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

Views: 41085
Tara Bishop MD

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: 714491
Dr Nic's Maths and Stats

This statistical analysis overview explains descriptive and inferential statistics. Watch more at http://www.lynda.com/Excel-2007-tutorials/business-statistics/71213-2.html?utm_medium=viral&utm_source=youtube&utm_campaign=videoupload-71213-0101
This specific tutorial is just a single movie from chapter one of the Excel 2007: Business Statistics course presented by lynda.com author Curt Frye. The complete Excel 2007: Business Statistics course has a total duration of 4 hours and 19 minutes and covers formulas and functions for calculating averages and standard deviations, charts and graphs for summarizing data, and the Analysis ToolPak add-in for even greater insights into data
Excel 2007: Business Statistics table of contents:
Introduction
1. Introducing Statistics
2. Learning Useful Excel Techniques
3. Summarizing Data Using Tables and Graphics
4. Describing Data Using Numerical Methods
5. Using Probability Distributions
6. Sampling Values from a Population
7. Testing Hypotheses
8. Using Linear and Multiple Regression
Conclusion

Views: 101497
LinkedIn Learning

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

Views: 215721
Jason Delaney

This video demonstrates how to interpret multiple regression output in SPSS. This example includes two predictor variables and one outcome variable. Unstandardized and standardized coefficients are reviewed.

Views: 108831
Dr. Todd Grande

Seven different statistical tests and a process by which you can decide which to use.
The tests are:
Test for a mean,
test for a proportion,
difference of proportions,
difference of two means - independent samples,
difference of two means - paired,
chi-squared test for independence and
regression.
This video draws together videos about Helen, her brother, Luke and the choconutties.
There is a sequel to give more practice choosing and illustrations of the different types of test with hypotheses.

Views: 680668
Dr Nic's Maths and Stats

Views: 68905
David Russell

How to run a chi-square test and interpret the output in SPSS (v20).
ASK SPSS Tutorial Series

Views: 816069
BrunelASK

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: 359186
Stomp On Step 1

For more, visit http://www.statscast.net
This video explains the purpose of t-tests, how they work, and how to interpret the results.

Views: 656060
StatsCast

Support and hit like and/or subscribe =).
This video explains the concept of the chromatogram. Don't focus on the numbers as the numbers are fiction. It's all about the basic principle of the chromatogram.
Learn the basic concept of High Pressure Liquid Chromatography
01. Introduction
https://youtu.be/IUwRWn9pEdg
02. The Mobile Phase
https://youtu.be/pmHtGDdagJU
03. The Stationary Phase
https://youtu.be/MYSBOxbnuAw
04. Normal Phase HPLC vs Reverse Phase HPLC
https://youtu.be/MLoitPJQH3g
05. HPLC Isocratic vs Gradient analysis
http://youtu.be/tAcfJPveWwM
06. HPLC - UV-VIS detection of analytes
https://youtu.be/sfxEj_MxBcs
07. HPLC - How to read a chromatogram?
https://youtu.be/qXmSb6Xwr5k
08. What is the difference between HPLC and GC?
https://www.youtube.com/watch?v=FlTf2BRtR2s

Views: 161225
MrSimpleScience

user interface design chapter 8

Views: 191
Android Code

Multiple Linear Regression Analysis, Evaluating Estimated Linear Regression Function (Looking at a single Independent Variable), basic approach to test relationships, (1) 𝐑^𝟐 Correlation between X (Independent Variable) & Y (Dependent Variable), F-Test, (2) Regression Analysis: If there is a significant relationship between X (Independent Variable) & Y (Dependent Variable), T-Test, (3) Explaining how to calculate the Degrees Of Freedom for the F-Test & T-Test, detailed discussion comparing two different regression equations to see which best predicts the dependent variable by Allen Mursau

Views: 185164
Allen Mursau

Interpretation of the coefficients on the predictors in multiple linear regression made easy.

Views: 370543
Phil Chan

This video covers basic information on how to interpret information when looking at a graph.

Views: 49588
Jaime Jackson

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 10:25
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: 99266
zedstatistics

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: 64357
ZieneMottiar

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: 186376
Southampton Education School

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;
*it surprises you;
*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.
3.10. 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
This tutorial showed how to focus on segments in the transcripts and how to put codes together and create categories. However, it is important to remember that 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.
Good luck with your study.
Text and video (including audio) © Kent Löfgren, Sweden

Views: 668094
Kent Löfgren

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

Views: 247726
bernstmj

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: 392581
Bozeman Science

What is TREND ANALYSIS? What does TREND ANALYSIS mean? TREND ANALYSIS meaning - TREND ANALYSIS definition - TREND ANALYSIS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ
Trend analysis is the rampant practice of collecting information and attempting to spot a pattern. In some fields of study, the term "trend analysis" has more formally defined meanings.
Although trend analysis is often used to predict future events, it could be used to estimate uncertain events in the past, such as how many ancient kings probably ruled between two dates, based on data such as the average years which other known kings reigned.
In project management, trend analysis is a mathematical technique that uses historical results to predict future outcome. This is achieved by tracking variances in cost and schedule performance. In this context, it is a project management quality control tool.
In statistics, trend analysis often refers to techniques for extracting an underlying pattern of behavior in a time series which would otherwise be partly or nearly completely hidden by noise. If the trend can be assumed to be linear, trend analysis can be undertaken within a formal regression analysis, as described in Trend estimation. If the trends have other shapes than linear, trend testing can be done by non-parametric methods, e.g. Mann-Kendall test, which is a version of Kendall rank correlation coefficient. For testing and visualization of non-linear trends also Smoothing can be used.

Views: 2546
The Audiopedia

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

Views: 155679
Terry Shaneyfelt

RR and OR are commonly used measures of association in observational studies. In this video I will discuss how to interpret them and how to apply them to patient care

Views: 198240
Terry Shaneyfelt

Check our video on "Introduction To Statistics - Basics - Data Collection"
Visit our website for more information: https://letstute.com
I hope you enjoy this online lecture on "Introduction To Statistics - Basics - Data Collection" by Let'stute.
#statistics #probability #mean #median #mode #cbse #quantitativeaptitude #problemsolving #maths
Topics covered in this session:
1. What is Statistics?
2. Statistics - Data Collection
3. Statistics - Organization
4. Statistics - Analysis
5. Statistics - Interpretation
6. Statistics - Presentation
BUY our entire Course Dvd’s and pendrive which includes video lectures, Assessments and Quiz
Amazon:
DVD'S:
For Class 9 : http://amzn.to/2qNvlgf
For Class 10 : https://goo.gl/yNb6pW
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Pendrive:
For Class 9 : http://amzn.to/2AHWrEW
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Values to Lead (Value Education): http://bit.ly/1poLX8j

Views: 30583
Letstute

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: 319450
americanbiotech

Qualitative research is a strategy for systematic collection, organization, and interpretation of phenomena that are difficult to measure quantitatively. Dr. Leslie Curry leads us through six modules covering essential topics in qualitative research, including what it is qualitative research and how to use the most common methods, in-depth interviews and focus groups. These videos are intended to enhance participants' capacity to conceptualize, design, and conduct qualitative research in the health sciences. Welcome to Module 5.
Bradley EH, Curry LA, Devers K. Qualitative data analysis for health services research:
Developing taxonomy, themes, and theory. Health Services Research, 2007; 42(4):1758-1772.
Learn more about Dr. Leslie Curry
http://publichealth.yale.edu/people/leslie_curry.profile
Learn more about the Yale Global Health Leadership Institute
http://ghli.yale.edu

Views: 146390
YaleUniversity

NOTE: On April 2, 2018 I updated this video with a new video that goes, step-by-step, through PCA and how it is performed. Check it out!
https://youtu.be/FgakZw6K1QQ
RNA-seq results often contain a PCA or MDS plot. This StatQuest explains how these graphs are generated, how to interpret them, and how to determine if the plot is informative or not. I've got example code (in R) for how to do PCA and extract the most important information from it on the StatQuest website: https://statquest.org/2015/08/13/pca-clearly-explained/
For a complete index of all the StatQuest videos, check out:
https://statquest.org/video-index/
If you'd like to support StatQuest, please consider a StatQuest t-shirt or sweatshirt...
https://teespring.com/stores/statquest
...or buying one or two of my songs (or go large and get a whole album!)
https://joshuastarmer.bandcamp.com/

Views: 366309
StatQuest with Josh Starmer

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: 737196
statisticsfun

I perform an independent samples t-test on data that have been simulated to correspond to an actual study done by Brody et al. (2004), which tested the hypothesis that individuals who do not smoke would have relatively larger frontal lobes than individuals who do smoke.
Something I didn't mention in the video is relevant to causality. Despite the fact that the Brody et al. (2004) investigation found that smokers have relatively smaller frontal lobes than non-smokers, one does not have a basis to infer causality in this case.
Get the data here:
http://how2stats.blogspot.com.au/2014/03/independent-samples-t-test-data1.html

Views: 568574
how2stats

Pearson's Chi Square Test (Goodness of Fit)
Watch the next lesson: https://www.khanacademy.org/math/probability/statistics-inferential/chi-square/v/contingency-table-chi-square-test?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Missed the previous lesson?
https://www.khanacademy.org/math/probability/statistics-inferential/chi-square/v/chi-square-distribution-introduction?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it!
About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to KhanAcademy’s Probability and Statistics channel:
https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1
Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 975579
Khan Academy

Exploring some basic data analysis in excel

Views: 43537
Jon Jasinski

Dr Darren Greenwood's presentation from the ESRC Strategic Network for Obesity Meeting - March 2016

Views: 262
Consumer Data Research Centre

Hypothesis Testing and P-values
Practice this yourself on Khan Academy right now: https://www.khanacademy.org/e/hypothesis-testing-with-simulations?utm_source=YTdescription&utm_medium=YTdescription&utm_campaign=YTdescription
Watch the next lesson: https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/one-tailed-and-two-tailed-tests?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Missed the previous lesson?
https://www.khanacademy.org/math/probability/statistics-inferential/margin-of-error/v/margin-of-error-2?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it!
About Khan Academy: Khan Academy is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to KhanAcademy’s Probability and Statistics channel:
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Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 2036760
Khan Academy

Provides steps for carrying out principal component analysis in r and use of principal components for developing a predictive model.
Link to code file: https://goo.gl/SfdXYz
Includes,
- Data partitioning
- Scatter Plot & Correlations
- Principal Component Analysis
- Orthogonality of PCs
- Bi-Plot interpretation
- Prediction with Principal Components
- Multinomial Logistic regression with First Two PCs
- Confusion Matrix & Misclassification Error - training & testing data
- Advantages and disadvantages
principal component analysis is an important statistical tool related to analyzing big data or working in data science field.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 24356
Bharatendra Rai

Views: 154
NIH ODP

This Lecture talks about Statistics or Data Analysis and Interpretation

Views: 1775
Cec Ugc

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: 774049
Jeremy Jones

This short video gives an explanation of the concept of confidence intervals, with helpful diagrams and examples.
Find out more on Statistics Learning Centre: http://statslc.com or to see more of our videos: https://wp.me/p24HeL-u6

Views: 681751
Dr Nic's Maths and Stats

Create your account and purchase your 2-day pass for only $5.25: https://analyze.intellectusstatistics.com/create_account/?key=m8KzB4FSoaHubjZn
Conduct and interpret a regression analysis in seconds using Intellectus Statistics. Students no longer need to struggle through spss tutorials, and spend hours trying to decipher spss output.
Intellectus Statistics provides output as a narrative interpretation of the analysis conducted in English prose, complete with APA tables and figures.
Try it for yourself with a one day pass. Upload your data, conduct your regression analysis, and download your fully interpreted editable word document.
Don't have data? No problem, we provide example data sets for you to practice with, learn the application, and learn how to conduct and interpret your analysis.
Get you daily pass here, and try it out for yourself: https://analyze.intellectusstatistics.com/create_account/?key=m8KzB4FSoaHubjZn
Don't have data? Don't worry! We provide example data sets already uploaded.

Views: 164
Intellectus Statistics

This chapter is based on Chapter 13: Data Analysis, Interpretation and Use in the following textbook:
Mertens, D. M. (2015). Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods (4th ed.). Thousand Oaks, CA: Sage Publications.
Content/ASLCoaching, Filming, Chapter Signer-Author: Frank Griffin
Editing: Raychelle Harris
To cite:
Griffin, F. & Harris, R. (2015). Sampling. In R. Harris & F. Williams (Eds.), Research and Evaluation in Education and Psychology, ASL Version (27:51 m.). Austin, Texas: ASLChoice.

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ASLized!

How to calculate the Correlation using the Data Analysis Toolpak in Microsoft Excel is Covered in this Video (Part 2 of 2).
Check out our brand-new Excel Statistics Text: https://www.amazon.com/dp/B076FNTZCV
In the text we cover the p-value for Correlation and much more.
YouTube Channel: https://www.youtube.com/user/statisticsinstructor
Channel Description: For step by step help with statistics, with a focus on SPSS (with Excel videos now too). 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

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Quantitative Specialists

Of the analysis, whether source variables and their concepts definitions are relevant to study, longitudinal or cross sectional nature of data is appropriate for this stage will explain different types qualitative analysis; Explain quantitative Help you interpret results your analysis. Analysis not left until the end; To avoid collecting data that quantitative research approach analysis. Data analysis and interpretation is the process of assigning meaning to collected information determining interpreting data an important critical thinking skill that helps you comprehend text books, graphs tables. The varying scales include nominal scale non numeric categories that cannot be ranked or compared quantitatively. Data analysis and presentation statistics canada. This involves identifying patterns and themes in data collected then examining interpreting these to draw meaning answer research questions. Definition analysis and interpretationdefinition interpretation. Experimental scientists base their interpretations largely on objective data and statistical calculations. Data analysis, interpretation and presentation uio. Data collection and interpretation dictionary definition of data analysis what is analysis? Definition & overview video lesson chapter 4 presentation. Definition analysis and interpretation. Textual data analysis in is the usual method used qualitative research approach. Data analysis and interpretation slideshare. A common method of assessing numerical data is known as statistical analysis, and the activity analyzing interpreting in order to make predictions learn sas or python programming, expand your knowledge analytical methods applications, conduct original research inform complex decisions. There are a variety of specific data analysis method the focus now turns to and interpretation for this studyanalysis. Wikipedia wiki data_analysis url? Q webcache. How do these differ by research tradition? Quantitativedata analysis during collection. It is a messy, ambiguous, time 30 may 2014 that's where interpretation of data comes in. It is described as messy, ambiguous and 1 jan 2015 7 nature functions of statistical analysis contd. This data often takes the form of records group discussions and interviews, but is not limited to this process evaluating using analytical logical reasoning examine each component provided. It is designed to help people with limited statistical or programming skills quickly become productive in an increasingly digitized workplace. Inferential statistics test for the relationship between two variables (at least one independent variable and dependent variable). Qualitative data analysis and interpretation unm. Po906 quantitative data analysis and interpretation university of. The process by which sense and meaning are made of the data gathered in qualitative research, emergent knowledge is applied to clients' problems. We will define it, learn about the forms of data collection, and go through process second,

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Joellen Reynolds Tipz

Paul Andersen shows you how to calculate the ch-squared value to test your null hypothesis. He explains the importance of the critical value and defines the degrees of freedom. He also leaves you with a problem related to the animal behavior lab. This analysis is required in the AP Biology classroom.
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

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Bozeman Science

The most simple and easiest intuitive explanation of regression analysis. Check out this step-by-step explanation of the key concepts of regression analysis.
It is assumed the viewer has little background in statistics. This lecture is suitable for tertiary students struggling with statistics.
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Simple Introduction to Hypothesis Testing:
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A Simple Rule to Correctly Setting Up the Null and Alternate Hypotheses:
https://www.youtube.com/watch?v=R2hxisYFKxM&feature=youtu.be
The Easiest Introduction to Regression Analysis:
http://www.youtube.com/watch?v=k_OB1tWX9PM
Super Easy Tutorial on Calculating the Probability of a Type 2 Error:
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Dave Your Tutor