This video is about Interpretting the ANOVA Results Table

Views: 116558
Statistical Services Centre

Views: 11110
AK Hartley

Before engaging any regression analysis, it is essential to have a feel of your data. That is, what are the distinctive features of each variable that make up your sample data? What information do they convey and how can you interpret them? This hands-on tutorial teaches how to run descriptive analysis in EViews10 and interpret the outcome.
Here is the link to the dar.xlsx dataset used for this tutorial (endeavour to have a Google account for easy accessibility): https://drive.google.com/drive/u/1/folders/1Ke7SVDfieGy-JRJ4Jzg79q-0h6W28EAj
Follow up with soft-notes and updates from CrunchEconometrix:
Website: http://cruncheconometrix.com.ng
Blog: https://cruncheconometrix.blogspot.com.ng/
Forum: http://cruncheconometrix.com.ng/blog/forum/
Facebook: https://www.facebook.com/CrunchEconometrix
YouTube Custom URL: https://www.youtube.com/c/CrunchEconometrix
Stata Videos Playlist: https://www.youtube.com/watch?v=sTpeY31zcZs&list=PL92YnqQQ1gbjyoGWR2VUemNPU93yivXZx
EViews Videos Playlist: https://www.youtube.com/watch?v=znObTs4aJA0&list=PL92YnqQQ1gbghRSJURtz08AZdImbge4h-

Views: 15715
CrunchEconometrix

A brief explanation of the output of regression analysis. For more information visit www.calgarybusinessblog.com

Views: 485983
Matt Kermode

All videos here: http://www.zstatistics.com/
It's a long one, but feel free to use the hyperlinks below to skip to the bit of particular interest.
Intro 0:00
Dataset described 1:07
Quick Recap (feel free to skip) 2:43
ANOVA SECTION 10:05
SS - sum of squares 11:08
R-squared 12:29
df - degrees of freedom 13:43
MS - mean square 14:20
F-test 14:36
p-value 16:04
SER or Root MSE 16:32
VARIABLES SECTION 19:19
Coefficients 20:51
Standard error 24:33
t-statistic 25:16
p-value 26:14
95% Confidence interval 32:00

Views: 139768
zedstatistics

In this video, I go over how to interpret the results of a meta-analysis.

Views: 50328
Tara Bishop MD

Views: 127941
William Divale

SKIP AHEAD:
0:39 – Null Hypothesis Definition
1:42 – Alternative Hypothesis Definition
3:12 – Type 1 Error (Type I Error)
4:16 – Type 2 Error (Type II Error)
4:43 – Power and beta
6:33 – p-Value
8:39 – Alpha and statistical significance
14:15 – Statistical hypothesis testing (t-test, ANOVA & Chi Squared)
For the text of this video click here http://www.stomponstep1.com/p-value-null-hypothesis-type-1-error-statistical-significance/
For my video on Confidence Intervals click here http://www.stomponstep1.com/confidence-interval-interpretation-95-confidence-interval-90-99/

Views: 492375
Stomp On Step 1

CORRECTION: Although my mistake is beyond the scope of the Step 1 exam, the interpretation of Confidence Interval that I used in the video is incorrect & a bit oversimplified. I stated that for an individual study there is a 95% chance that the true value lies within the 95% CI. However, confidence interval is a type of frequentist inference and the interpretation I gave in the video is really better suited for interpreting statistics of Bayesian Inference (Again please don’t feel like you need to know these terms for the exam). What I should have said is something like “if 100 similarly designed studies use a 95% confidence interval then 95 of these intervals will contain the true value and 5 will not. For more info on this misconception click here https://en.wikipedia.org/wiki/Bayesian_inference
A Confidence Interval (CI) is the range of values the true value in the population is expected to fall within based on the study results. The results we receive in any study do not perfectly mirror the overall population and the confidence interval lets us get a better idea of what the results in the overall population might be. The confidence interval is based on a certain level of confidence. Don't get this confused with the value of the sample population. If the measured BMI in 100 people in your study population and the mean is 25 than you are very confident that the actual mean BMI in that group is 25. Confidence interval only comes into play when you try to extrapolate your study results to other situations (like to the population overall).
If you have a 95% confidence interval (which is most common) that means there is a 95% chance that the true value lies somewhere in the confidence interval. You can also alter the width of the confidence interval by selecting a different percentage of confidence. 90% & 99% are also commonly used. A 99% confidence interval is wider (has more values) than a 95% confidence interval & 90% confidence interval is the most narrow.
The width of the CI changes with changes in sample size. The width of the confidence interval is larger with small sample sizes. You don't have enough data to get a clear picture of what is going on so your range of possible values is wider. Imagine your study on a group of 10 individuals shows an average shoe size of 9. If based on the results you are 95% sure that the actual average shoe size for the entire population is somewhere in between 6 and 12, then the 95% CI is 6-12. Based just on your results you don't really know what the average in the population is, because your study population is a very small sliver of the overall population. Now if you repeat the study with 10,000 individuals and you get an average shoe size of 9 the confidence interval is going to be smaller (something like 8.8 to 9.3). Here you have a much larger sample size and therefore your results give you a much clearer idea of what is going on with the entire population. Therefore, your 95% CI shrinks. The width of the confidence interval decreases with an increasing sample size (n). This is sort of like the standard deviation decreasing with an increased sample size.
Confidence intervals are often applied to RR & OR. For example, the odds ratio might be 1.2, but you aren't sure how much of an impact chance had on determining that value. Therefore, instead of just reporting the value of 1.2 you also report a range of values where the true value in the population is likely to lie. So we would report something like the odds ratio is 1.2 and we are 95% confident that the true value within the overall population is somewhere between .9 and 1.5.
You can use the confidence interval to determine statistical significance similar to how you use the p-Value. If the 95% confidence interval crosses the line of no difference that is the same things as saying there is a p-value of greater than 5%. This is intuitive because if the confidence interval includes the value of no difference then there is a reasonable chance that there is no difference between the groups. If the confidence interval does not cross the line of no difference than the observed difference is statistically significant, because you know it is highly unlikely that the two groups are the same.
For both relative risk (RR) and odds ratio (OR), the "line of no difference" is 1. So an RR or OR of 1 means there is no difference between the two groups being compared with respect to what you are measuring. This is because RR and OR are ratios and a value divided by itself is 1. If the 95% confidence interval of the RR or OR includes the value 1, that means it is possible the true value is 1 and there is no difference between groups. If that is the case, we say the null hypothesis cannot be rejected or that there is no statistically significant difference shown. This is the same thing as saying the p-value is greater than .05.

Views: 223359
Stomp On Step 1

The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends.
The steps are also described in writing below (Click Show more):
STEP 1, reading the transcripts
1.1. Browse through all transcripts, as a whole.
1.2. Make notes about your impressions.
1.3. Read the transcripts again, one by one.
1.4. Read very carefully, line by line.
STEP 2, labeling relevant pieces
2.1. Label relevant words, phrases, sentences, or sections.
2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant.
2.3. You might decide that something is relevant to code because:
*it is repeated in several places;
*the interviewee explicitly states that it is important;
*you have read about something similar in reports, e.g. scientific articles;
*it reminds you of a theory or a concept;
*or for some other reason that you think is relevant.
You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you.
It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds.
STEP 3, decide which codes are the most important, and create categories by bringing several codes together
3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand.
3.2. You can create new codes by combining two or more codes.
3.3. You do not have to use all the codes that you created in the previous step.
3.4. In fact, many of these initial codes can now be dropped.
3.5. Keep the codes that you think are important and group them together in the way you want.
3.6. Create categories. (You can call them themes if you want.)
3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever.
3.8. Be unbiased, creative and open-minded.
3.9. Your work now, compared to the previous steps, is on a more general, abstract level. You are conceptualizing your data.
STEP 4, label categories and decide which are the most relevant and how they are connected to each other
4.1. Label the categories. Here are some examples:
Adaptation (Category)
Updating rulebook (sub-category)
Changing schedule (sub-category)
New routines (sub-category)
Seeking information (Category)
Talking to colleagues (sub-category)
Reading journals (sub-category)
Attending meetings (sub-category)
Problem solving (Category)
Locate and fix problems fast (sub-category)
Quick alarm systems (sub-category)
4.2. Describe the connections between them.
4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study.
STEP 5, some options
5.1. Decide if there is a hierarchy among the categories.
5.2. Decide if one category is more important than the other.
5.3. Draw a figure to summarize your results.
STEP 6, write up your results
6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results.
6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example:
*results from similar, previous studies published in relevant scientific journals;
*theories or concepts from your field;
*other relevant aspects.
STEP 7 Ending remark
Nb: it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.)
Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze:
*notes from participatory observations;
*documents;
*web pages;
*or other types of qualitative data.
STEP 8 Suggested reading
Alan Bryman's book: 'Social Research Methods' published by Oxford University Press.
Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE.
Text and video (including audio) © Kent Löfgren, Sweden

Views: 785049
Kent Löfgren

This video explains how to compare the mean and standard deviation of two groups of data.
http://mathispower4u.com

Views: 47385
Mathispower4u

This video uses Anderson 11e Chapter 15 #4 to walk through regression output and explain how to interpret it.

Views: 249885
Jason Delaney

This video is meant to be used as an introductory lesson to Mini Research Writing focusing on Data Analysis and Discussion.
As this is a mini class project, some of the requirements have been made simple due to time constraints. Plus, the focus of this mini research paper is to get students familiarized to the ways of writing an academic paper and the items that needs to be included.
suitable for beginners!

Views: 30220
NurLiyana Isa

Relative and absolute methods of qPCR analysis. Created for an assignment for BIOC3001: Molecular Biology at the University of Western Australia.
****SCRIPT**** [I know it's a bit fast]
qPCR or quantitative real-time PCR…
….is simply classic PCR monitored using fluorescent dyes or probes.
qPCR is accurate, reliable and extremely sensitive, it can even detect a SINGLE copy of a specific transcript.
qPCR is commonly coupled to reverse transcription to measure gene expression.
No wonder it is so important for molecular diagnostics, life sciences, agriculture, and medicine.
Firstly, let's go over the NUTS and BOLTS of qPCR. For this you use a fluorescent dye which binds to the DNA. As qPCR progresses, the fluorescent signal increases.
Ideally the signal should double with every cycle, which is then plotted.
Because there are few template strands to start with, initially there’s a faint signal.
Eventually, usually after 15 cycles, the signal rises above the background noise and can be detected. We call this the THRESHOLD CYCLE, Ct, the point from which all quantitative data analysis begins.
But how do you analyse qPCR data?
You can either use an absolute quantification method, with a standard curve, OR a relative method, using one or more reference genes to standardize and compare the differences in Ct values between two treatments.
The absolute standard curve method determines ORIGINAL DNA concentration by comparing the Ct value of the sample of interest with a standard curve.
To create the standard curve, you need to make DNA samples of different KNOWN concentrations.
After doing PCR on these, you will see different PCR plots for each standard …..
and unsurprisingly they have different Ct values. The GREATER the concentration of the original DNA sample, the SMALLER the Ct value.
So if you plot ORIGINAL DNA concentration against the Ct values. You will have a standard curve like this…..
Now let’s say the PCR plot of your unknown DNA sample is somewhere here…..
...which corresponds to this Ct value on the standard curve here….
Using the standard curve you can figure out the log concentration of your DNA sample to be x.
As this is in log scale, you can simply calculate your sample DNA concentration to be 10 to the power of x.
Absolute analysis is suitable when you want to determine the ACTUAL transcript copy number, that is the level of gene expression.
On the other hand, Relative quantification is used when you want to COMPARE the difference in gene expression BETWEEN two treatments, for example light or dark treated Arabadopsis thaliana.
This is done using one or more reference genes, such as actin, which are expressed at the SAME level for both treatments.
You then perform qPCR on both your samples and the reference genes, find out the DIFFERENCE between the two Cts values, delta Ct, in EACH treatment.
Now the RATIO of the two delta Cts …[pause a bit] . tells you how much gene expression has changed.
For instance, in the dark treatment, the Ct value of your reference gene is at THIS level, the Ct value of your target gene is THIS Level. So you have this delta Ct which is the difference in Cts in the first treatment.
in the dark treatment, the Ct value of your reference gene is STILL at THIS level, but the Ct value of your target gene may become only this much.
So the ratio of the two Ct values is..
delta Ct(dark treatment) divided by delta Ct(light treament) equals one third
….showing the delta Ct has DECREASED by a factor of 3, which means that gene expression of the target gene is GREATER in the dark treated sample.
This is how relative quantification using a reference gene helps detect change in the expression of your target gene.
In conclusion, there are two ways to quantify transcripts using qPCR: absolute quantification using a standard curve, and relative quantification using a reference gene.
The method used depends on whether you want to determine the ACTUAL number of transcripts or the RELATIVE change in gene expression.

Views: 207436
TARDIStennant

Video provides overview of ways of obtaining and interpreting descriptive statistics for variables with different scales of measurement.

Views: 21843
Mike Crowson

This video will discuss how to interpret the information contained in a typical forest plot.

Views: 178337
Terry Shaneyfelt

Tutorial for using SPSS 16 to run descriptive statistics for categorical and continuous variables, a 2-way contingency table for categorical variables, and chi-squared analysis, and a correlation analysis for 2 continuous variables.
These videos are not intended to teach you how to calculate, comprehend, or interpret statistics. These videos are merely a tool to introduce you to some basic SPSS procedures.
Download the sample data at the KSU Psych Lab web page:
http://www.kennesaw.edu/psychology/videos/lab/sample_data.xlsx
Subtitles available: click on the CC button toward the bottom right of the video.
Menu available for jumping to chapters in the flash video posted on the KSU Psych Lab website:
http://psychology.hss.kennesaw.edu/resources/psychlab/

Views: 151960
Terry Jorgensen

This video is a short summary of interpreting regression output from Stata. Specifically the p-value for the F-test, the R squared, the p-values for t-tests and the coefficients of the model are outlined.
The data for the video can be downloaded from http://www.justindoran.ie/uploads/6/9/6/0/6960312/ireland_solow_growth_model_example.dta
The do file used in the analysis can be downloaded from http://www.justindoran.ie/uploads/6/9/6/0/6960312/ireland_solow_example_do_file.do

Views: 131401
Justin Doran

This video is for students who have had some exposure to regression methods, but need a refresher on how to interpret regression tables.

Views: 48523
Doug McKee

Views: 17870
BYU-Hawaii Learning Channel

Science and Engineering Practice 3:
Analyzing and Interpreting Data
Paul Andersen explains how scientists analyze and interpret data. Data can be organized in a table and displayed using a graph. Students should learn how to present and evaluate data.
Intro Music Atribution
Title: I4dsong_loop_main.wav
Artist: CosmicD
Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/
Creative Commons Atribution License

Views: 66549
Bozeman Science

Session 18: Descriptive Statistics: Summarising and Visualising Data
Fifth Video

Views: 59707
Anthony Kuster

Interpret the SPSS output for an independent two-sample t-test.
ASK SPSS Tutorial Series

Views: 185325
BrunelASK

This video provides an example of interpreting multiple regression output in excel. The data set comes from Andy Field's "Discovering Statistics Using SPSS" (2009, 3rd Edition).

Views: 337227
TheWoundedDoctor

In this video Dr. Ziene Mottiar, DIT, discusses issues around analyzing data and writing the analysing chapter. The difference between Findings and Analysis chapters is also discussed. This video is useful for anyone who is writing a dissertation or thesis.

Views: 69340
ZieneMottiar

In this Bio-Rad Laboratories Real Time Quantitative PCR tutorial (part 1 of 2), you will learn how to analyze your data using both absolute and relative quantitative methods. The tutorial also includes a great explanation of the differences between Livak, delta CT and the Pfaffl methods of analyzing your results. For more videos visit http://www.americanbiotechnologist.com

Views: 358795
americanbiotech

With Spanish subtitles. This video explains how to use the p-value to draw conclusions from statistical output. It includes the story of Helen, making sure that the choconutties she sells have sufficient peanuts.
You might like to read my blog:
http://learnandteachstatistics.wordpress.com

Views: 863242
Dr Nic's Maths and Stats

Here, we demonstrate the first steps of data analysis for some typical ELISA data. The procedure shown is as follows: 1) Compare the arrangement of our samples on the 96-well plate with the absorbance measurements obtained from the plate reader. 2) Subtract out the blank measurements from all the entire plate. 3) Arrange the absorbance measurements of the standards next to their corresponding known concentrations. 4) Place the absorbance measurements of the samples below the absorbance measurements of the standards.
In the next video of this series, we will demonstrate how to interpolate the concentration of our samples using software called GraphPad Prism. Click here for a free 30-day trial of GraphPad Prism - http://protocol-place.com/Prism
This video and other protocols can be found at our website, the "Protocol Place" - http://protocol-place.com/
A full ELISA protocol can be found at http://protocol-place.com/assays/sandwich-elisa-protocol/
This video is a part of our ELISA Tutorial Series. Here are all of the videos in this series:
ELISA Tutorial 1: Understand How an ELISA Works - http://youtu.be/nNjlBCnpGZ4
ELISA Tutorial 2: Coating and Blocking the ELISA Plate - http://youtu.be/AmG7FBolfdc
ELISA Tutorial 3: Preparing and Adding Samples to the ELISA Plate - http://youtu.be/darrx6F0wsg
ELISA Tutorial 4: Finishing the Assay (Sandwich ELISA) - http://youtu.be/zI4khIJhCd8
ELISA Tutorial 5: Preparing ELISA Data in Excel for Analysis with GraphPad Prism - http://youtu.be/l9tO81ZCeRg
ELISA Tutorial 6: How to Analyze ELISA Data with GraphPad Prism - http://youtu.be/5IqqpKSnXfI
Competitive ELISA Tutorial 1: How a Competitive ELISA Works - http://youtu.be/Kb26nQVMHds
Competitive ELISA Tutorial 2: How to Use Calbiotech's Competitive ELISA Kits - http://youtu.be/3E_U_4Z2dUc
Competitive ELISA Tutorial 3: Analyzing Typical ELISA Data in Excel - http://youtu.be/s2t0jiWxiDI
We hope you enjoy watching and benefit from our tutorials. If so, please take a minute to "like," or better yet, share them with others!
Thanks for watching!
Youssef Farhat
Protocol Place: http://protocol-place.com
***Check out some of our other tutorials via the links below***
Competitive ELISA Tutorials - - - http://www.youtube.com/watch?v=Kb26nQVMHds&list=PLR4wfoQ4HbymisBtft-i-QTsyRDMFymC3
ELISA Tutorials - - - http://www.youtube.com/watch?v=nNjlBCnpGZ4&list=PLR4wfoQ4HbynbS01zeuBV-awsOAxDPhYO
Gelatin Zymography Tutorials - - - http://www.youtube.com/watch?v=MF2sWQSaBWg&list=PLR4wfoQ4Hbykrj7rxk6i5jzkjtvTtZUVx

Views: 54695
protocolplace

How to Interpret the Results of A Two Way ANOVA (Factorial) also known as Factorial Analysis. Step by step visual instructions on how to calculate degrees of freedom, mean square, F score (F ratio). Includes instructions on how to read and understand the F distribution table. How to determine to reject or fail to reject the null hypothesis.
Playlist on Two Way ANOVA
http://www.youtube.com/playlist?list=PLWtoq-EhUJe2TjJYfZUQtuq7a0dQCnOWp
Playlist on One Way ANOVA
http://www.youtube.com/course?list=EC3A0F3CC5D48431B3
Like MyBookSucks On FaceBook
http://www.FaceBook.Com/PartyMoreStudyLess
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet

Views: 200412
statisticsfun

Views: 80674
David Russell

How to conduct simple linear regressions using SPSS/PASW.

Views: 127529
bernstmj

Learn how to interpret the tables created in SPSS Output when you run a linear regression & write the results in APA Style.

Views: 27070
Kelsey Hall

Views: 85223
Ross Avilla

This video explains the purpose of t-tests, how they work, and how to interpret the results.
For a simple explanation of Chi-Squares, visit: https://www.youtube.com/watch?v=ZjdBM7NO7bY

Views: 737295
StatsCast

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

Views: 80994
Quantitative Specialists

This video is part of the University of Southampton, Southampton Education School, Digital Media Resources
http://www.southampton.ac.uk/education
http://www.southampton.ac.uk/~sesvideo/

Views: 205210
Southampton Education School

This short tutorial explains how to produce and interpret basic descriptive statistics of sample data in SPSS.
A helpful discussion on skewness statistics: https://brownmath.com/stat/shape.htm
A helpful discussion skewness and kurtosis:
http://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm

Views: 6600
ASU SAM Lab

Excel file: https://dl.dropboxusercontent.com/u/561402/TTEST.xls
In this video Paul Andersen explains how to run the student's t-test on a set of data. He starts by explaining conceptually how a t-value can be used to determine the statistical difference between two samples. He then shows you how to use a t-test to test the null hypothesis. He finally gives you a separate data set that can be used to practice running the test.
Do you speak another language? Help me translate my videos:
http://www.bozemanscience.com/translations/
Music Attribution
Intro
Title: I4dsong_loop_main.wav
Artist: CosmicD
Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/
Creative Commons Atribution License
Outro
Title: String Theory
Artist: Herman Jolly
http://sunsetvalley.bandcamp.com/track/string-theory
All of the images are licensed under creative commons and public domain licensing:
1.3.6.7.2. Critical Values of the Student’s-t Distribution. (n.d.). Retrieved April 12, 2016, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm
File:Hordeum-barley.jpg - Wikimedia Commons. (n.d.). Retrieved April 11, 2016, from https://commons.wikimedia.org/wiki/File:Hordeum-barley.jpg
Keinänen, S. (2005). English: Guinness for strenght. Retrieved from https://commons.wikimedia.org/wiki/File:Guinness.jpg
Kirton, L. (2007). English: Footpath through barley field. A well defined and well used footpath through the fields at Nuthall. Retrieved from https://commons.wikimedia.org/wiki/File:Footpath_through_barley_field_-_geograph.org.uk_-_451384.jpg
pl.wikipedia, U. W. on. ([object HTMLTableCellElement]). English: William Sealy Gosset, known as “Student”, British statistician. Picture taken in 1908. Retrieved from https://commons.wikimedia.org/wiki/File:William_Sealy_Gosset.jpg
The T-Test. (n.d.). Retrieved April 12, 2016, from http://www.socialresearchmethods.net/kb/stat_t.php

Views: 563804
Bozeman Science

This video describes the procedure of tabulating and analyzing the likert scale survey data using Microsoft Excel. This video also explains how to prepare graph from the tabulated data.
Photo courtesy: http://littlevisuals.co/

Views: 136233
Edifo

This A Level / IB Psychology revision video for Research Methods looks at interpreting inferential statistics.

Views: 26397
tutor2u

How to conduct an analysis of frequencies and descriptive statistics using SPSS/PASW.

Views: 309083
bernstmj

Likert Scale: http://en.wikipedia.org/wiki/Likert_scale
R: http://www.r-project.org/

Views: 225023
Alan Cann

Use simple data analysis techniques in SPSS to analyze survey questions.

Views: 868962
Claus Ebster

This video's more focused on the concept. This one explains how it's calculated: https://www.youtube.com/watch?v=WVx3MYd-Q9w
Video transcript:
"Have we discovered a new particle in physics?
Is a manufacturing process out of control?
What percentage of men are taller than Lebron James? How about taller than Yao Ming?
All of these questions can be answered using the concept of standard deviation.
For any set of data, the mean and standard deviation can be calculated. For example, five people may have the following amounts of money in their wallets: 21, 50, 62, 85, and 90. The mean is $61.60 and the standard deviation is $28.01.
How much does the data vary from the average? Standard deviation is a measure of spread, that is, how spread out a set of data is.
A low standard deviation tells us that the data is closely clustered around the mean (or average), while a high standard deviation indicates that the data is dispersed over a wider range of values.
It is used when the distribution of data is approximately normal, resembling a bell curve.
Standard deviation is commonly used to understand whether a specific data point is “standard” and expected or unusual and unexpected. Standard deviation is represented by the lowercase greek letter sigma. A data point’s distance from the mean can be measured by the number of standard deviations that it is above or below the mean. A data point that is beyond a certain number of standard deviations from the mean represents an outcome that is significantly above or below the average. This can be used to determine whether a result is statistically significant or part of expected variation, such as whether a bottle with an extra ounce of soda is to be expected or warrants further investigation into the production line.
The 68-95-99.7 rule tells us that about 68% of the data fall within one standard deviation of the mean. About 95% of data fall within two standard deviations of the mean. And about 99.7% of data fall within 3 standard deviations of the mean.
The average height of an American adult male is 5’10, with a standard deviation of 3 inches. Using the 68-95-99.7 rule, this means that 68% of American men are 5’10 plus or minus 3 inches, 95% of American men are 5’10 plus or minus 6 inches, and 99.7% of American men are 5’10 plus or minus 9 inches. So, this means only about .3% of American men deviate more than 9 inches from the average, with .15% taller than 6’7 and .15% shorter than 5’1. This reasoning suggests that Lebron James is 1 in 2500 and Yao Ming is 1 in 450 million.
In particle physics, scientists have what are called 5-sigma results, results that are five standard deviations above or below the mean. A result that varies this much can signify a discovery as it has only a 1 in 3.5 million chance that it is due to random fluctuation.
In summary, standard deviation is a measure of spread. Along with the mean, the standard deviation allows us to determine whether a value is statistically significant or part of expected variation."

Views: 1018162
Jeremy Jones

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: 703835
how2stats

Analyze data using variance and standard deviation in Excel. Watch more Excel tutorials at http://www.lynda.com/Excel-tutorials/Analyzing-data-using-variance-standard-deviation/196583/375050-4.html?utm_campaign=XddpZQyaaqU&utm_medium=social&utm_source=youtube-earned
This tutorial is from the Data-Analysis Fundamentals with Excel course presented by lynda.com author Curt Frye. The complete course provides an overview of the fundamentals, from performing common calculations to conducting Bayesian analysis with Excel.
Connect with lynda.com:
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Views: 19882
LinkedIn Learning