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The MANOVA series is available as an iTune book for FREE. This may be accessed at the following URL. https://itunes.apple.com/us/book/manova/id657187300?ls=1 This video provides an introduction to MANOVA. Topics include a description of what MANOVA really is, the assumptions of MANOVA, writing research questions and hypotheses, and identification of the required statistics.
Views: 45051 Lee Rusty Waller
A Webcast to accompany my 'Discovering Statistics Using ....' textbooks. This looks at how to do MANOVA on SPSS and interpret the output.
Views: 152141 Andy Field
http://thedoctoraljourney.com/ This tutorial overviews ANOVAs and MANOVAs and discusses when these analyses might be used by a researcher. For more statistics, research and SPSS tools, visit http://thedoctoraljourney.com/.
Views: 46347 The Doctoral Journey
This video is an introduction to the one-way multivariate analysis of variance (one-way MANOVA) 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 multivariate normality, homogeneity of variance-covariance matrices, and linearity are reviewed.
Views: 4472 Dr. Todd Grande
Applied Multivariate Statistical Modeling by Dr J Maiti,Department of Management, IIT Kharagpur.For more details on NPTEL visit http://nptel.ac.in
Views: 12974 nptelhrd
How to run and interpret the results of a MANOVA in SPSS is covered in this video (part 1). Video Transcript: So let's go ahead and get started with our problem in SPSS. Now in SPSS you can see here we have three variables, gender, where we have males and females; I can turn those value labels off for a minute. We have males and females given by 1s and 2s, and we have our two dependent variables, empathic concern and cognitive perspective taking. So notice here we have to have at least two variables once again for MANOVA, so you can see those here, and then we have our grouping variable which is gender. So let's go ahead and run the MANOVA. So to do that we want to go to Analyze and then General Linear Model and then Multivariate. And here the Multivariate dialog box opens. We're going to take our two dependent variables, empathic concern and perspective-taking, move those over to the dependent variables box, and I just pressed and held the control key to grab both of those at once, and then move gender to Fixed Factor(s). And then let's go ahead and go to Options, select gender and move that over to the Display Means for box, check on Descriptive statistics, Estimates of effect size, and Homogeneity tests, and then click Continue and then go ahead and click OK. Next we get our results out, and you can see here our first table shows us our Between-Subjects Factors, and here we have gender, 1 and 2, 1 is male 2 is female, and notice we have 15 adolescents in each group. Next is our Descriptive Statistics table. We have empathic concern for males and females, so here's these two means, and notice in the sample, let's just go ahead and go through this and ask yourself, which of the two genders scored higher on empathic concern? Now we don't know if this is significant yet, but just descriptively, just visually inspecting. Notice that females scored higher than males by 8.27 points, approximately, on empathic concern. And then here on perspective-taking, we can see that females scored about almost three points higher on perspective-taking than did males. OK next we have Box's Test for Equality of Covariance Matrices. Now this test tests an assumption of the MANOVA, which is that the variance-covariance matrices, also referred to as the covariance matrices, as you see here, are equal for the two groups. Now we're going to have a separate video on this assumption to look at it in more detail, but basically in a nutshell it's very similar to the equal variance assumption for the ANOVA; this assumption is the multivariate generalization or extension of the assumption of equal variances for the ANOVA, it's testing the corresponding variances and covariances are equal for the two groups. But for now all you really need to know is that we want to look at our p-value here and we hope that this is greater than .05. But in fact this test, Box's test the equality of covariance matrices, is so sensitive to departures from non- normality, that a lot of people will even use a standard of p is greater than .001. So, basically, we want this result to be not significant. So if we use the more lenient standard and use p is greater than .001, then you can clearly see, well at either level (.05 or .001), this is not significant. So that provides us some evidence that the variance-covariance matrices are equal for the two groups. And that's an assumption of MANOVA, we want that to hold. So, unlike our significance tests on means, we want this to be greater than .05 or greater than .001. And in that other video I'll go into more detail, some of the subtleties of that, and the research findings as well, and what we can do if we do have a p-value that's less than say, .001, for Box's test. OK next we'll go to our Multivariate Tests box MANOVA Multivariate analysis of variance MANOVA in SPSS Wilk's Lambda Pillai Trace DFA Hotelling's T-squared For more on inferential Statistics: https://www.udemy.com/inferential-statistics-spss/ Lifetime access to SPSS videos: http://tinyurl.com/m2532td Channel Description: https://www.youtube.com/user/statisticsinstructor For step by step help with statistics, with a focus on SPSS. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor
1. What is MANOVA 2. Difference between MANOVA and ANOVA 3. NULL Hypothesis of MANOVA 4. Execution steps of MANOVA 5. MANOVA using R
Views: 9474 Gopal Malakar
Subject: Social Work Education Paper:Research Methods and Statistics Module: Univariate Analysis & Bivariate Analysis Content Writer: Dr. Graciella Tavares
Views: 37908 Vidya-mitra
This video demonstrates how to conduct and interpret a one-way MANOVA with two dependent variables in SPSS. Methods for testing the assumptions of MANOVA are reviewed.
Views: 29805 Dr. Todd Grande
This guide shows you how to run a MANOVA is SPSS. Two examples are included one with a two level IV and another with a three level IV. Write up of all stats is inluded
Subject:Statistics Paper: Multivariate analysis
Views: 231 Vidya-mitra
Currell: Scientific Data Analysis. SPSS analyses for the data in Figs 3.11 and 3.15 http://ukcatalogue.oup.com/product/9780198712541.do © Oxford University Press
Let's learn about Chi-square, t-test, and ANOVA!
Views: 43493 ChrisFlipp
Multivariate analysis of variance(MANOVA) is a multivariate extension of analysis if variance. (Recorded with http://screencast-o-matic.com)
This video is an introduction to the two-way multivariate analysis of variance (two-way MANOVA) 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 multivariate normality, homogeneity of variance-covariance matrices, and linearity are reviewed.
Views: 3237 Dr. Todd Grande
Introduction to Univariate Statistics using SPSS - Nominal, Ordinal, and Interval levels of measurement
Views: 75998 Delton Daigle
Speaker: Daniel Borcard (University of Montreal, Canada) School on Recent Advances in Analysis of Multivariate Ecological Data: Theory and Practice | (smr 2835) 2016_10_26-11_40-smr2835
Paper: Multivariate Analysis Module name: Introduction toMultivariate Analysis Content Writer: Souvik Bandyopadhyay
Views: 68834 Vidya-mitra
A step-by-step introduction to MANOVA in SPSS is covered in this video (part 3). Video Transcript: now remember that for these two tests here we're using the alpha of .025 per test. We're not using alpha .05 per test, but instead we're using that Bonferroni adjusted level. So our first result, the empathic concern, this is a p of .015, and that is less than .025, so that result is significant. So this indicates that boys and girls differed on empathic concern. Looking at our next result, we see a p of .138, which is not less than .025, so this result is not significant. So, in other words, there was not a significant difference between boys and girls on perspective taking. So we want to summarize these results in just a minute in our written results, but before we do that, empathic concern was significant and recall earlier I had said if there was a significant result for ANOVA that we want to go ahead and look at the means so we can describe the differences. So we can look at our Estimated Marginal Means table if we'd like. We also saw the means earlier in the Descriptive Statistics table, right here. But let's go and look at the other table for practice as well. So down here at Marginal Means, empathic concern was significant, it had that p of .015 recall. So here we see that males and females, there's the two means. So which group had the higher mean? Females, right? So females had significantly higher or demonstrated significantly higher empathic concern than did males. But as far as perspective-taking was concerned, there was not a significant difference between males and females. So next we'll write those results up using APA format. OK now going back up to our Multivariate Test table, remember we're going to use Wilks' Lambda. And here notice first of all that Wilks' Lambda has a value of of .741, an F of 4.73, rounding, degrees of freedom of 2 and 27, a p-value of .017 as we saw before, and then partial eta-squared, rounding to two decimal places, of .26. So I'm going to use all of that information in the written results here. So the first sentence, I say there was a significant difference between males and females when considered jointly on the variables in empathic concern and perspective taking. And then here we have Wilks', and this is the Lambda, this symbol here, is equal to .741, and you saw that right here, and then F 2, 27. From here on out this part looks like a normal ANOVA. F 2 and 27, so here's the degrees of freedom, 2,27 equals 4.73, which you see right here rounded to two decimal places, and then finally p of .017, which is right there, and then partial eta-squared of .26. OK, so that first sentence shows the multivariate result, or the MANOVA. And then next we have a separate ANOVA was conducted for each dependent variable Lifetime access to SPSS videos: http://tinyurl.com/m2532td Channel Description: https://www.youtube.com/user/statisticsinstructor For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Videos series coming soon include: multiple regression in spss, factor analysis in spss, nonparametric tests in spss, multiple comparisons in spss, linear contrasts in spss, and many more. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor
Currell: Scientific Data Analysis. Minitab analyses for the data in Figs 3.11 and 3.15 http://ukcatalogue.oup.com/product/9780198712541.do © Oxford University Press
In this video tutorial you will learn the basics of MANOVA in R. MANOVA = Multivariate Analysis of Variance.
Views: 54147 Ed Boone
A step-by-step introduction to MANOVA is covered in this video (part 4). Video Transcript: with each ANOVA evaluated at an alpha level of .025. Recall that was the Bonferroni adjustment or correction. Now I'm going to go down to the ANOVA results here which was right here in this gender row. So we'll be consulting these values to write the results. So our first ANOVA, I talk about the significant result first. I say there was a significant difference between males and females on empathic concern and I have F of 1 and 28, that's right here 1 and then 28 for error. One and 28 is equal to 6.72, p is .015 and partial eta-squared of .19. So you can see all of that right here. And then I said with females and I gave a mean there, 35.27, scoring higher than males, with a mean of 27. So once again we got that down here. Here's the 35.267 for females, and then 27 for males. And then going to our last ANOVA result, let's go back up here for a minute, here we have this result, perspective-taking. I say there was not a significant difference between males and females on perspective-taking, F of 1 and 28 that's the 1 and then 28 for error, which is right there, equals 2.33, which you see right here under F. And then p is .138, partial eta-squared of .08, and that's right there, the partial eta-squared. So the written results in summary once again we have our first sentence, this is just one of many ways you could do it, but the first sentence shows the results of the MANOVA and then after that I talked about the ANOVA, first of all saying what each ANOVA was conducted at, what alpha level, and then I gave the significant result and I discussed the group means, so the reader would know which group was higher, and then I gave the non significant results after that. That's really about it for our two group MANOVA. Once again this was the most basic kind of problem you can get with MANOVA, where we have two groups and two dependent variables. And one last thing before we close here, partial eta-squared, we can think about this as being similar to eta-squared in the univariate case, in the ANOVA case. It's a little bit different in terms of how it's calculated behind the scenes, but basically there's really no effect size standard for small, medium, and large for partial eta-squared for MANOVA but, as always, bigger is better. So the bigger the partial eta-squared, the stronger the effect, or the more variance that the different groups accounted for on the dependent variables. This concludes our example on MANOVA with two groups and two dependent variables. Thanks for watching. Lifetime access to SPSS videos: http://tinyurl.com/m2532td Channel Description: https://www.youtube.com/user/statisticsinstructor For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Videos series coming soon include: multiple regression in spss, factor analysis in spss, nonparametric tests in spss, multiple comparisons in spss, linear contrasts in spss, and many more. Subscribe today! YouTube Channel: https://www.youtube.com/user/statisticsinstructor
A step-by-step introduction to MANOVA in SPSS is covered in this video (part 2). Lifetime access to SPSS videos: http://tinyurl.com/m2532td YouTube Channel: https://www.youtube.com/user/statisticsinstructor Subscribe today! Video Transcript: and here we have two things, intercept, this usually isn't of interest in those cases so we're just going to skip over that; but our next test is gender and that's what we want. So for gender we're going to look at the p-value here and notice first of all there's four different tests here Pillai's Trace, Wilks' Lambda, Hoteling's Trace and Roy's Largest Root. Now with two groups, as we have in this study, all four of these values are identical, they're the exact same. So notice the p-values, which is the most important thing, they're all the exact same. So when we have two groups all of these are the exact same. And some texts, although I'm seeing this less and less over time, some refer to this test as Hotelling's T-squared. In other words, the MANOVA when you have two groups Hotelling's T-squared. But as SPSS does, we're going to think of it just as MANOVA. So we'll go and report Wilk's Lambda, but just keep in mind once again, with two groups, all the results are the same. Recall that for the MANOVA we have an alpha of .05, and our p-value here for Wilks lambda is .017. So since this is less than .05, we reject that null hypothesis I showed you earlier about the two mean vectors being equal, and that indicates that the male and female adolescents differ when considered jointly on those two dependent variables. So in other words, boys and girls are significantly different from the MANOVA, that's what that indicates, the MANOVA results showed significance. So since this is significant, We're going to go ahead and go down and look at, in just a moment, our ANOVA results. But first let's briefly look at this Levene's test of the equality of error variances. And here notice there's two p-values, one for empathic concern, and one for perspective-taking. And now we're at the univariate level, so from this point on, Levene's test down, we're looking at each dependent variable separately. The multivariate tests table looked at the dependent variables together, or simultaneously. So after that table, now we're looking at the univariate, and that's where we have two p-values, one for each of our two variables. So here, just like the covariance matrices assumption, we want these values to be greater than .05 for Levene's test. And, although I said with the covariance matrices some use .001, here we're going to use .05 for Levene's test; it's a better test than Box's which was the earlier one. So notice .926 and .553, those are both greater than .05, so this provides some evidence that the equal variance assumption is satisfied on the univariate level. OK so next we'll look at the results of our ANOVA and here this Tests of Between-Subjects Effects table, it has a lot of effects here, and we're going to look specifically at gender. So for gender, we have empathic concern and cognitive perspective taking. And what these are these is these are just two separate ANOVAs, so we want to look at the p-values here.
For this video, I will give you the background theory and perform R demonstrations for one-way and factorial Multivariate Analysis of Variance (MANOVA). MANOVA is used for comparing mean vectors containing the means of multiple outcome variables between more group variables with more than 2 categories. It's the multivariate extension of the ANOVA. For more details of the 4 Statistics: https://onlinecourses.science.psu.edu/stat505/node/163 Calculate the F approximation of the 4 statistics: https://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_introreg_sect012.htm Like Me On FB: https://www.facebook.com/RenaissanceMonaLisa/?pnref=lhc
Views: 2975 RenaissanceWoman
This video discusses the n-way ANOVA option in R-Commander.
Views: 166 Neeraj Kaushik
http://thedoctoraljourney.com/store/ This is lesson 1 in a 12-lesson course titled, "Conducting a One-way MANOVA Using SPSS". Info on the One-Way MANOVA Course: From assumption testing to hypothesis testing, you will learn how to use SPSS outputs to make conclusions and write a results section. With over 90 minutes of hands-on, step-by-step video training, you’ll save hours (if not days or weeks) reading book after book trying to learn how to conduct a MANOVA. Here’s what you’ll learn in the course: Lesson 1: An Overview of the MANOVA and Research Problem Lesson 2: An Overview of the Assumptions for a MANOVA Lesson 3: Assumption Testing: Univariate Normality and Outliers Lesson 4: Assumption Testing: Multivariate Normality and Outliers Lesson 5: Assumption Testing: Linearity Lesson 6: Assumption Testing: Multicollinearity or Singularity Lesson 7: Assumption Testing: Homogeneity of Variance and Homogeneity of Co-Variance Lesson 8: Running the One-Way MANOVA in SPSS Lesson 9: Interpreting and Reporting SPSS Output: Descriptive Data Lesson 10: Interpreting and Reporting SPSS Output: The One-Way MANOVA Lesson 11: Interpreting and Reporting SPSS Output: Follow-Up ANOVAs Lesson 12: Writing a Statistical Results Section for a MANOVA Learn More About the Full One-Way MANOVA Course Here: http://thedoctoraljourney.com/store/
Views: 6515 The Doctoral Journey
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: 571181 J David Eisenberg
Manova merupakan uji statistik dengan jumlah variabel terikat lebih dari dua dan jumlah veriabel bebas lebih dari dua sehingga diperlukan SPSS dalam mengganalisis dan mengintrpretasikan
Views: 7624 Miftah Nur Solikh
In this video you will learn how to perform a One Way MANOVA procedure on SPSS. How to check for assumptions and interpret the outcome.
Views: 4607 educresem
Introduction to MANOVA by Jennifer Ripley, Ph.D., Regent University
Views: 2726 Jennifer Ripley
CEC/UGC: Social Science - 2, Education,Psychology, Home Science and related subjects managed by CEC,DELHI
Overview of the basic theory and application of multivariate analysis of variance.
The ANOVA video series is available for FREE as an iTune book for download on the iPad. The ISBN is 978-1-62847-067-3. The title is "t-Test and ANOVA". Waller and Lumadue are the authors. The iTune text provides accompanying narrative and the SPSS readouts used in the video series. The textbook can be obtained from: https://itunes.apple.com/us/book/t-test-anova/id657099412?ls=1 This video introduces comparative analysis where normality exists. The relationship between and among t-test, ANOVA, and MANOVA are examined.
Views: 5133 Lee Rusty Waller
This video illustrates how to perform and interpret a multiple regression statistical analysis in SPSS. Multiple Regression Regression R-Squared ANOVA table Regression Weight Beta Weight Predicted Value YouTube Channel (Quantitative Specialists): https://www.youtube.com/user/statisticsinstructor Subscribe today! Inferential course: https://www.udemy.com/inferential-statistics-spss Descriptives course: https://www.udemy.com/descriptive-statistics-spss Questionnaire/Survey & Likert Course: https://www.udemy.com/survey-data ANOVA course: https://www.udemy.com/anova-spss MANOVA course: https://www.udemy.com/manova-spss Video Transcript: In this video, we'll take a look at how to run a multiple regression in SPSS. And on your screen as an example we have four variables SAT score, social support, gender, and college GPA. And in this example we're using the first three variables SAT score, social support, and gender, to predict first year college GPA. And here SAT score was taken in high school, social support is a measure of how much support a student felt that they received from others, where higher scores indicate greater support, and that was taken in the first year in college, and then gender, our dichotomous variable, where 1 is male and 2 is female, and the variable, college GPA, was the GPA after the first year in college. And in regression what we're trying to predict in this case, college GPA, is known as our criterion variable. It's also known as the dependent variable (DV). And then the variables that we're using to predict the criterion variable, SAT score, social support, and gender, those are known as are predictors or predictor variables, and we also refer to those as independent variables (IV). And those once again are SAT score, social support, and gender. Now in multiple regression you always have one criterion or dependent variable, and for it to be multiple regression you have to have two or more predictors or independent variables. if you just had one predictor or independent variable, such as SAT score, then that would be simple regression. But since we have two or more, in this case we have three once again, we're doing multiple regression. OK so to run multiple regression SPSS we want to go to Analyze, and then Regression and then go ahead and select Linear. And here we want to move college GPA to our Dependent box and then we want to select all the predictors and move those to our Independent(s) box. And then go ahead and click OK. And our output opens here and the first table, Variables Entered/Removed, this confirms that we had the variables gender, SAT score, and social support as our predictors, and then our dependent variable, or criterion variable, was college GPA, so that looks good. OK our next two tables, Model Summary and ANOVA, these two tables, they're looking at whether are predictors, once again, SAT score, social support, and gender, when those are taken together as a set or as a group, do they predict college GPA. And the Model Summary and ANOVA table are getting that slightly different things, but they're very closely related. So let's go ahead and start with Model Summary and take a look at that. So for Model Summary in this video we're going to focus on R square and then in another video we'll talk about these measures in more detail. But for this general overview the most commonly reported value in the Model Summary table is the R square value. And R squared, if I round this to two decimal places and then convert it to a percentage, so this would round two .50 or 50%, I could interpret R squared as follows. R squared once again is equal to .50 and then taken as a set the predictors SAT score, social support, and gender, account for 50% of the variance in college GPA. OK so R squared is a measure of the amount of variance in the dependent variable that the independent variables or predictors account for when taken as a group. And that's very important, it doesn't measure how much a given individual predictor accounts for, but only when we take them all as a group, this Model Summary table says overall, the regression model, which is what is referred to sometimes as a model, these three predictors predicting college GPA, that overall model accounts for 50% of the variance. Which is pretty good in practice. OK next we have our ANOVA table
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. Like MyBookSucks on Facebook! http://www.facebook.com/PartyMoreStudyLess PlayList on ANOVA http://www.youtube.com/course?list=EC3A0F3CC5D48431B3 PlayList On TWO ANOVA http://www.youtube.com/playlist?list=PLWtoq-EhUJe2TjJYfZUQtuq7a0dQCnOWp Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 824801 statisticsfun
This video demonstrates how distinguish and evaluate main and interaction effects in a two-way ANOVA using SPSS. A main effect represents the effect of one independent variable on a dependent variable and an interaction effect represents the effect of multiple independent variables simultaneously.
Views: 50282 Dr. Todd Grande
Subject:Human Resource Management Paper: Research Methodology
Views: 5055 Vidya-mitra
This video demonstrates how to conduct and interpret a One-Way MANCOVA in SPSS. Assumptions for MANCOVA are tested, including homogeneity of variance-covariance and homogeneity of regression slopes.
Views: 26084 Dr. Todd Grande
Views: 84 James Pulido
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: 46959 OriginLab Corp.
For this seminar, I will take you through a general introduction of multivariate analysis and perform an R demonstration of a simple multivariate analysis: mean comparison.
Views: 4376 RenaissanceWoman
This video is an introduction to the one-way multivariate analysis of covariance (one-way MANCOVA) including a description of how it is used, its elements, and the assumptions data must meet to be analyzed by the test. A MANCOVA has one or more covariates. The assumptions of multivariate normality, homogeneity of variance-covariance matrices, and linearity are reviewed.
Views: 1897 Dr. Todd Grande
This video is an introduction to the two-way multivariate analysis of covariance (two-way MANCOVA) including a description of how it is used, its elements, and the assumptions data must meet to be analyzed by the test. A MANCOVA has one or more covariates. The assumptions of multivariate normality, homogeneity of variance-covariance matrices, and linearity are reviewed.
Views: 2138 Dr. Todd Grande
The MANOVA series is available as an iTune book for FREE. This may be accessed at the following URL. https://itunes.apple.com/us/book/manova/id657187300?ls=1 This video explains the protocol for conducting MANOVA.
Views: 2982 Lee Rusty Waller
Conducting Multivariate Analysis of Variance (MANOVA) in Education by Peter A. Okebukola -LASU STERG
Views: 126 John Cranmer
Subject: Computer Science Paper: Data Analytics
Views: 4678 Vidya-mitra
kobriendublin.wordpress.com/spss Questions Compute the significance value of Wilk's Lambda. Interpret this value. For each of the three dependent variables, state the significance value for the test of between-subject effects and interpret these value.
Views: 19602 Dragonfly Statistics