Search results “One way analysis variance anova”

Demonstration of how to conduct a One-Way ANOVA by hand.

Views: 724819
ArmstrongPSYC2190

statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!

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statslectures

This video is an introduction to the one-way analysis of variance (ANOVA), including a description of how it is used, its elements, and the assumptions data must meet to be analyzed by the test. The assumptions of normality and homogeneity of variances are reviewed.

Views: 1383
Dr. Todd Grande

Compare the means of three or more samples using a one-way ANOVA (Analysis of Variance) test to calculate the F statistic. This video shows one method for determining F using sums of squares.

Views: 123740
Eugene O'Loughlin

A brief introduction to one-way Analysis of Variance (ANOVA). I discuss the null and alternative hypotheses and conclusions of the test. I also illustrate the difference between and within group variance using a visual example.
In other related videos, I discuss the ANOVA formulas in detail and work through a real-world example.

Views: 65558
jbstatistics

In this video, I demonstrate how to perform and interpret a oneway analysis of variance (ANOVA) in SPSS. I do so using two different procedures and describe the benefits of each. one way anova

Views: 607397
how2stats

A description of the concepts behind Analysis of Variance. There is an interactive visualization here: http://demonstrations.wolfram.com/VisualANOVA/ but I have not tried it, and this: http://rpsychologist.com/d3-one-way-anova has another visualization

Views: 457306
J David Eisenberg

Lecture given by me at the University of Sussex 2012. Contents maps onto the 'Comparing Several Means (GLM1)' Chapter of my 'Discovering Statistics Using ...' textbooks. Looks at one-way independent ANOVA.

Views: 55054
Andy Field

Views: 16273
Evan Ortlieb

One Way ANOVA in SPSS - Part 2 (one way analysis of variance - ANOVA). Learn how to conduct the one-way analysis of variance test in this video, including running the anova in spss and interpreting the results.
Lifetime access to SPSS videos: http://tinyurl.com/kuejrzz
Video Transcript: the one-way ANOVA. To run the one way ANOVA, we want to go to Analyze and then Compare Means and then select the last option down there, One-Way ANOVA. And the One-Way ANOVA dialog box opens and what we want to do is put our dependent variable, the exam scores, into the Dependent List, and we want to put volume, our independent variable, into the Factor box. And recall that I said that the independent variable in ANOVA can also be called a factor, so that's where this term comes from. OK next we want to go to Options, and then select Descriptive, and then click Continue. And then one more thing we want to do is click on Post Hoc. Under Post Hoc, let's select Tukey. And I'll talk about post hoc tests, such as what they are, when we use them, and so forth, when we discuss our results in just a little while. One other thing worth mentioning here, the default level for the significance level is .05, and since we're using alpha .05, that's what we want to leave this at. If we were going to use alpha of .01, then we would want to change this accordingly. But since we're using .05, we'll leave it as it is. Next click Continue. And then everything looks good here, so let's click OK. And as we do that, our output or viewer window opens. And we have a few tables here. First of all, we have the Descriptives table, and we'll just take a quick glance at that. Notice our three groups here and once again remember the labels are output here, because I created value labels. There are 10 people in each group, this looks good. And notice here no music and low-volume both have means and the 84s, no music 84.9, low-volume 84.2. Whereas the high volume group had a mean at 77.5. So this group, at least in the sample, is definitely lower than these two groups, with the no music group having the best exam performance overall. Now this is just describing the results in the sample; we don't know whether these results are significant yet or not. We'll take a look at that next. Next, we go to our ANOVA table, and this is where we get the results of whether or not the groups are significantly different from one another. That is, does the level of volume in the background played while studying have an impact on exam performance? And with the ANOVA we get an F value, instead of a t, which we got with the t test, but here we get an F, and most importantly as always we have our significance value, or our p-value, and this is what we want to assess to see whether or not the test is statistically significant. And as a reminder our decision rule for assessing this test is as follows using alpha .05, if p is less than or equal to .05, the test is significant, and this indicates that the test scores differ significantly somewhere between the groups. If p is greater than .05, on the other hand, the test is not significant, indicating that the test scores do not differ significantly between the groups. So we want to look at our p-value here, compare it to these two conditions, and see which of the two it falls into. So with a p of .019, it falls into the first condition, since it's less than equal to .05. Therefore, we will conclude that the test is significant, indicating that the test scores differ significantly somewhere between the groups. And we can write our results as follows: the level of volume of music played while studying had a significant impact on exam performance. And what this is saying is the background volume of music played did influence the exam performance. In other words, exam scores differed for different volume groups. And then we have our APA format and notice now we have an F instead of a t since we're using an F-test, which you can see right here. And next we put our degrees of freedom and these degrees of freedom come from this column right here in our table, where we have between groups 2, and within groups 27, and you see here 2, 27. So if you're using APA format, you want to use these two values in your written results. Next we have an F of 4.62 which, when rounding to two decimal places, you can see right here. And finally our p-value is .019, which is reported here in the p value column shown as Sig. And, as always, we could state that p is less than .05, instead of p is equal to .019, although reporting the exact p-value is more informative, and therefore is recommended.
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ANOVA in SPSS
one-way ANOVA - SPSS
analysis of variance in SPSS
SPSS ANOVA video
ANOVA
one way ANOVA
ANOVA in SPSS
Analysis of Variance in SPSS

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Quantitative Specialists

http://thedoctoraljourney.com/ This tutorial defines an one-way ANOVA, provides examples for when this analysis might be used by a researcher, walks through the process of conducting this analysis, and discusses how to set up an SPSS file and write an APA results section for this analysis.
For more statistics, research and SPSS tools, visit http://thedoctoraljourney.com/.

Views: 32698
The Doctoral Journey

statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!

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statslectures

http://thedoctoraljourney.com/ This tutorial demonstrates how to conduct a One Way ANOVA in SPSS.
For more statistics, research and SPSS tools, visit http://thedoctoraljourney.com/.

Views: 410586
The Doctoral Journey

This tutorial shows how to use the statistics application SPSS (formerly known as PASW) to compare the means of several groups (3 in this case) on a single quantitative outcome using a one-way analysis of variance (ANOVA). It also shows how to perform a post-hoc analysis using the Tukey test (or Tukey HSD test, for Honestly Significant Difference).

Views: 32267
Barton Poulson

Many more great Excel tutorials linked below:
http://www.youtube.com/playlist?list=PL8004DC1D703D348C&feature=plcp
Be sure to watch my other Excel tutorial videos on my channel, including more advanced techniques
and many useful and practical ones. Be sure to Subscribe and Comment.
Technically you should say Fail to Reject Ho because you have determined there is a lack of evidence against Ho. You have not proven Ho in any significant way. With that said, many introductory courses teach students that they can conclude that we Accept Ho. Please be aware of the nuance regardless of how you choose to phrase the conclusion.
Reject Ho, however, is a stronger statement that we can justifiably make using the laws of probability and the level of significance of the test. When we Reject Ho we are concluding that there is enough evidence against Ho with the state level of significance used. We are willing to accept the chance of making a Type I Error, but we are very clear about the probability of its occurrence, i.e., it is equal to alpha (at least nominally).

Views: 240851
Jalayer Academy

A visual explanation and step by step guide on how to calculate a one way ANOVA using SPSS. Tutorial includes an explanation of the results.
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Related Videos: PlayList on Two Way Anova http://www.youtube.com/playlist?list=PLWtoq-EhUJe2TjJYfZUQtuq7a0dQCnOWp

Views: 164741
statisticsfun

One Way ANOVA in SPSS - Part 1 (one way analysis of variance - ANOVA). Learn how to conduct the one-way analysis of variance test in this video. This video demonstrates how to conduct a one way ANOVA in SPSS, including running the analysis, Interpreting the output, F test, writing the results in APA format, conducting post hoc tests, and calculating the effect size.
Video Transcript: In this video we'll take a look at how to run the one-way ANOVA in SPSS. ANOVA stands for analysis of variance and it's similar to the independent samples t-test, in that is used to compare independent or unrelated groups. But where the independent samples t compares only two groups, the ANOVA can be used to compare 2, 3, 4, 5, 6, or more groups - as many as you need to test. And on your screen you see an example where we have two variables. We have volume and exam scores. And we used a similar example when we conducted the independent samples t-test. In this example what I've done is I've added a third group to help underscore the relationship between these two tests. And let me show you what I mean here. First of all we have our exam scores variable, as I said, where we have the scores on the exam. This is also known as our dependent variable. And then we have our other variable, volume, where as we take a look at this variable you can see there's three groups, we have 1s, 2s, and 3s. Now if you recall the independent samples t-test had two groups as I had said before and we had 1s and 2s in our variable, our grouping variable. This is also known as our independent variable, the variable that separates or divides people into different groups. This would be what would be required for the t-test, 1s and 2s. But as we have a third group here notice how we added 3s for the third group. And if you had a fourth group you would add 4s, and so on, 5s for 5th group, etc. Now in this study what we had was we had a no music group and then as was the case with the independent t we also had a high volume group, but we added another group, and that was a low volume group. So as a reminder as to the background of this study, we had people who listen to either no volume, they had low volume now, that's our new condition, or they had high volume playing while they were studying. And then the next day they took the exam. And these once again are their exam scores. So we'll use the ANOVA to see if there's a significant difference between these groups, our 1s, 2s, and 3s or our no volume, low volume, and high volume groups. Now the one-way ANOVA, the 'one-way' stands for one independent variable, where in this example, as I said before, the independent variable is volume. And our independent variable consists of three groups: no music, low volume, and high volume. These groups can also be called levels that's l-e-v-e-l-s. So we have three levels to our independent variable. When we start doing ANOVA's we do get this new terminology, like levels which I already mentioned, and instead of an independent variable, which this variable is, it's an independent variable, we also will use the name, factor. And you'll see that when we run the analysis in a minute. There's more than one-way ANOVAs there's also a two-way ANOVA, for example. And if one-way ANOVA means one independent variable, then you can probably guess what a two-way ANOVA means, that would mean two independent variables. Such as we can have volume and then we could have gender, males and females, and then exam scores. But we're just going to focus on the one-way ANOVA here. So the one-way ANOVA will answer the question, Does the volume of background noise while studying for an exam have an impact on exam scores? So we want to know: Does volume make a difference? Do people do better in one or more conditions as compared to another? And we'll run this test using alpha .05 and it's worth noting as well that the ANOVA, unlike the t-test, which we could do a two-tailed test or a one-tailed test, the ANOVA really is one tailed by design for our purposes. So we'll just be using a one-tailed test, but that's the way it's set up to be naturally. OK one last thing too, in this design. Of these 30 people in our study, assume that they were randomly assigned to the three groups. So if I was in the study, for example, whether I received the high volume, low volume, or no volume condition was totally up to chance. That is, we random assignment of people to the respective groups. All right let's go and take a look at how to run the one-way ANOVA.
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Lifetime access to SPSS videos: http://tinyurl.com/m2532td
ANOVA
Analysis of Variance
One-way ANOVA
F test

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Quantitative Specialists

Using the same example from the Wizard of Oz involving Munchkins and wicked witches in various regions that we used learning ANOVA by hand, we are going to learn about conducting a one-way ANOVA in SPSS. We will create the dataset in SPSS, conduct a one-way ANOVA, and interpret the results, including the post hoc. Let’s take a walk down the yellow brick road and listen for the sounds of the dark side of the moon, to put us in the mood to conduct a one-way ANOVA in SPSS.

Views: 43080
Research By Design

Visual tutorial on how to calculate analysis of variance (ANOVA) and how to understand it too. The tutorial includes how to interpret the results of an Anova test, f test and how to look up values in the f distribution table. The Anova example is for a one way anova test.
I am rounding in the video, so if you are doing your own calculations you will not get the same exact numbers.
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PlayList on ANOVA
http://www.youtube.com/course?list=EC3A0F3CC5D48431B3
PlayList On TWO ANOVA
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Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet

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statisticsfun

ANOVA: Analysis Of Variance
Hey guys it looks like the audio might only be coming through the left channel on this one. Apologies for any inconvenience!
Downloadable ANOVA spreadsheet:
http://zstatistics.files.wordpress.com/2011/07/anova-spreadsheet.xlsx
0:00 Introduction
0:48 Variance and SST
1:40 Exercise 1: Finding SST
3:09 One-way ANOVA
4:48 SSW and SSB
8:10 Exercise 2: Finding SSW and SSB
9:15 F-test
11:45 MS Excel aid

Views: 31048
zedstatistics

This video describes two methods of performing a one-way ANOVA using SPSS, including how to interpret post hoc test results.

Views: 122913
Dr. Todd Grande

Discover how to calculate a oneway analysis of variance (ANOVA) using Stata. Created using Stata 12. Copyright 2011-2017 StataCorp LLC. All rights reserved.

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StataCorp LLC

Learn how to perform One-Way and Two-Way Analysis of Variance (ANOVA) in Origin when your data are organized in two different ways.

Views: 39166
OriginLab Corp.

Follow Along with this Excel Sheet: https://drive.google.com/file/d/0BxXGvoyFS1Kpc2p2eU1MV3RDMnc/edit?usp=sharing
Table of Contents: (Click to access)
Experimental Setup: 0:15
The Gist of an ANOVA: 0:49
How to Install The Data Pack: 1:11
How to Run the ANOVA: 1:53

Views: 56001
Nancy Miorelli

One Way ANOVA in SPSS - Part 3 (one way analysis of variance - ANOVA). ANOVA Post Hoc Tests are covered in this video on SPSS. Learn how to conduct post hoc tests for the one-way analysis of variance procedure.
Video Transcript: In this video we'll continue with our discussion of the analysis of the one-way ANOVA, and now we're going to focus on post-hoc tests. Recall in our prior video that our test was significant, with a p-value .019, which indicated that there was a significant difference somewhere between these three groups. But we didn't know exactly where the difference existed at this point. So we were going to do post-hoc tests to flush out those differences and see where in fact they are. Now post-hoc tests are conducted after the fact. They are typically only conducted or interpreted after a significant ANOVA. So we so We have a significant result, recall, .019, so that effectively gives us the green light to go and dive in and try and find out where the differences lie between the groups. So post-hoc tests are used to, as I just said, dive in and look for the differences between the groups and, importantly, they test each possible pair of groups. So they test two at a time. So, for example, a post-hoc test will test none versus low volume, and there will be another test that tests none vs high volume, and then there will be a final test that tests low volume versus high volume. So it does all possible pairs, two at a time. The total alpha used for the set of tests is .05, and that's important to keep in mind. It's not .05 per test, but it's .05 in total. And we're using a test which we selected in the previous video under the Post-Hoc button which was called Tukey. We're going to use Tukey's test, and Tukey's test does a good job at keeping the whole set of tests at .05. So the post-hoc tests for Tukey's test there's actually two tables that come out; there's a Post Hoc Tests table, which is labeled Multiple Comparisons, and then there's the Homogeneous Subsets table, which is labeled exam scores. We'll take a look at each of these in turn. So let's start with our Multiple Comparisons result. Now here the way this is organized is that we have our pairs organized from left to right. So, for example, this first test is none versus low volume, and if we scroll over, we can zero in on this column, these are the p-values. So here the p-value is .963. And we use the same decision rule as always, if p is less than equal to .05, there's a significant difference between the groups. If p is greater than .05, there's no significant difference. So here we can see that no music and low volume is not significantly different, since this is greater than .05. OK our next result we read here, diagonally, so we move down diagonally, and this compares the no volume versus high volume. And as we read over we can see that this test is in fact significant at .027. So there's a significant difference between no volume and high volume. Our next result, low volume versus none, if you look at this low volume versus none with the p of .963, you know we've already done this. If you notice it up here, none versus low volume, low volume versus none, these two have the exact same p-values, this is the same test. And this is one of the drawbacks of the multiple comparisons table; it produces actually the same test twice, for each test, and we've seen this one right here, so we're going to ignore this because it tells us the same thing. Next we have low volume versus high volume, and notice that p-value is .049. So that's very close, but it in fact also is significant. So there's a significant difference between the low volume and the high-volume groups. As I move down here, high volume versus none, that test has already been done here, notice the same p value .027. And then high volume low volume, we just read that one right here, with a p of .049. So, in summary, the results we have here are
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Views: 5520
Quantitative Specialists

One-way analysis of variance (ANOVA) in SPSS.

Views: 3246
Wouter SMCR

I look at the calculation formulas and the meaning of the terms in the one-way ANOVA table.
In other related videos, I have a brief introduction to one-way ANOVA, and work through a real world example.

Views: 61097
jbstatistics

Many more great Excel tutorials linked below:
http://www.youtube.com/playlist?list=PL8004DC1D703D348C&feature=plcp
Be sure to watch my other Excel tutorial videos on my channel, including more advanced techniques
and many useful and practical ones. Be sure to Subscribe and Comment.

Views: 38914
Jalayer Academy

An introduction to Two Way ANOVA (Factorial) also known as Factorial Analysis. Step by step visual instructions organize data to conduct a two way ANOVA. Includes a comparison with One Way ANOVA. Instructions on how to build a mean table.
Playlist on Two Way ANOVA
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David Longstreet Professor of the Universe
Professor of the Universe: David Longstreet http://www.linkedin.com/in/davidlongstreet/
MyBookSucks.Com

Views: 264704
statisticsfun

The assumptions for One-Way ANOVA require a scale-level dependent variable and a categorical independent variable, typically with three or more levels. Check for outliers, independence, and normality. The non-parametric alternative is the Kruskal-Wallis One Way ANOVA test. The null hypothesis for ANOVA is that the means are the same.
Table of Contents:
00:17 - Requirements for One-Way ANOVA
02:04 - Assumptions
05:05 - NHST Settings
06:59 - Critical Value for One-Way ANOVA
08:23 - Finding the Critical Value
09:04 - Homogeneity of Variance

Views: 11952
Research By Design

Comparing 3 or more groups on a single quantitative outcome.

Views: 144815
TheRMUoHP Biostatistics Resource Channel

Views: 113933
Scott Snyder

This video is an introduction to Friedman’s ANOVA, including a description of how it is used, its elements, and the assumptions data must meet to be analyzed by the test. Friedman’s ANOVA (Friedman’s test) is a nonparametric alternative to one-way analysis of variance (ANOVA). The assumptions of Friedman’s ANOVA are reviewed.

Views: 3198
Dr. Todd Grande

Views: 10600
Darryl Morrell

Views: 13657
Lillian Eidel

Introduction to Statistical Modelling
Training session with Dr Helen Brown, Senior Statistician, at The Roslin Institute, December 2015.
************************************************
These training sessions were given to staff and research students at the Roslin Institute. The material is also used for the Animal Biosciences MSc course taught at the Institute.
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*Recommended Youtube playback settings for the best viewing experience: 1080p HD
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Content:
Recap: Analysis of Variance (ANOVA)
-Tests if groups (eg genes) differ more than expected by chance
---Eg Compare level of expression between 6 genes
-Expressing the ANOVA model :
-‘Simple ANOVA’ or ‘One-way ANOVA’ (since one grouping is analysed)
Simple ANOVA example: Compare level of expression between 6 genes, 3 observations per gene
-Null hypothesis: groups have same expression levels
-Note: Sometimes model equation simply indicate the effects fitted (non-mathematical notation):
ANOVA table
-One-way ANOVA: Gene versus Expression (Minitab output)
-F statistic compared to an F distribution
-Note:
---F distribution has 2 degrees of freedom (DF)
-----DF1 relates to number of groups (DF=5 here)
-----DF2 relates to sample size (DF=12 here)
-----Use an F(5,12) distribution
---Not essential to understand calculation of F and its DF
---ANOVA on 2 groups gives an identical p-value to a t-test

Views: 2798
The Roslin Institute - Training

Learn how to use Excel to generate an ANOVA table to test if there is a significant difference (or not) between the means of three or more samples. This method uses Excel's "Analysis Toolpak" Add-In which needs to be added (how to do this is shown in the video).

Views: 51148
Eugene O'Loughlin

Analysis Of Variance (ANOVA), Multiple Comparisons & Kurskal Wallis in R ;
Dataset: https://bit.ly/2RNeR0f ANOVA Explanation: https://goo.gl/QfQv9b More Statistics and R Programming Tutorials: https://goo.gl/4vDQzT
How to conduct one way Analysis of Variance (ANOVA) in R, ANOVA Pairwise Comparison in R, (Multiple Comparisons in R), and Kruskal Wallis one-way ANOVA in R:
►►In this Tutorial you will learn to use various commands to :
► Conduct one way analysis of variance ANOVA test in R
►View ANOVA table in R
►Produce a visual display for the pair-wise comparisons of the analysis of variance in R
► Conduct multiple comparisons/ANOVA pair-wise comparisons in R
► Produce Kruskal-Wallis one-way analysis of variance using ranks with R Statistical Software.
▶︎To access and download the dataset visit https://www.statslectures.com/
■Table of Content
0:00:12 when to use one-way analysis of variance (ANOVA)
0:00:37 how to conduct ANOVA in R using the "aov" command
0:00:42 how to access the help menu in R for ANOVA commands
0:00:52 how to create a boxplot in R
0:01:42 how to view ANOVA table in R using "summary" command
0:02:07 how to ask R to let us know what is stored in an object using the "attributes" command.
0:02:23 how to extract certain attributes from an object in R using the dollar sign ($)
0:02:48 how to conduct multiple comparisons/pair-wise comparisons for the analysis of variance in R using the "TukeyHSD" command
0:03:17 how to produce a visual display for the pair-wise comparisons of the analysis of variance in R using "plot" command
0:03:50 how to produce Kruskal-Wallis one-way analysis of variance using ranks in R using the "kruskal.test" command
0:03:56 when is it appropriate to use Kruskal-Wallis one-way analysis of variance for data
This video is a tutorial for programming in R Statistical Software for beginners, using RStudio.
▶︎▶︎ Watch More
▶︎ANOVA Use and Assumptions https://youtu.be/_VFLX7xJuqk
▶︎ Understanding Sum of Squares in ANOVA ,concept of analysis of variance, and ANOVA hypothesis testing https://youtu.be/-AeU4y2vkIs
▶︎ ANOVA Test Statistic and P Value: https://youtu.be/k-xZzEYL8oc
▶︎ ANOVA & Bonferroni Multiple Comparisons Correction https://youtu.be/pscJPuCwUG0
▶︎ Two Sample t test for independent groups https://youtu.be/mBiVCrW2vSU
▶︎ Paired t test https://youtu.be/Q0V7WpzICI8
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These #RTutorial are created by #marinstatslectures to support the statistics course (#SPPH400) at The University of British Columbia(UBC) although we make all videos available to the public for free.
Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!

Views: 119450
MarinStatsLectures-R Programming & Statistics

This video is an introduction to the two-way analysis of variance (two-way ANOVA; factorial ANOVA), including a description of how it is used, its elements, and the assumptions data must meet to be analyzed by the test. Main and interaction effects are reviewed as well as the assumptions of normality and homogeneity of variances.

Views: 2997
Dr. Todd Grande

Views: 576
prof smith

A one-way ANOVA in MATLAB is implemented using the 'anova1' function, which will return a p-value. A low p-value provides evidence that at least one of the population means differs from the others. The 'anova1' function also returns an ANOVA table, and a box-and-whisker plot that contains the range of the data, the inter-quartile range, the median, and the 95% confidence interval for the median.
Made by faculty at the University of Colorado Boulder, Department of Mechanical Engineering and produced by the Department of Chemical & Biological Engineering.
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Views: 11368
LearnChemE

One Way ANOVA in SPSS - Part 4 (one way analysis of variance - ANOVA). ANOVA Post Hoc Tests are covered in this video on SPSS. Learn how to conduct post hoc tests for the one-way analysis of variance procedure.
Lifetime access to SPSS videos: http://tinyurl.com/m2532td
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Video Transcript: no music versus low volume. Take a moment to follow along with me here, to make sure you can line these up in the table. No music versus low volume had a p of .963, so that's none versus low, p = .963. And then no music vs high-volume, none vs high, had a p of .027, you can see that right here. And then low volume versus high volume, this result right here, low versus high, had a p of .049, and you can see that right here. And we found that this first test was not significant and the latter two tests were significant. So no music vs high, low volume versus high volume. So if we look at these means here, I have the means presented here, and they were obtained by going to the Descriptives table. So notice these here 84.90, 84.20, and 77.50. These are just displayed once again here, you see the three matched up. OK so I have the means here to make this a little bit easier, and for the significant results, what I want to do is see which group had the better exam performance. So for no music vs high-volume, notice the no music mean is 84.90, while the high-volume mean is 77.50. Since it was significant, this indicates that no music performed significantly better than high volume, so I'm going to write this here. No was greater than high, just short-handed there. And then low volume versus high-volume was also significant, this was very close, but it was in fact significant. So let's compare these two means. Low is 84.20 and high is 77.50, so we can see here that the low volume group performed significantly better than the high volume group, so we'll write that here. Low greater than high, just short-handing in our results. And then, once again, no music versus low volume was not significant. So we could summarize the results at this point - these are all our tests - there was not a significant difference between no music and low volume and then we could say that the no music condition and the low volume condition, these were both significantly higher than the high volume condition. Now let's take a moment to look at our final table, the Homogeneous Subsets table. And the way to interpret this table is as follows: groups that share the same column are not significantly different, so if groups share column, there's no difference. Groups that do not share the same column, are significantly different. So let's take a look at that in our table here. Let's start with the groups that share a column. Notice here in column 2 we have low volume and we have none or no music, no volume, these two groups share column 2. So that means that, since they share the same column, they are not significantly different. So we would say low and no volume are not significantly different. However, once again, groups that do not share the same column are significantly different. So notice that the high volume group is in its own column; it doesn't share the same column with low and none which is right here. So this indicates, since they don't share the same column, this indicates that low volume and none are in fact significantly different than high. And the nice thing about this table is not only is it not redundant as was the Multiple Comparisons table, where it presented every result twice, but it also has the means inside the table, so I know that low and none are significantly different than high. But I can actually see the means here as well, so when I know there's a difference, all I have to do is look at the means and see which ones are higher. So I can see that low volume and none are in fact higher than high volume, so they're significantly higher in terms of exam scores than the high volume group. So I could summarize these results as follows: there was not a significant difference between the low volume and the no volume group, you see that right here, however the low volume group and the no volume group were significantly higher than the high volume group. So, once again, these two tables will give you the same results, they're just presented somewhat differently. Now some post-hoc tests are presented with this type of table, and then some are presented with this type of table as well. But some of the tests available under the Post Hoc button do not have this type of presentation at all. We're going to focus on Tukey's test, because research indicates that it's a pretty good test, and it still does a good job at keeping our alpha at .05 overall. This concludes the discussion of post-hoc tests for the one-way ANOVA. Thanks for watching.

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Quantitative Specialists

One way analysis of variance (One Way ANOVA) procedures produce an analysis for a quantitative dependent variable affected by a single factor (independent variable). Analysis of variance is used to test the hypothesis that several means are equal. This technique is an extension of the two-sample t test.
The instructions and examples will help guide you through one way anova (analysis of variance).
#myCalStateLA #mycsula #its #SPSS

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MyCalStateLA

The Analysis of Variance (ANOVA) is a hypothesis test to compare the means of more than two populations. For more free math videos, visit: http://www.professorserna.com
Here, I explain the steps of the ANOVA Complete Randomized Design: 1)Setting up the Null hypothesis that all the means are equal and the Alternative hypothesis that at least two mean differ. 2) Calculation of the test statistics (the actual calculations are done in the next video of this series). 3) Finding the rejection region using the F distribution. 4) Making the decision whether to reject or not to reject Ho and 5) Writing the conclusion.
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ProfessorSerna

Get this full Course at http://www.MathTutorDVd.com.
This lesson covers the technique known as analysis of variance (anova) in statistics. We will first begin by discussing what anova is and why it is a useful tool to use to solve problems. Specifically, we will discuss the one way anova technique. We will discuss example problems, the concept of the anova table, and how it relates to the definition of anova.
Next, we will provide concrete examples to illustrate the technique behind analysis of variance. In future lessons, we will solve a great many problems that require anova and show and explain every step. In future lessons we will also use microsoft excel to solve anova problems in statistics.

Views: 90726
mathtutordvd

In this video, I demonstrate how to perform and interpret a oneway analysis of variance (ANOVA) in SPSS. I do so using two different procedures and describe the benefits of each.

Views: 111500
how2stats

You should report a measure of effect size whenever you report a statistical test. We will review the difference between statistical significance and practical significance, then learn how to calculate eta squared, partial eta squared, and omega squared. Finally, we will see how to calculate partial eta squared in SPSS.

Views: 6803
Research By Design

The complete video covering the ANOVA and post hoc tests can be found here: https://www.youtube.com/watch?v=ykGAuUot1cc&list=PLRV_2nAtkiVNt7K5Rnch1pD2WSvl76y8j

Views: 1770
Quantitative Specialists

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). Variance within the groups is due only to chance (or error). This is the variance that we are analyzing.
Table of Contents:
00:30 - Definitions for Analysis of Variance
01:42 - Step 1: Omnibus Test
02:42 - The F Ratio
03:22 - Logic of Analysis of Variance
04:13 - Distribution of F Ratios
05:41 - Examples of Between and Within
07:56 - Step 2: Post Hoc Test
09:02 - Post Hoc Tests in SPSS
10:39 - Example
11:46 - Example

Views: 9925
Research By Design

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