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

Views: 459638
Matt Kermode

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: 773373
Dr Nic's Maths and Stats

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: 115940
zedstatistics

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: 407168
Stomp On Step 1

Subject:Anthropology
Paper: Research Methods and Field work

Views: 981
Vidya-mitra

This Lecture talks about Statistics or Data Analysis and Interpretation

Views: 1853
Cec Ugc

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: 733313
Dr Nic's Maths and Stats

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: 881385
Jeremy Jones

This presentation focuses on important study design/statistical issues that needs to be considered for the interpretation of randomized controlled trials and meta-analyses results.

Views: 86
European Society of Cardiology

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: 180076
MrSimpleScience

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
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Views: 33329
Letstute

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: 66797
ZieneMottiar

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: 471258
Bozeman Science

Everyone needs to understand regression! Its a useful data science technique that allows us to understand the relationship between different variables. In this video, we'll play the role of a newly hired data analyst at a genetics company trying to find the relationship between advertising mediums (TV, newspaper, radio) and ticket sales to our newly opened theme park. Along the way, we'll learn about 5 types of regression models (linear, non-linear, multiple, lasso, and ridge). Expect math, code, and layers of explanation. Enjoy!
Code for this video:
https://github.com/llSourcell/ISL-Ridge-Lasso
Please Subscribe! And Like. And comment. Thats what keeps me going.
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More learning resources:
https://www.youtube.com/watch?v=XdM6ER7zTLk
https://www.analyticsvidhya.com/blog/2017/06/a-comprehensive-guide-for-linear-ridge-and-lasso-regression/
http://statisticsbyjim.com/regression/choose-linear-nonlinear-regression/
https://hbr.org/2015/11/a-refresher-on-regression-analysis
http://blog.minitab.com/blog/adventures-in-statistics-2/how-to-interpret-regression-analysis-results-p-values-and-coefficients
Join us at the School of AI:
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Views: 14970
Siraj Raval

Practice this lesson yourself on KhanAcademy.org right now:
https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/e/variance?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Watch the next lesson: https://www.khanacademy.org/math/probability/descriptive-statistics/variance_std_deviation/v/variance-of-a-population?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Missed the previous lesson?
https://www.khanacademy.org/math/probability/descriptive-statistics/box-and-whisker-plots/v/range-and-mid-range?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.
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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: 1260064
Khan Academy

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: 706378
Kent Löfgren

This video explains the differences between parametric and nonparametric statistical tests. The assumptions for parametric and nonparametric tests are discussed including the Mann-Whitney Test, Kruskal-Wallis Test, Wilcoxon Signed-Rank Test, and Friedman’s ANOVA.

Views: 152059
Dr. Todd Grande

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: 29444
Bharatendra Rai

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: 159085
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: 416922
StatQuest with Josh Starmer

Purchase the spreadsheet (formulas included!) that's used in this tutorial for $5: https://gum.co/satisfactionsurvey
-----
Soar beyond the dusty shelf report with my free 7-day course: https://depictdatastudio.teachable.com/p/soar-beyond-the-dusty-shelf-report-in-7-days/ Most "professional" reports are too long, dense, and jargony. Transform your reports with my course. You'll never look at reports the same way again.

Views: 367190
Ann K. Emery

In order to get a Regression Equation, many times you can complete that job with only the Scatter Plot, but performing a Single Regression Analysis will give you more significant information. This video will explain how to interpret results of Simple Regression Analysis using Excel Data Analysis Tools. You will learn about the 'Coefficient of Determination', 'Correlation Coefficient', ‘Adjusted R Square’ and the differences among them and how to interpret them for your business. The P-Value represents the degree of relationship between the Explanatory Variable X and the Objective Variable Y, which is important for Multiple Regression Analysis, so this video will explain the P-Value in detail in this video.
(Process Improvement Methodology for Service Operations, PMP, Project Management, Lean Six Sigma, Japan, Six Sigma, Excel, VBA: Episode 106)
＜＜Read this video's transcript in my blog.＞＞
http://econoshift.com/en/simple-regression-analysis-Interpretation-2/
＜＜ Related Videos ＞＞
Simple Regression Analysis by Scatter Plot in Excel
https://youtu.be/jSbEQHlkURY
How to use the T-test and F-test in a real world【Excel Function】
https://youtu.be/kP8Ieb0Sm9I
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Free Excel Template Downloads: http://econoshift.com/ja/free-downloads/
Facebook Page: https://www.facebook.com/Econoshift/

Views: 56
econoshift.com

This entertaining video works step-by-step through a hypothesis test. Helen wishes to know whether giving away free stickers will increase her chocolate sales. This is a companion video for "Understanding the p-value".
You might like to read my blog:
http://learnandteachstatistics.wordpress.com

Views: 431941
Dr Nic's Maths and Stats

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: 2108254
Khan Academy

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: 212180
Stomp On Step 1

user interface design chapter 8

Views: 234
Android Code

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

Views: 166504
Terry Shaneyfelt

Links of Data set and case study used in the above video.
1.https://drive.google.com/open?id=1WzUf0rJU87nAZQ-ckf_X6w8FGpNB-srE

Views: 5209
Dr. Shailesh Kaushal

StatHelp supports Ph.D and MA candidates in any and all aspects of the research process including design, implementation, analysis and SPSS, writing, and presentation of results.
Free 1-hour Dissertation Consultation (for new clients):
Let's discuss your project and your needs to insure that your dissertation or thesis is on point, on track and gets finished on time? You can reach me at 310-623-7501.
StatHelp's Principle Researcher, Joseph Philippe Cohen, has diverse experience designing, analyzing, and reporting Business and Academic Research. Mr. Cohen has been sharing his thorough knowledge of SPSS and statistics since he taught and consulted with PhD candidates in Education as a graduate student working on his MBA.
After receiving his MBA, Mr. Cohen worked over a decade in corporate marketing research providing empirically driven insights to leading corporations like Symantec, WellPoint, Scripps Newspapers, and UCLA Extension. Mr Cohen's research knowledge is underpinned by an education that includes an MBA from SDSU and a BA in Cognitive Science from UC San Diego both of which emphasized social science research and analytical skills.
Phil's passion is to guide PhD and MA candidates in developing, implementing, and analyzing research plans that apply research and statistics to the candidates knowledge and ideas in their field of expertise.
Let me help you get to your goal - Dissertation Completion!
Choosing a Dissertation Topic
An anonymous academic once told one of my students that "A good dissertation is a finished dissertation." Though off-putting to an anxious PhD or Master candidate, the prof's comment speaks to the tenacity and heads up planning that must go into conceiving, executing, analyzing, and writing a dissertation. The more cognizant you are of the many constraints that must be fulfilled to complete a dissertation the better your chance of creating a good dissertation and indeed finishing your dissertation. In this post and the accompanying white paper "A Guide to Picking a Dissertation Topic," I outline three important factors and constraints that should go into any dissertation starting with your interests and ending with the literature that your research question must fit into.
Research Design
* Design research plan.
* Create goals, hypotheses
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* Fielding surveys and experimental research.
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Joseph Philippe Cohen

Python data analysis / data science tutorial. Let’s go!
For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata
Sample data and sample code: https://www.csdojo.io/data
My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8
Also, keep in touch on Twitter: https://twitter.com/ykdojo
And Facebook: https://www.facebook.com/entercsdojo
Outline - check the comment section for a clickable version:
0:37: Why data visualization?
1:05: Why Python?
1:39: Why Matplotlib?
2:23: Installing Jupyter through Anaconda
3:20: Launching Jupyter
3:41: DEMO begins: create a folder and download data
4:27: Create a new Jupyter Notebook file
5:09: Importing libraries
6:04: Simple examples of how to use Matplotlib / Pyplot
7:21: Plotting multiple lines
8:46: Importing data from a CSV file
10:46: Plotting data you’ve imported
13:19: Using a third argument in the plot() function
13:42: A real analysis with a real data set - loading data
14:49: Isolating the data for the U.S. and China
16:29: Plotting US and China’s population growth
18:22: Comparing relative growths instead of the absolute amount
21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata

Views: 206368
CS Dojo

Visual tutorial on how to calculate analysis of variance (ANOVA) and how to understand it too. The tutorial includes how to interpret the results of an Anova test, f test and how to look up values in the f distribution table. The Anova example is for a one way anova test.
I am rounding in the video, so if you are doing your own calculations you will not get the same exact numbers.
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PlayList on ANOVA
http://www.youtube.com/course?list=EC3A0F3CC5D48431B3
PlayList On TWO ANOVA
http://www.youtube.com/playlist?list=PLWtoq-EhUJe2TjJYfZUQtuq7a0dQCnOWp
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet

Views: 777883
statisticsfun

This video demonstrates how to test data for normality using SPSS. The Kolmogorov-Smirnov and Shapiro-Wilk tests are discussed.

Views: 172690
Dr. Todd Grande

Get this complete course at http://www.MathTutorDVD.com
In this lesson, we will discuss the very important topic of p-values in statistics. The p-value is a calculation that we make during hypothesis testing to determine if we reject the null hypothesis or fail to reject it.
The p-value is calculated by first finding the z test statistic. Once this is known we then need to find the probability of our population having a value more extreme than the test statistic. This is done by looking up the probability in a normal distribution table. We then interpret the results by comparing the p-value to the level of significance.
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Views: 474998
mathtutordvd

Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class.
Playlist on Linear Regression
http://www.youtube.com/course?list=ECF596A4043DBEAE9C
Like us on: http://www.facebook.com/PartyMoreStudyLess
Created by David Longstreet, Professor of the Universe, MyBookSucks
http://www.linkedin.com/in/davidlongstreet

Views: 716324
statisticsfun

PROCESS model 1 demonstrated on SPSS
A moderation analysis: meaning, procedure, plot and interpretation

Views: 58449
Goldi Tewari, Ph.D.

*** Check-out the improved version of this video here: https://youtu.be/tDLcBrLzBos
I describe the standard normal distribution and its properties with respect to the percentage of observations within each standard deviation. I also make reference to two key statistical demarcation points (i.e., 1.96 and 2.58) and their relationship to the normal distribution. Finally, I mention two tests that can be used to test normal distributions for statistical significance.
normal distribution, normal probability distribution, standard normal distribution, normal distribution curve, bell shaped curve

Views: 1055806
how2stats

There is a mistake at 9.22. Alpha is normally set to 0.05 NOT 0.5. Thank you Victoria for bringing this to my attention.
This video reviews key terminology relating to type I and II errors along with examples. Then considerations of Power, Effect Size, Significance and Power Analysis in Quantitative Research are briefly reviewed. http://youstudynursing.com/
Research eBook on Amazon: http://amzn.to/1hB2eBd
Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam
Quantitative research is driven by research questions and hypotheses. For every hypothesis there is an unstated null hypothesis. The null hypothesis does not need to be explicitly stated because it is always the opposite of the hypothesis. In order to demonstrate that a hypothesis is likely true researchers need to compare it to the opposite situation. The research hypothesis will be about some kind of relationship between variables. The null hypothesis is the assertion that the variables being tested are not related and the results are the product of random chance events. Remember that null is kind of like no so a null hypothesis means there is no relationship.
For example, if a researcher asks the question "Does having class for 12 hours in one day lead to nursing student burnout?"
The hypothesis would indicate the researcher's best guess of the results: "A 12 hour day of classes causes nursing students to burn out."
Therefore the null hypothesis would be that "12 hours of class in one day has nothing to do with student burnout."
The only way of backing up a hypothesis is to refute the null hypothesis. Instead of trying to prove the hypothesis that 12 hours of class causes burnout the researcher must show that the null hypothesis is likely to be wrong. This rule means assuming that there is not relationship until there is evidence to the contrary.
In every study there is a chance for error. There are two major types of error in quantitative research -- type 1 and 2. Logically, since they are defined as errors, both types of error focus on mistakes the researcher may make. Sometimes talking about type 1 and type 2 errors can be mentally tricky because it seems like you are talking in double and even triple negatives. It is because both type 1 and 2 errors are defined according to the researcher's decision regarding the null hypothesis, which assumes no relationship among variables.
Instead of remembering the entire definition of each type of error just remember which type has to do with rejecting and which one is about accepting the null hypothesis.
A type I error occurs when the researcher mistakenly rejects the null hypothesis. If the null hypothesis is rejected it means that the researcher has found a relationship among variables. So a type I error happens when there is no relationship but the researcher finds one.
A type II error is the opposite. A type II error occurs when the researcher mistakenly accepts the null hypothesis. If the null hypothesis is accepted it means that the researcher has not found a relationship among variables. So a type II error happens when there is a relationship but the researcher does not find it.
To remember the difference between these errors think about a stubborn person. Remember that your first instinct as a researcher may be to reject the null hypothesis because you want your prediction of an existing relationship to be correct. If you decide that your hypothesis is right when you are actually wrong a type I error has occurred.
A type II error happens when you decide your prediction is wrong when you are actually right.
One way to help you remember the meaning of type 1 and 2 error is to find an example or analogy that helps you remember. As a nurse you may identify most with the idea of thinking about medical tests. A lot of teachers use the analogy of a court room when explaining type 1 and 2 errors. I thought students may appreciate our example study analogy regarding class schedules.
It is impossible to know for sure when an error occurs, but researchers can control the likelihood of making an error in statistical decision making. The likelihood of making an error is related to statistical considerations that are used to determine the needed sample size for a study.
When determining a sample size researchers need to consider the desired Power, expected Effect Size and the acceptable Significance level.
Power is the probability that the researcher will make a correct decision to reject the null hypothesis when it is in reality false, therefore, avoiding a type II error. It refers to the probability that your test will find a statistically significant difference when such a difference actually exists. Another way to think about it is the ability of a test to detect an effect if the effect really exists.
The more power a study has the lower the risk of a type II error is. If power is low the risk of a type II error is high. ...

Views: 91446
NurseKillam

I explain what Cronbach's alpha is, how to interpret it, and discuss guidelines for acceptable levels.

Views: 135321
how2stats

This is a model fit exercise during a CFA in AMOS. I demonstrate how to build a good looking model, and then I address model fit issues, including modification indices and standardized residual covariances. I also discuss briefly the thresholds for goodness of fit measures. For a reference, you can use:
Litze Hu & Peter M. Bentler (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives, Structural Equation Modeling: A Multidisciplinary Journal, 6:1, 1-55

Views: 407446
James Gaskin

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: 730308
Dr Nic's Maths and Stats

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: 208946
Terry Shaneyfelt

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.
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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: 5327
The Audiopedia

Links of Data set and case study used in the above video.
1.https://drive.google.com/open?id=1wfmXJvCnW5slGFaDXm7H8ad-U9vnpG0c
2.https://drive.google.com/open?id=1x5DeQUIMFUHxJjwabiY2SUbSzz-9LOaL
3.https://drive.google.com/open?id=11vAuVIh06DNZ8Dca_-1TqktSK5NFTHJR

Views: 6159
Dr. Shailesh Kaushal

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.

Views: 757
ASLized!