general significance between parameters' variance, while examining parameters of the same type, such as:Hypotheses regarding equality vs. inequality between k expectancies μ1 = μ2 = ⋯ = μk vs. at least one pair μj ≠ μj′, where j, j′ = 1, ..., k and j ≠ j′, in Analysis Of Variance (ANOVA); or regarding equality between k standard deviations σ1 = σ2= ⋯ = σk vs. at least one pair σj ≠ σj′ in testing equality of variances in ANOVA; or regarding coefficients β1 = β2 = ⋯ = βk vs. at least one pair βj ≠ βj′ in Multiple linear regression or in Logistic regression. Usually, it tests more than two parameters of the same type and its role is to find general significance of at least one of the parameters involved. (Wikipedia).
A silent video testing series for convergence or divergence using the comparison test
From playlist 242 spring 2012 exam 3
3_5_1 The Integral Test and Comparison Tests
The integral test for convergence of a series.
From playlist Advanced Calculus / Multivariable Calculus
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From playlist 360° videos
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From playlist Minitab and Minitab Express Demonstrations
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Lecturer: Rebecca Knoph Missouri State University Fall 2015 This video covers how to run a between-subjects ANOVA with two independent variables in SPSS. Lecture materials and assignments available at statisticsofdoom.com. https://statisticsofdoom.com/page/advanced-statistics/
From playlist Intermediate Statistics Videos
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From playlist DevOpsDays Rockies 2016
SPSS - One-Way Repeated Measures ANOVA Example 2
Recorded by Rebecca Knoph Missouri State University Fall 2015 This video covers how to run a one-way Repeated measures ANOVA on SPSS. All lecture materials and assignments can be found on statisticsofdoom.com https://statisticsofdoom.com/page/advanced-statistics/
From playlist Basic Statistics Videos
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When measuring groups with ANOVA, there are two sources of variance: between and within. Variance between groups is due to actual treatment effect plus differences due to chance (or error). True variance between indicates differences between groups. Variance within the groups is due only t
From playlist WK12 One-Way ANOVA - Online Statistics for the Flipped Classroom
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https://www.patreon.com/ProfessorLeonard Statistics Lecture 3.3: Finding the Standard Deviation of a Data Set
From playlist Statistics (Full Length Videos)
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Lecturer: Rebecca Knoph Missouri State University Fall 2015 This video covers how to run a repeated-measures ANOVA with two independent variables in SPSS. Lecture materials and assignments available at statisticsofdoom.com. https://statisticsofdoom.com/page/advanced-statistics/
From playlist Intermediate Statistics Videos
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Lecturer: Rebecca Knoph Missouri State University Fall 2015 This video covers how to run a one way between subjects ANOVA in SPSS using compare means. Lecture materials and assignments available at statisticsofdoom.com. https://statisticsofdoom.com/page/advanced-statistics/
From playlist Basic Statistics Videos
3_5_2 The Integral Test and Comparison Tests
The integral test for convergence of a series.
From playlist Advanced Calculus / Multivariable Calculus
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https://www.patreon.com/ProfessorLeonard Statistics Lecture 8.2: An Introduction to Hypothesis Testing
From playlist Statistics (Full Length Videos)
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From playlist Jamovi Tutorials
JASP 0.10.1 Tutorial: One-Way Analysis of Variance (Episode 10)
In this JASP tutorial, I go through a One-Way Analysis of Variance (ANOVA) example, discussing and explaining each option you can use to fully explore the test. NOTE: This tutorial is the first of the series that uses the new preview build of 0.10.1.0. Episodes 1-9 are on preview build 0.
From playlist JASP Tutorials
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From playlist Unit 9: t Inference and 2-Sample Inference
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From playlist Learning medical statistics with python and Jupyter notebooks
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From playlist IT'S HISTORY Feature Videos
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From playlist Random Blender Tests