Search results “What type of data analysis to use”

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: 803136
Dr Nic's Maths and Stats

In common health care research, some hypothesis tests are more common than others. How do you decide, between the common tests, which one is the right one for your research?
Thank you to the Statistical Learning Center for their excellent video on the same topic.
https://www.youtube.com/rulIUAN0U3w

Views: 406351
Erich Goldstein

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: 177805
Dr. Todd Grande

The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples.
Subtitles in English and Spanish.

Views: 947449
Dr Nic's Maths and Stats

A step-by-step approach for choosing an appropriate statistcal test for data analysis.

Views: 466869
TheRMUoHP Biostatistics Resource Channel

Data falls into several categories. Each type has some pros and cons, and is best suited for specific needs. Learn more in this short video from our Data Collection DVD available at http://www.velaction.com/data-collection-lean-training-on-dvd/.

Views: 157140
VelactionVideos

This video part of our online course Become a Data Analyst, it helps to understand types of data source available and which one to select.
Our course doesn’t only seek to teach you about data analysis but also helps you learn how to apply it in real-life situations. Apart from detailed programs on learning the basics of Python and the art of data analysis using Python, the course provides you with five projects that are real-life case studies.
Check our tutorial on BECOME A DATA ANALYST using Python:
http://yodalearning.com/courses/become-a-data-analyst-using-python
On top of that…
ALSO CHECK SOME OF THE DEMO COURSES WE OFFER
http://yodalearning.com/p/advanced-online-courses
Do not miss out from any videos and course offers. Follow us now!
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Check out more of our youtube video: https://www.youtube.com/channel/UCDLslmxC07X9DBun71c62sQ

Views: 3654
Yoda Learning Official

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

Views: 575370
J David Eisenberg

@ Members ~ This video would let you know about rising importance of Analytics where by we are covering all 4 Branches of Analytics like Financial Analytics , Risk Based Analytics , Cash Flow Analytics and Data Analytics. Video would also let you know about 3 types of Analytics covering Descriptive Analytics , Predictive Analytics and Prescriptive Analytics.
You are most welcome to connect with us at 91-9899242978 (Handheld) , Skype ~ Rahul5327 , Twitter @ Rahulmagan8 , [email protected] , [email protected] or visit our website - www.treasuryconsulting.in

Views: 54707
Foreign Exchange Maverick Thinkers

Here are a few of the many ways to look at data. Which is your favorite?
Practice this lesson yourself on KhanAcademy.org right now:
https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/dot-plot/e/intro-to-simple-data?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade
Watch the next lesson: https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-data-statistics/dot-plot/v/frequency-tables-and-dot-plots?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade
Missed the previous lesson? https://www.khanacademy.org/math/cc-sixth-grade-math/cc-6th-geometry-topic/cc-6th-polygons-in-the-coordinate-plane/v/constructing-polygon-on-coordinate-plane-example?utm_source=YT&utm_medium=Desc&utm_campaign=6thgrade
Grade 6th on Khan Academy: By the 6th grade, you're becoming a sophisticated mathemagician. You'll be able to add, subtract, multiply, and divide any non-negative numbers (including decimals and fractions) that any grumpy ogre throws at you. Mind-blowing ideas like exponents (you saw these briefly in the 5th grade), ratios, percents, negative numbers, and variable expressions will start being in your comfort zone. Most importantly, the algebraic side of mathematics is a whole new kind of fun! And if that is not enough, we are going to continue with our understanding of ideas like the coordinate plane (from 5th grade) and area while beginning to derive meaning from data! (Content was selected for this grade level based on a typical curriculum in the United States.)
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 Khan AcademyÂÃÂªs 6th grade channel:
https://www.youtube.com/channel/UCnif494Ay2S-PuYlDVrOwYQ?sub_confirmation=1
Subscribe to Khan Academy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 436300
Khan Academy

This Data Science tutorial video will give you an idea on the life of a Data Scientist, steps involved in Data science project, roles & salary offered to a Data Scientist. Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Data Science is basically dealing with unstructured and structured data. Data Science is a field that comprises of everything that is related to data cleansing, preparation, and analysis. In simple terms, it is the umbrella of techniques used when trying to extract insights and information from data. Now, let us get started and understand what is Data Science all about.
Below topics are explained in this Data Science tutorial:
1. Life of a Data Scientist
2. Steps in Data Science project
- Understanding the business problem
- Data acquisition
- Data preparation
- Exploratory data analysis
- Data modeling
- Visualization and communication
- Deploy & maintenance
3. Roles offered to a Data Scientist
4. Salary of a Data Scientist
To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
Read the full article here: https://www.simplilearn.com/career-in-data-science-ultimate-guide-article?utm_campaign=What-is-Data-Science-bTTxei-S1WI&utm_medium=Tutorials&utm_source=youtube
Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6
#DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package
5. Gain expertise in machine learning using the Scikit-Learn package
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
Learn more at: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=What-is-Data-Science-X3paOmcrTjQ&utm_medium=Tutorials&utm_source=youtube
For more information about Simplilearn’s courses, visit:
- Facebook: https://www.facebook.com/Simplilearn
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- LinkedIn: https://www.linkedin.com/company/simp...
- Website: https://www.simplilearn.com
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Views: 370357
Simplilearn

This video reviews the scales of measurement covered in introductory statistics: nominal, ordinal, interval, and ratio (Part 1 of 2).
Scales of Measurement
Nominal, Ordinal, Interval, Ratio
YouTube Channel: https://www.youtube.com/user/statisticsinstructor
Subscribe today!
Lifetime access to SPSS videos: http://tinyurl.com/m2532td
Video Transcript:
In this video we'll take a look at what are known as the scales of measurement. OK first of all measurement can be defined as the process of applying numbers to objects according to a set of rules. So when we measure something we apply numbers or we give numbers to something and this something is just generically an object or objects so we're assigning numbers to some thing or things and when we do that we follow some sort of rules. Now in terms of introductory statistics textbooks there are four scales of measurement nominal, ordinal, interval, and ratio. We'll take a look at each of these in turn and take a look at some examples as well, as the examples really help to differentiate between these four scales. First we'll take a look at nominal. Now in a nominal scale of measurement we assign numbers to objects where the different numbers indicate different objects. The numbers have no real meaning other than differentiating between objects. So as an example a very common variable in statistical analyses is gender where in this example all males get a 1 and all females get a 2. Now the reason why this is nominal is because we could have just as easily assigned females a 1 and males a 2 or we could have assigned females 500 and males 650. It doesn't matter what number we come up with as long as all males get the same number, 1 in this example, and all females get the same number, 2. It doesn't mean that because females have a higher number that they're better than males or males are worse than females or vice versa or anything like that. All it does is it differentiates between our two groups. And that's a classic nominal example. Another one is baseball uniform numbers. Now the number that a player has on their uniform in baseball it provides no insight into the player's position or anything like that it just simply differentiates between players. So if someone has the number 23 on their back and someone has the number 25 it doesn't mean that the person who has 25 is better, has a higher average, hits more home runs, or anything like that it just means they're not the same playeras number 23. So in this example its nominal once again because the number just simply differentiates between objects. Now just as a side note in all sports it's not the same like in football for example different sequences of numbers typically go towards different positions. Like linebackers will have numbers that are different than quarterbacks and so forth but that's not the case in baseball. So in baseball whatever the number is it provides typically no insight into what position he plays. OK next we have ordinal and for ordinal we assign numbers to objects just like nominal but here the numbers also have meaningful order. So for example the place someone finishes in a race first, second, third, and so on. If we know the place that they finished we know how they did relative to others. So for example the first place person did better than second, second did better than third, and so on of course right that's obvious but that number that they're assigned one, two, or three indicates how they finished in a race so it indicates order and same thing with the place finished in an election first, second, third, fourth we know exactly how they did in relation to the others the person who finished in third place did better than someone who finished in fifth let's say if there are that many people, first did better than third and so on. So the number for ordinal once again indicates placement or order so we can rank people with ordinal data. OK next we have interval. In interval numbers have order just like ordinal so you can see here how these scales of measurement build on one another but in addition to ordinal, interval also has equal intervals between adjacent categories and I'll show you what I mean here with an example. So if we take temperature in degrees Fahrenheit the difference between 78 degrees and 79 degrees or that one degree difference is the same as the difference between 45 degrees and 46 degrees. One degree difference once again. So anywhere along that scale up and down the Fahrenheit scale that one degree difference means the same thing all up and down that scale. OK so if we take eight degrees versus nine degrees the difference there is one degree once again. That's a classic interval scale right there with those differences are meaningful and we'll contrast this with ordinal in just a few moments but finally before we do let's take a look at ratio.

Views: 402305
Quantitative Specialists

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

Views: 433478
statslectures

Dr. Lisa Moyer at EIU discusses what type of statistical analysis is appropriate to use to answer research questions or test hypotheses.

Views: 261
Lisa Moyer

( Correction - Pen was assumed name instead of auther)
T-test,
Z-test,
F-yest,
Chi square test.
For different competitive exams
Keep watching chanakya group of economics.

Views: 372132
CHANAKYA group of Economics

Data Analytics for Beginners -Introduction to Data Analytics
https://acadgild.com/big-data/data-analytics-training-certification?utm_campaign=enrol-data-analytics-beginners-THODdNXOjRw&utm_medium=VM&utm_source=youtube
Hello and Welcome to data analytics tutorial conducted by ACADGILD. It’s an interactive online tutorial.
Here are the topics covered in this training video:
• Data Analysis and Interpretation
• Why do I need an Analysis Plan?
• Key components of a Data Analysis Plan
• Analyzing and Interpreting Quantitative Data
• Analyzing Survey Data
• What is Business Analytics?
• Application and Industry facts
• Importance of Business analytics
• Types of Analytics & examples
• Data for Business Analytics
• Understanding Data Types
• Categorical Variables
• Data Coding
• Coding Systems
• Coding, coding tip
• Data Cleaning
• Univariate Data Analysis
• Statistics Describing a continuous variable distribution
• Standard deviation
• Distribution and percentiles
• Analysis of categorical data
• Observed Vs Expected Distribution
• Identifying and solving business use cases
• Recognizing, defining, structuring and analyzing the problem
• Interpreting results and making the decision
• Case Study
Get started with Data Analytics with this tutorial. Happy Learning
For more updates on courses and tips follow us on:
Facebook: https://www.facebook.com/acadgild
Twitter: https://twitter.com/acadgild
LinkedIn: https://www.linkedin.com/company/acadgild

Views: 279892
ACADGILD

Practice this lesson yourself on KhanAcademy.org right now:
https://www.khanacademy.org/math/probability/statistical-studies/types-of-studies/e/types-of-statistical-studies?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Watch the next lesson: https://www.khanacademy.org/math/probability/statistical-studies/types-of-studies/v/correlation-and-causality?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Missed the previous lesson?
https://www.khanacademy.org/math/probability/statistical-studies/statistical-questions/v/reasonable-samples?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:
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Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 172780
Khan Academy

An explanation of how to compute the chi-squared statistic for independent measures of nominal data.
For an explanation of significance testing in general, see http://evc-cit.info/psych018/hyptest/index.html
There is also a chi-squared calculator at http://evc-cit.info/psych018/chisquared/index.html

Views: 1024046
J David Eisenberg

How to perform a simple t-test in Microsoft Excel

Views: 1272485
Jim Grange

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: 175790
YaleUniversity

This webinar provides an overview of basic quantitative analysis, including the types of variables and statistical tests commonly used by Student Affairs professionals. Specifically discussed are the basics of Chi-squared tests, t-tests, and ANOVAs, including how to read an SPSS output for each of these tests.

Views: 22342
CSSLOhioStateU

Basic introduction to correlation - how to interpret correlation coefficient, and how to chose the right type of correlation measure for your situation.
0:00 Introduction to bivariate correlation
2:20 Why does SPSS provide more than one measure for correlation?
3:26 Example 1: Pearson correlation
7:54 Example 2: Spearman (rhp), Kendall's tau-b
15:26 Example 3: correlation matrix
I could make this video real quick and just show you Pearson's correlation coefficient, which is commonly taught in a introductory stats course. However, the Pearson's correlation IS NOT always applicable as it depends on whether your data satisfies certain conditions. So to do correlation analysis, it's better I bring together all the types of measures of correlation given in SPSS in one presentation.
Watch correlation and regression: https://youtu.be/tDxeR6JT6nM
-------------------------
Correlation of 2 rodinal variables, non monotonic
This question has been asked a few times, so I will make a video on it. But to answer your question, monotonic means in one direction. I suggest you plot the 2 variables and you'll see whether or not there is a monotonic relationship there. If there is a little non-monotonic relationship then Spearman is still fine. Remember we are measuring the TENDENCY for the 2 variables to move up-up/down-down/up-down together. If you have strong non-monotonic shape in the plot ie. a curve then you could abandon correlation and do a chi-square test of association - this is the "correlation" for qualitative variables. And since your 2 variables are ordinal, they are qualitative.
Good luck

Views: 528231
Phil Chan

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: 563047
Bozeman Science

Download File: https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch00/Excel2013StatisticsChapter00.xlsx
All Excel Files for All Video files: http://people.highline.edu/mgirvin/excelisfun.htm.
Intro To Excel: Store Raw Data, Data Types, Data Analysis, Formulas, PivotTables, Charts, Keyboards, Number Formatting, Data Analysis & More:
(00:08) Introduction to class
(00:49) Cells, Worksheets, Workbooks, File Names
(02:54) Navigating Worksheets & Workbook
(03:58) Navigation Keys
(04:15) Keyboard move Active Sheet
(05:40) Ribbon Tabs
(06:25) Add buttons to Quick Access Tool Bar
(07:40) What Excel does: Store Raw Data, Make Calculations, Data Analysis & Charting
(08:55) Introduction to Data Analysis
(10:37) Data Types in Excel: Text, Numbers, Boolean, Errors, Empty Cells
(11:16) Keyboard Enter puts content in cell and move selected cell down
(13:00) Data Type DEFAULT Alignments
(13:11) First Formula. Entering Cell References in formulas
(13:35) Keyboard Ctrl + Enter puts content in cell & keep cell selected
(14:45) Why we don’t override DEFAULT Alignments
(15:05) Keyboard Ctrl + Z is Undo
(17:05) Proper Data Sets & Raw Data
(24:21) How To Enter Data & Data Labels
(24:21) Stylistic Formatting
(26:35) AVERAGE Function
(27:31) Format Formulas Differently than Raw Data
(28:30) Keyboard Ctrl + C is Copy. Keyboard Ctrl + V is Paste
(29:59) Use Eraser remove Formatting Only
(29:19) Keyboard Ctrl + B adds Bold
(29:57) Excel’s Golden Rule
(31:43) Keyboard F2 puts cell in Edit Mode
(32:01) Violating Excel’s Golden Rule
(34:12) Arrow Keys to put cell references in formulas
(35:40) Full Discussion about Formulas & Formulas Elements
(37:22) SUM function Keyboard is Alt + =
(38:22) Aggregate functions
(38:50) Why we use ranges in functions
(40:56) COUNT & COUNTA functions
(42:47) Edit Formula & change cell references
(44:18) Absolute & Relative Cell References
(45:52) Use Delete Key, Not Right-click Delete
(46:40) Fill Handle & Angry Rabbit to copy formula
(47:41) Keyboard F4 Locks Cell Reference (make Absolute)
(49:45) Keyboard Tab puts content in Cell and move selected Cell to right
(50:55) Order of Operation error
(52:17) Range Finder to find formula errors
(52:34) Lock Cell Reference after you put cell in Edit Mode
(53:58) Quickly copy an edited formula down a column
(53:07) F2 key in last cell to find formula errors
(54:15) Fix incorrect range in function
(54:55) SQRT function & Fractional Exponents
(57:20) STDEV.P function
(58:10) Navigate Large Data Sets
(58:48) Keyboard Ctrl + Arrow jumps to bottom of data set
(59:42) Keyboard Ctrl + Shift + Arrow selects to bottom of data set (Current Range)
(01:01:41) Keyboard Shift + Enter puts content in Cell and move selected Cell up
(01:02:55) Counting with conditions or criteria: COUNTIFS function
(01:03:43) Keyboard Ctrl + Backspace jumps back to Active Cell
(01:05:31) Counting between an upper & lower limit with COUNTIFS
(01:07:36) COUNTIFS copied down column
(01:10:08) Joining Comparative Operator with Cell Reference in formula
(01:12:50) Data Analysis features in Excel
(01:13:44) Sorting
(01:16:59) Filtering
(01:20:39) Introduction to PivotTables
(01:23:39) Create PivotTable dialog box
(01:24:33) Dragging & dropping Fields to create PivotTable
(01:25:31) Dragging Field to Row area creates a Unique List
(01:26:17) Outline/Tabular Layout
(01:27:00) Value Field Settings dialog to change: Number Formatting, Function, Name
(01:28:12) 2nd & 3rd PivotTable examples
(01:31:23) What is a Cross Tabulated Report?
(01:33:04) Create Cross Tabulated Report w PivotTable
(01:35:05) Show PivotTable Field List
(01:36:48) How to Pivot the Report
(01:37:50) Summarize Survey Data with PivotTable.
(01:38:34) Keyboard Alt, N, V opens PivotTable dialog box
(01:41:38) PivotTable with 3 calculations: COUNT, MAX & MIN
(01:43:25) Count & Count Number calculations in a PivotTable
(01:45:30) Excel 2013 Charts to Visually Articulate Quantitative Data
(01:47:00) #1 Rule for Charts: No Chart Junk!
(01:47:30) Explain chart types: Column, Bar, Pie, Line and X-Y Scatter Chart
(01:51:34) Create Column Chart using Recommended Chart feature
(01:53:00) Remove Field Buttons from Pivot Chart
(01:54:10) Chart Formatting Task Pane
(01:54:45) Vary Fill Color by point
(01:55:15) Format Axis with Numbers by Formatting Source Data in PivotTable
(01:56:02) Add Data Labels to Chart
(01:57:28) Copy Chart & Create Bar Chart
(01:57:48) Change Chart Type
(01:58:15) Change Gap Width.
(01:59:17) Create Pie Chart
(01:59:23) Do NOT use 3-D Pie
(01:59:42) Add % Data Labels to Pie Chart
(02:00:25) Create Line Chart From PivotTable
(02:01:20) Link Chart Tile to Cell
(02:02:20) Move a Chart
(02:02:33) Create an X-Y Scatter Chart
(02:03:35) Add Axis Labels
(02:05:27) Number Formatting to help save time
(02:07:24) Number Formatting is a Façade
(02:10:27) General Number Format
(02:10:52) Percentage Number Formatting
(02:14:03) Don’t Multiply Relative Frequency by 100
(02:17:27) Formula for % Change & End Amount

Views: 436240
ExcelIsFun

Advice on gathering and analyzing data in organizations, tips on using Likert scales, and a case study on leveraging data to help the bottom line.
<br/ ><br/ >Chris McMillan\'s Full Interview<br/ >Full Case Study by Sivar

Views: 18944
ASQ

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: 151898
Terry Jorgensen

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: 784074
Kent Löfgren

Business Analytics and Data Science are almost same concept. For both we need to learn Statistics. In this video I tried to create value on most used statistical methods for Data Science or Business Analytics for Statistical model Building.
Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics any can handle a scientific, industrial, or societal problem. I value your time and effort that is why I have capture almost 20 statically concept in this video.
Learn Basic statistics for Business Analytics
Here I have capture how to learn Mean, how to learn Mode, How to learn median, Concept of Sleekness, Concept of Kurtosis, learn Variables, concept of Standard deviation, Concept of Covariance, Concept of correlation, Concept of regression, How to read regression formula, how to read regression graph, Concept of Intercept, Concept of slope coefficient, Concept of Random Error, Different types of regression Analysis, Concept ANOVA (Analysis of Variance), How to read ANOVA table, How to learn R square (Interpreted R square), Concept of Adjusted R Square, Concept of F test, Concept of Information Value, Concept of WOE, Concept of Variable inflation Factors.
Learn Basic statistics for Business Analytics
By this video you can Start Learn statistics for Data Science and Business analytics easily and effectively.
These statistics are useful when at the time of running linear regression, Logistic regression statistics models.
For Statistical Data Exploration you may need to see Meager of central tendency and Data Spread in Statistics. By Understanding Mean, Mode, Median, Sleekness, Kurtosis, Variance, Standard deviation.
Learn Basic statistics for Business Analytics
To understand statistical relationship between variables you can use Covariance, Correlation coefficient, Regression , ANOVA (Analysis of Variance) .
Learn Basic statistics for Business Analytics
To understand Strength of stastical relationship between variables you can use R square, Adjusted R square, F test.
If you want to understand variable importance in your stastical model you can use Information value (IV) and Weight of evidence (WOE) Concept. Information value and Weight of evidence mostly used in Logistic Regression Analysis.
Learn Basic statistics for Business Analytics
Variable inflation factors (VIF) is used for understanding, It is the stastical method to understand variable importance. What is the importance of this variable statically in the Regression model? By VIF we check Correlation between variable.
Learn Basic statistics for Business Analytics
At last I have explained when to use ANOVA, When to Use Linear regression and when to use Logistic regression.
Learn Basic statistics for Business Analytics
Thank you So much for watching this video, Hope I can add some value in your Journey as a Statistician, Business Analytics professional and Data Scientist professional.
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Views: 93405
Analytics Analysis Business

This lesson will teach you Predictive analytics and Predictive Modelling Techniques.
Watch the New Upgraded Video: https://www.youtube.com/watch?v=DtOYBxi4AIE
After completing this lesson you will be able to:
1. Understand regression analysis and types of regression models
2. Know and Build a simple linear regression model
3. Understand and develop a logical regression
4. Learn cluster analysis, types and methods to form clusters
5. Know more series and its components
6. Decompose seasonal time series
7. Understand different exponential smoothing methods
8. Know the advantages and disadvantages of exponential smoothing
9. Understand the concepts of white noise and correlogram
10. Apply different time series analysis like Box Jenkins, AR, MA, ARMA etc
11. Understand all the analysis techniques with case studies
Regression Analysis:
• Regression analysis mainly focuses on finding a relationship between a dependent variable and one or more independent variables.
• It predicts the value of a dependent variable based on one or more independent variables
• Coefficient explains the impact of changes in an independent variable on the dependent variable.
• Widely used in prediction and forecasting
Data Science with R Language Certification Training: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-r-tools-training?utm_campaign=Predictive-Analytics-0gf5iLTbiQM&utm_medium=SC&utm_source=youtube
#datascience #datasciencetutorial #datascienceforbeginners #datasciencewithr #datasciencetutorialforbeginners #datasciencecourse
The Data Science with R training course has been designed to impart an in-depth knowledge of the various data analytics techniques which can be performed using R. The course is packed with real-life projects, case studies, and includes R CloudLabs for practice.
Mastering R language: The course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
Mastering advanced statistical concepts: The course also includes the various statistical concepts like linear and logistic regression, cluster analysis, and forecasting. You will also learn hypothesis testing.
As a part of the course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and Internet. R CloudLab has been provided to ensure a practical and hands-on experience. Additionally, we have four more projects for further practice.
Who should take this course?
There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals:
1. IT professionals looking for a career switch into data science and analytics
2. Software developers looking for a career switch into data science and analytics
3. Professionals working in data and business analytics
4. Graduates looking to build a career in analytics and data science
5. Anyone with a genuine interest in the data science field
6. Experienced professionals who would like to harness data science in their fields
For more updates on courses and tips follow us on:
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Get the android app: http://bit.ly/1WlVo4u
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Views: 218303
Simplilearn

Views: 80598
David Russell

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

Views: 868414
Claus Ebster

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
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Views: 2215688
Khan Academy

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: 135839
Edifo

Use Student's t-test to compare the means of two samples. However, the formulas that you use depends on whether the samples are paired or unpaired. If unpaired you also have to check for equality of variance. This video maps out a path to each of the three possible t-test formulas.

Views: 35006
Eugene O'Loughlin

How to enter and analyze questionnaire (survey) data in SPSS is illustrated in this video. Lots more Questionnaire/Survey & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT
Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and ‘Unlimited’ members, get our text for free. (Only 4.99 otherwise, but likely to increase soon.)
Survey data
Survey data entry
Questionnaire data entry
Channel Description: https://www.youtube.com/user/statisticsinstructor
For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Subscribe today!
YouTube Channel: https://www.youtube.com/user/statisticsinstructor
Video Transcript:
In this video we'll take a look at how to enter questionnaire or survey data into SPSS and this is something that a lot of people have questions with so it's important to make sure when you're working with SPSS in particular when you're entering data from a survey that you know how to do. Let's go ahead and take a few moments to look at that. And here you see on the right-hand side of your screen I have a questionnaire, a very short sample questionnaire that I want to enter into SPSS so we're going to create a data file and in this questionnaire here I've made a few modifications. I've underlined some variable names here and I'll talk about that more in a minute and I also put numbers in parentheses to the right of these different names and I'll also explain that as well. Now normally when someone sees this survey we wouldn't have gender underlined for example nor would we have these numbers to the right of male and female. So that's just for us, to help better understand how to enter these data. So let's go ahead and get started here. In SPSS the first thing we need to do is every time we have a possible answer such as male or female we need to create a variable in SPSS that will hold those different answers. So our first variable needs to be gender and that's why that's underlined there just to assist us as we're doing this. So we want to make sure we're in the Variable View tab and then in the first row here under Name we want to type gender and then press ENTER and that creates the variable gender. Now notice here I have two options: male and female. So when people respond or circle or check here that they're male, I need to enter into SPSS some number to indicate that. So we always want to enter numbers whenever possible into SPSS because SPSS for the vast majority of analyses performs statistical analyses on numbers not on words. So I wouldn't want and enter male, female, and so forth. I want to enter one's, two's and so on. So notice here I just arbitrarily decided males get a 1 and females get a 2. It could have been the other way around but since male was the first name listed I went and gave that 1 and then for females I gave a 2. So what we want to do in our data file here is go head and go to Values, this column, click on the None cell, notice these three dots appear they're called an ellipsis, click on that and then our first value notice here 1 is male so Value of 1 and then type Label Male and then click Add. And then our second value of 2 is for females so go ahead and enter 2 for Value and then Female, click Add and then we're done with that you want to see both of them down here and that looks good so click OK. Now those labels are in here and I'll show you how that works when we enter some numbers in a minute. OK next we have ethnicity so I'm going to call this variable ethnicity. So go ahead and type that in press ENTER and then we're going to the same thing we're going to create value labels here so 1 is African-American, 2 is Asian-American, and so on. And I'll just do that very quickly so going to Values column, click on the ellipsis. For 1 we have African American, for 2 Asian American, 3 is Caucasian, and just so you can see that here 3 is Caucasian, 4 is Hispanic, and other is 5, so let's go ahead and finish that. Four is Hispanic, 5 is other, so let's go to do that 5 is other. OK and that's it for that variable. Now we do have it says please state I'll talk about that next that's important when they can enter text we have to handle that differently.

Views: 659423
Quantitative Specialists

How to define variables and enter data into SPSS (v20)
ASK SPSS Tutorial Series

Views: 517143
BrunelASK

Data Analytics is a fast evolving field and so are the careers related to it it. So, what are the types of roles that one can transition to when it comes to Data Analytics? Answers to all your questions related to it can be answered through this video. Go now and watch the full video to add to your knowledge
While the answer is not rigid and compartmentalized, Rohit Sharma, Program Director for UpGrad and IIIT Bangalore's Data Analytics Program, tries to chalk out the various roles that exist in the industry, that you can apply and transition your career.
#DataAnalytics #StrategicDigitalMarketing #InternetMarketing
What does it take to be a Data Engineer, a Data Analyst, a Data Visualiser/Business Intelligence Professional or a Data Scientist? If you ever start pondering over these roles - moreover, if you are an IT professional wondering what will become of your career after IT - then you need to watch this video now and get accustomed to the main roles that exist within the space.
Grab a cool infographic here: https://blog.upgrad.com/top-4-data-analytics-skills-you-need-to-become-an-expert/?utm_source=YouTube&utm_medium=Organic_Social&utm_campaign=YouTube_Video&utm_term=YouTube_Video_Data&utm_content=YouTube_Video_Data_Analytics_Roles_Blog_Link
Let us know what you think in the comments section below and if you have any further questions regarding data analytics, do reach out!
Wish to transition to the coolest job in the industry?
Know about the Data Analytics Program from UpGrad here: https://upgrad.com/data-science/?utm_source=YouTube&utm_medium=Organic_Social&utm_campaign=YouTube_Video&utm_term=YouTube_Video_Data&utm_content=YouTube_Video_Data_Analytics_Roles
upGrad is an online higher education platform providing rigorous industry-relevant programs designed and delivered in collaboration with world-class faculty and industry. Merging the latest technology, pedagogy, and services, upGrad is creating an immersive learning experience – anytime and anywhere. upGrad began in 2015 with the conviction that in an ever-changing industry, professionals need to continuously upskill themselves in order to stay relevant.
upGrad has created some of India’s largest online programs to help thousands of professionals achieve their career goals in the areas of data, technology, and management.
Stay on top of your industry by interacting with us on our social channels:
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https://www.linkedin.com/company/ueducation/Joint Certificate from Cambridge Judge Business School Executive Education and upGrad

Views: 89361
upGrad

Every day plenty of data is generated worldwide and stored by public administration and private companies, around 2.5 trillion bytes globally to be precise. Besides, cities are full of sensors collecting all kinds of feed regarding weather, telephony, traffic.
Big Data Analytics is a concept that clusters all those technologies and mathematical developments dedicated to store, analyze and cross-reference all that information to try and find behavioural patterns. Let´s dive into Big Data Analytics and this year´s trends.
More information: https://www.imnovation-hub.com/digital-transformation/big-data-analytics-the-datafication-of-society/
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Views: 78994
ACCIONA

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: 120390
LinkedIn Learning

Get the full course at: http://www.MathTutorDVD.com
The student will learn the big picture of what a hypothesis test is in statistics. We will discuss terms such as the null hypothesis, the alternate hypothesis, statistical significance of a hypothesis test, and more.
In this step-by-step statistics tutorial, the student will learn how to perform hypothesis testing in statistics by working examples and solved problems.

Views: 1515790
mathtutordvd

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Views: 174298
Joma Tech

1. Descriptives: 1:32
2. T test: 2:52
3. Correlation: 4:41
4. Chi square: 5:39
5. Linear regression: 6:45
This video discusses the basic statistical analytical procedures that are required for a typical bachelor's thesis. Five stats are highlighted here: descriptives, T test, correlation, Chi square, and linear regression.
For requirements on reporting stats, please refer to the appendix of your research module manuals -- Frans Swint and I wrote an instructional text on APA reporting of stats. There is no upper limit in terms of how advanced your stats should be in your bachelor's dissertation. This video covers the basic procedures and is not meant to replace the instructions of your own research supervisor. Please consult your own research advisor for specific questions regarding your data analyses.
Please LIKE this video if you enjoyed it.
Otherwise, there is a thumb-down button, too... :P
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The views expressed in this video are my own and do not necessarily reflect the organizations with which I am affiliated.
#RanywayzRandom #SPSS #Research

Views: 10201
Ranywayz Random

Learn about Likert Scales in SPSS and how to copy labels from one variable to another in this video. Entering codes for Likert Scales into SPSS is also covered.
Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and 'Unlimited' members, get our text for free! (Only $4.99 otherwise, but will likely increase soon.)
Lots more Likert & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT
Likert scale SPSS video.
YouTube Channel: https://www.youtube.com/user/statisticsinstructor
Channel Description: For step by step help with statistics and SPSS. Both descriptive and inferential statistics covered. Subscribe today!
Video Transcript: In this video we'll take a look at how to enter value labels for a variable which will be review since we've done that before. But then I also want to show you how to apply value labels that were entered for one variable to a number of different variables which can be really useful as it's a great time saver. Here in this data set notice that I have 10 people and I have the variables gender, item 1, 2, 3, 4, and 5. And they answered on what's known as a Likert scale. Now you very well may have heard of a Likert scale before and the first thing is you may have heard of it called LIKE-ERT scale which is very common to call it that but it's actually Likert, so it's pronounced LICK-ERT instead of LIKE-ERT and it was developed by Rensis Likert in the early to middle 1900s he developed the scale. And it's used so commonly, it's used in this 5-point option as you see here, 5 to 1, and we'll talk about that in just a moment. You'll also see it in a 7-point option, it's very commonly used that way. And less commonly so but you'll see it in other ways like 9-point scale and so forth. And it's used with many different kinds of descriptions like definitely true, somewhat true, and so forth; not just agree as you see here. So, in the most traditional use of this scale, which is what we see right here, we have a 5=strongly agree, a 4=agree, 3 is neither agree nor disagree - this is sometimes called neutral - 2 is disagree and then 1 is strongly disagree. On item 1 they would read the following statement: I can turn to others for support when needed. And then what they do is they read that item, they look at these 5 options, and if it's someone who has a lot of support in their network or friendships or what have you, they might answer 5, strongly agree, or 4, agree. And if it's someone who doesn't experience a lot of social support, they might answer a 1 for strongly disagree or a 2 for disagree and so on. So, the first person here in row 1, notice for item 1 they answered a 4, so they answered agree. Item 2 they answered a 5 for strongly agree and so on. If we look down item 1, did anyone answer strongly disagree - let's take a look at that. We're looking for a 1 here, and notice that participant number 9, they answered a 1 on item 1, so they answered strongly disagree, and so on. So what I want to do here is go ahead and enter the value labels for item 1 so we're going to enter these into SPSS that you see here. And then I want to show you how to apply those to the remaining items in a very quick way. First of all, notice that we have gender, if I click on my value labels button here as a review, gender is already coded, I already entered those. But what I don't have entered is item 1, item 2, 3, 4, and 5. And I'd like to go ahead and enter those to have them in the dataset, so if I go back and look at this file at a later time, I'll remember that a 5 corresponded to strongly agree and a 1 corresponded to strongly disagree, so in other words I'll know which direction this scale is scored, and what I mean by that is higher scores indicate greater social support because people strongly agreed with a given item. Whereas lower scores indicated less social support. Since we're looking at entering value labels, let's begin with item 1. So I could either double-click on item 1 or I could go to the variable view tab. Let's go ahead and double-click on item 1 right at the column heading here that's "name". So I double-click on that and notice it takes me to the variable view window. So that's a quick way to get there if you want to access the variable view window. And then we'll go to the "values" column here, click on the "None" cell and then notice the 3 dots appear. So I click on that and then here let's start with
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Video on adding Likert items together to create a total score: http://youtu.be/7jxpSLZCBsw
Likert Scales
Likert
Strongly Agree to Strongly Disagree
Likert in SPSS

Views: 194786
Quantitative Specialists

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

Views: 224847
Alan Cann

Learn the difference between Nominal, ordinal, interval and ratio data. http://youstudynursing.com/
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Quantitative researchers measure variables to answer their research question.
The level of measurement that is used to measure a variable has a significant impact on the type of tests researchers can do with their data and therefore the conclusions they can come to. The higher the level of measurement the more statistical tests that can be run with the data. That is why it is best to use the highest level of measurement possible when collecting information.
In this video nominal, ordinal, interval and ratio levels of data will be described in order from the lowest level to the highest level of measurement. By the end of this video you should be able to identify the level of measurement being used in a study. You will also be familiar with types of tests that can be done with each level.
To remember these levels of measurement in order use the acronym NOIR or noir.
The nominal level of measurement is the lowest level. Variables in a study are placed into mutually exclusive categories. Each category has a criteria that a variable either has or does not have. There is no natural order to these categories.
The categories may be assigned numbers but the numbers have no meaning because they are simply labels. For example, if we categorize people by hair color people with brown hair do not have more or less of this characteristic than those with blonde hair.
Nominal sounds like name so it is easy to remember that at a nominal level you are simply naming categories.
Sometimes researchers refer to nominal data as categorical or qualitative because it is not numerical.
Ordinal data is also considered categorical. The difference between nominal and ordinal data is that the categories have a natural order to them. You can remember that because ordinal sounds like order.
While there is an order, it is also unknown how much distance is between each category.
Values in an ordinal scale simply express an order.
All nominal level tests can be run on ordinal data.
Since there is an order to the categories the numbers assigned to each category can be compared in limited ways beyond nominal level tests. It is possible to say that members of one category have more of something than the members of a lower ranked category. However, you do not know how much more of that thing they have because the difference cannot be measured.
To determine central tendency the categories can be placed in order and a median can now be calculated in addition to the mode.
Since the distance between each category cannot be measured the types of statistical tests that can be used on this data are still quite limited. For example, the mean or average of ordinal data cannot be calculated because the difference between values on the scale is not known.
Interval level data is ordered like ordinal data but the intervals between each value are known and equal. The zero point is arbitrary. Zero simply represents an additional point of measurement.
For example, tests in school are interval level measurements of student knowledge. If you scored a zero on a math test it does not mean you have no knowledge. Yet, the difference between a 79 and 80 on the test is measurable and equal to the difference between an 80 and an 81.
If you know that the word interval means space in between it makes remembering what makes this level of measurement different easy.
Ratio measurement is the highest level possible for data. Like interval data, Ratio data is ordered, with known and measurable intervals between each value. What differentiates it from interval level data is that the zero is absolute. The zero occurs naturally and signifies the absence of the characteristic being measured. Remember that Ratio ends in an o therefore there is a zero.
Typically this level of measurement is only possible with physical measurements like height, weight and length.
Any statistical tests can be used with ratio level data as long as it fits with the study question and design.

Views: 343157
NurseKillam

It’s easy to get lost in a lot of text-based data. NVivo is qualitative data analysis software that provides structure to text, helping you quickly unlock insights and make something beautiful to share.
http://www.qsrinternational.com

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NVivo by QSR

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

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StatsCast

In this video, I present an example of a multiple regression analysis of website visit duration data using both quantitative and qualitative variables. Variables used include gender, browser, mobile/non-mobile, and years of education. Gender and mobile each require a single dummy variable, while browser requires several dummy variables. I also present models that include interactions between the dummy variables and years of education to analyze intercept effects, slope effects, and fully interacted models. In short, I cover:
- multiple category qualitative variables
- dummy variables
- intercept effects
- slope effects
- dummy interactions
I hope you find it useful! Please let me know if you have any questions!
--Dr. D.

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Jason Delaney

How to run a correlation analysis using Excel and write up the findings for a report

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Chris Olson

Updated video 2018: SPSS for Beginners - Introduction https://youtu.be/_zFBUfZEBWQ
This video provides an introduction to SPSS/PASW. It shows how to navigate between Data View and Variable View, and shows how to modify properties of variables.

Views: 1598916
Research By Design

© 2019 Make business online successful

This is a point that I want to expand on a little more, specifically in relation to copying other traders. Below is a screenshot of my equity chart over six months. The red line shows the number of people copying me. My equity vs copiers chart. The same holds true for the stock market in general. Long-term growth of UK stock market. Useful resources. How to Start Trading Cryptocurrencies. Cryptocurrency trading can be extremely profitable if you know what you are doing, but it can also lead to disaster. Even though most traders decide to either go with fiat or bitcoin, other cryptocurrencies can represent viable income sources, as long you as you tread carefully and understand what you are doing. This guide is for those who want to start getting involved in cryptocurrency trading. Where to trade.