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Choosing which statistical test to use - statistics help
 
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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.
Views: 678961 Dr Nic's Maths and Stats
Choosing a Statistical Test
 
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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: 326891 Erich Goldstein
How to Use SPSS: Choosing the Appropriate Statistical Test
 
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A step-by-step approach for choosing an appropriate statistcal test for data analysis.
How to choose the correct statistical test - or how to tell the correct test has been used
 
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Knowing how to chose the correct statistical test is essential if you're analysing data, reading a paper or sitting in the academic stations of the FRCS or National Selection. Watch this podcast by statistical guru Brett Doleman and you'll know how to chose the right test or know if the right one has been chosen. Using a step by step, easy to follow decision tree, Brett takes you to the correct test for the type of data you have. Statistical tests demystified forever!
Views: 26005 school of surgery
Types of Data: Nominal, Ordinal, Interval/Ratio - Statistics Help
 
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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: 783889 Dr Nic's Maths and Stats
Parametric and Nonparametric Statistical Tests
 
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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: 132936 Dr. Todd Grande
Introduction to Statistics..What are they? And, How Do I Know Which One to Choose?
 
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This tutorial provides an overview of statistical analyses in the social sciences. It distinguishes between descriptive and inferential statistics, discusses factors for choosing an analysis procedure, and identifies the difference between parametric and nonparametric procedures.
Views: 215505 The Doctoral Journey
Data Analytics for Beginners | Introduction to Data Analytics | Data Analytics Tutorial
 
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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: 204851 ACADGILD
Ways to represent data | Data and statistics | 6th grade | Khan Academy
 
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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 Academy‰Ûªs 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: 410932 Khan Academy
Different types of data sources you can use for Data Analysis
 
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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! http://www.facebook.com/yodalearning http://www.twitter.com/yodalearning Check out more of our youtube video: https://www.youtube.com/channel/UCDLslmxC07X9DBun71c62sQ
Views: 2887 Excel Yoda learning
Excel 2013 Statistical Analysis #01: Using Excel Efficiently For Statistical Analysis (100 Examples)
 
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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: 402052 ExcelIsFun
Data Analytics - Descriptive , Predictive and Prescriptive Analytics
 
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@ 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
Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
 
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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: 145948 YaleUniversity
Intro to Hypothesis Testing in Statistics - Hypothesis Testing Statistics Problems & Examples
 
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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: 1095778 mathtutordvd
Data Collection: Understanding the Types of Data.
 
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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: 136133 VelactionVideos
Data Analytics: Top 4 Jobs and Careers
 
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Data Analytics is an evolving and fast-growing field. Therefore, so are the careers within it. So, what are the types of roles that one can transition to when it comes to Data Analytics? While the answer is not strict 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 skill yourself for and transition to. What does it take to be a Data Engineer, a Data Analyst, a Data Visualiser/Business Intelligence Professional or a Data Scientist? If you've found yourself wondering or considering 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 takes pride in constantly churning out content that is contemporary, written by subject matter experts and delves into the world of Data Science, Big Data, Digital Marketing, Entrepreneurship, Product Management, Machine Learning and Artificial Intelligence, Software Development on regular basis. Stay on top of your industry by interacting with us on our social channels: Follow us on Instagram: https://instagram.com/upgrad_edu Like us on Facebook: https://www.facebook.com/UpGradGlobal Follow us on Twitter: https://www.twitter.com/upgrad_edu Follow us on LinkedIn: https://in.linkedin.com/company/ueducation
Views: 58750 UpGrad
Analysis of Variance (ANOVA)
 
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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: 454652 J David Eisenberg
Hypothesis testing and p-values | Inferential statistics | Probability and Statistics | Khan Academy
 
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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: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 2033416 Khan Academy
Types of statistical studies | Statistical studies | Probability and Statistics | Khan Academy
 
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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: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 166312 Khan Academy
Data Analysis in SPSS Made Easy
 
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Use simple data analysis techniques in SPSS to analyze survey questions.
Views: 791258 Claus Ebster
What is Correlation Analysis?
 
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In this brief presentation, Kelly Clement shows you what correlation analysis is, and how to use it in your market analysis.
Views: 28516 MetaStock
Likert Scales and Data Analysis
 
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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. McMillan Interview http://videos.asq.org/influencing-public-policy-with-data-analysis Full Case Study by S. Pandravada and T. Gurun https://secure.asq.org/perl/msg.pl?prvurl=http://asq.org/2017/02/statistical-process-control/fresh-foods-ordering-process.pdf
Views: 8820 ASQ
Interview with a Data Scientist
 
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This video is part of the Udacity course "Intro to Programming". Watch the full course at https://www.udacity.com/course/ud000
Views: 281630 Udacity
The 4 Types of Data Analytics
 
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This vlog piece was filmed in Penang, Malaysia and introduces viewers to the 4 types of data analytics. The 4 Types of Data Analytics Descriptive - Answers the question, "What Happened?". Diagnostic - Commonly used in engineering and sciences to diagnose "what went wrong?". Predictive - Used to predict for future trends and events based on statistical or mathematical modeling of current and historical data. Prescriptive - Used to tell you what to do to achieve a desired result based on the findings of predictive analytics.
SPSS for questionnaire analysis:  Correlation analysis
 
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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: 494547 Phil Chan
Qualitative analysis of interview data: A step-by-step guide
 
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The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends. The steps are also described in writing below (Click Show more): STEP 1, reading the transcripts 1.1. Browse through all transcripts, as a whole. 1.2. Make notes about your impressions. 1.3. Read the transcripts again, one by one. 1.4. Read very carefully, line by line. STEP 2, labeling relevant pieces 2.1. Label relevant words, phrases, sentences, or sections. 2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant. 2.3. You might decide that something is relevant to code because: *it is repeated in several places; *it surprises you; *the interviewee explicitly states that it is important; *you have read about something similar in reports, e.g. scientific articles; *it reminds you of a theory or a concept; *or for some other reason that you think is relevant. You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you. It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds. STEP 3, decide which codes are the most important, and create categories by bringing several codes together 3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand. 3.2. You can create new codes by combining two or more codes. 3.3. You do not have to use all the codes that you created in the previous step. 3.4. In fact, many of these initial codes can now be dropped. 3.5. Keep the codes that you think are important and group them together in the way you want. 3.6. Create categories. (You can call them themes if you want.) 3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever. 3.8. Be unbiased, creative and open-minded. 3.9. Your work now, compared to the previous steps, is on a more general, abstract level. 3.10. You are conceptualizing your data. STEP 4, label categories and decide which are the most relevant and how they are connected to each other 4.1. Label the categories. Here are some examples: Adaptation (Category) Updating rulebook (sub-category) Changing schedule (sub-category) New routines (sub-category) Seeking information (Category) Talking to colleagues (sub-category) Reading journals (sub-category) Attending meetings (sub-category) Problem solving (Category) Locate and fix problems fast (sub-category) Quick alarm systems (sub-category) 4.2. Describe the connections between them. 4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study. STEP 5, some options 5.1. Decide if there is a hierarchy among the categories. 5.2. Decide if one category is more important than the other. 5.3. Draw a figure to summarize your results. STEP 6, write up your results 6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results. 6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example: *results from similar, previous studies published in relevant scientific journals; *theories or concepts from your field; *other relevant aspects. STEP 7 Ending remark This tutorial showed how to focus on segments in the transcripts and how to put codes together and create categories. However, it is important to remember that it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.) Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze: *notes from participatory observations; *documents; *web pages; *or other types of qualitative data. STEP 8 Suggested reading Alan Bryman's book: 'Social Research Methods' published by Oxford University Press. Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE. Good luck with your study. Text and video (including audio) © Kent Löfgren, Sweden
Views: 666676 Kent Löfgren
Introduction to ANOVA
 
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statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!
Views: 352643 statslectures
Determining what kind of statistical analysis to do
 
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Dr. Lisa Moyer at EIU discusses what type of statistical analysis is appropriate to use to answer research questions or test hypotheses.
Views: 223 Lisa Moyer
Nominal, ordinal, interval and ratio data: How to Remember the differences
 
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Learn the difference between Nominal, ordinal, interval and ratio data. http://youstudynursing.com/ Research eBook on Amazon: http://amzn.to/1hB2eBd Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam For help with Research - Get my eBook "Research terminology simplified: Paradigms, axiology, ontology, epistemology and methodology" here: http://www.amazon.com/dp/B00GLH8R9C Related Videos: http://www.youtube.com/playlist?list=PLs4oKIDq23AdTCF0xKCiARJaBaSrwP5P2 Connect with me on Facebook Page: https://www.facebook.com/NursesDeservePraise Twitter: @NurseKillam https://twitter.com/NurseKillam Facebook: https://www.facebook.com/laura.killam LinkedIn: http://ca.linkedin.com/in/laurakillam 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: 317444 NurseKillam
How to tabulate, analyze, and prepare graph from Likert Scale questionnaire data using Ms Excel.
 
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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: 78376 Edifo
Mini Statistics Lecture: Analyzing Likert Scale Questionnaire Data using R
 
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Likert Scale: http://en.wikipedia.org/wiki/Likert_scale R: http://www.r-project.org/
Views: 200418 Alan Cann
Excel 2013 Statistical Analysis #5 Data Categorical, Quantitative, Nominal, Ordinal, Interval, Ratio
 
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Download files: http://people.highline.edu/mgirvin/excelisfun.htm Topics in this video: 1. (00:43) Categorical Data vs. Quantitative Data 2. (02:00) Scales of Measurement (Levels of Measurement): Nominal, Ordinal, Interval, Ratio 3. (14:42) Cross Sectional Data vs. Time Series Data 4. (15:48) Graphical Display of types of Data 5. (16:22) How to Enter Data into the spreadsheet and use the Auto Complete (Auto Text) to your benefit 6. (18:50) How to create a new Excel Workbook to do your Homework from the Textbook
Views: 32798 ExcelIsFun
SPSS for Beginners 1: Introduction
 
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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: 1355117 Research By Design
Data Science With Python | Python for Data Science | Python Data Science Tutorial | Simplilearn
 
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This Data Science with Python Tutorial will help you understand what is Data Science, basics of Python for data analysis, why learn Python, how to install Python, Python libraries for data analysis, exploratory analysis using Pandas, introduction to series and dataframe, loan prediction problem, data wrangling using Pandas, building a predictive model using Scikit-Learn and implementing logistic regression model using Python. The aim of this video is to provide a comprehensive knowledge to beginners who are new to Python for data analysis. This video provides a comprehensive overview of basic concepts that you need to learn to use Python for data analysis. Now, let us understand how Python is used in Data Science for data analysis. This Data Science with Python tutorial will cover the following topics: 1. What is Data Science? 2. Basics of Python for data analysis - Why learn Python? - How to install Python? 3. Python libraries for data analysis 4. Exploratory analysis using Pandas - Introduction to series and dataframe - Loan prediction problem 5. Data wrangling using Pandas 6. Building a predictive model using Scikit-learn - Logistic regression To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/ifQRpS 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 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=Data-Science-With-Python-mkv5mxYu0Wk&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - 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: 30359 Simplilearn
SPSS Questionnaire/Survey Data Entry - Part 1
 
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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: 457277 Quantitative Specialists
Qualitative Data Analysis - Coding & Developing Themes
 
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This is a short practical guide to Qualitative Data Analysis
Views: 97305 James Woodall
R tutorial: The basic data types in R
 
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In this video you will understand what the basic data types are in R. Want to take the interactive coding exercises and earn a certificate? Join DataCamp today, and start the free introduction to R tutorial: https://www.datacamp.com/courses/free-introduction-to-r In the previous video you saw that R is also know as the Language for Statistical Computing. Data is the center of any statistical analysis, so let me introduce you to some of R's fundamental data types, also called atomic vector types. Throughout our experiments, we will use the function class(). This is a useful way to see what type a variable is. Let's head over to the console and start with TRUE, in capital letters. TRUE is a logical. That's also what class(TRUE) tells us. Logicals are so-called boolean values, and can be either `TRUE` or `FALSE`. Well, actually, `NA`, to denote missing values, is also a logical, but I won't go into detail on that here. `TRUE` and `FALSE` can be abbreviated to `T` and `F` respectively, as you can see here. However, I want to strongly encourage you to use the full versions, `TRUE` and `FALSE`. Next, let's experiment with numbers. The values 2 and 2.5 are called numerics in R. You can perform all sorts of operations on them such as addition, subtraction, multiplication, division and many more. A special type of numeric is the integer. It is a way to represent natural numbers like 1 and 2. To specify that a number is integer, you can add a capital L to them. You don't see the difference between the integer 2 and the numeric 2 from the output. However, the `class()` function reveals the difference. Instead of asking for the class of a variable, you can also use the is-dot-functions to see whether variables are actually of a certain type. To see if a variable is a numeric, we can use the is-dot-numeric function. It appears that both are numerics. To see if a variable is integer, we can use is-dot-integer. This shows us that integers are numerics, but that not all numerics are integers, so there's some kind of type hierarchy going on here. Last but not least, there's the character string. The class of this type of object is "character". It's important to note that there are other data types in R, such as double for higher precision numerics, complex for handling complex numbers, and raw to store raw bytes. However, you will have tons of fun working with numerics, integers, logicals and characters in the remainder of this introductory course so we'll leave these alone for now. There are cases in which you want to change the type of a variable to another one. How would that work? This is where coercion comes into play! By using the as dot functions one can coerce the type of a variable to another type. Many ways of transformation between types are possible. Have a look at these examples. The first command here coerces the logical TRUE to a numeric. FALSE, however, coerces to the numeric zero. We can also coerce numerics to characters. But what about the other way around? Can you also coerce characters to numerics? Sure you can! You can even convert this character string, "4.5", to an integer, but this implies some information loss, because you cannot keep the decimal part here. But beware: coercion, as in converting data types, is not always possible. Let's try to convert the character "Hello" to a numeric. This conversion outputs an NA, a missing value. R doesn't understand how to transform "Hello" into a numeric, and decides to return a Not Available instead. You already have the essentials on what R is, how to use its basic features and what are the most important data types you will encounter in your R quest. Now head over to the exercises and I'll see you in the next chapter!
Views: 76800 DataCamp
StatsCast: What is a t-test?
 
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For more, visit http://www.statscast.net This video explains the purpose of t-tests, how they work, and how to interpret the results.
Views: 655026 StatsCast
GraphPad Prism Tutorial 1 - Introducing Table Types
 
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This is the first video in our series dedicated to the powerful data analysis software, GraphPad Prism. In this video, we introduce the six basic types of Data Tables that are used for various analyses in GraphPad Prism. Understanding how to choose the right type of Data Table will make all the subsequent steps for graphing and analyzing data very straightforward! If you do not already have GraphPad Prism, you can download a free 30-day trial here: http://protocol-place.com/Prism This video and other protocols can be found at our website, the "Protocol Place" - http://protocol-place.com/ This video is a part of our GraphPad Prism Tutorial Series. You can see the entire set of videos here - http://www.youtube.com/watch?v=M0Sl-3eu974&list=PLR4wfoQ4HbykS2rzwpHTI_R4QEGSmHbNV We hope you enjoy watching and benefit from our tutorials. If so, please take a minute to "like," or better yet, share them with others! Thanks for watching! Youssef Farhat Protocol Place: http://protocol-place.com ***Check out some of our other tutorials via the links below*** Basic Lab Techniques Playlist - - - http://www.youtube.com/watch?v=LP_5SX9lNN4&list=PLR4wfoQ4HbynZqdtxFmOBRCpUfAvdgDi8 Competitive ELISA Tutorials - - - http://www.youtube.com/watch?v=Kb26nQVMHds&list=PLR4wfoQ4HbymisBtft-i-QTsyRDMFymC3 ELISA Tutorials - - - http://www.youtube.com/watch?v=nNjlBCnpGZ4&list=PLR4wfoQ4HbynbS01zeuBV-awsOAxDPhYO Gelatin Zymography Tutorials - - - http://www.youtube.com/watch?v=MF2sWQSaBWg&list=PLR4wfoQ4Hbykrj7rxk6i5jzkjtvTtZUVx
Views: 156510 protocolplace
SPSS for newbies: Data entry for multiple response questions, "Tick boxes that apply" ✔
 
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Questionnaire data entry in SPSS: multiple response questions - frequency and graph CONTENT 1:58 Examples of questions with multiple (many) answers - dichotomies and categories 6:53 Dichotomies type question - data entry and summary stats: data entry, frequencies , % of response v % Cases 18:15 Multiple response sets (why use and how to create them) 22:25 Dealing with questions with no ticks in boxes but that are not missing values 25:34 How to make bar charts in SPSS Date: 8 August, 2013
Views: 283267 Phil Chan
t-test in Microsoft Excel
 
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How to perform a simple t-test in Microsoft Excel
Views: 1106308 Jim Grange
How to Analyze Satisfaction Survey Data in Excel with Countif
 
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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: 345805 Ann K. Emery
Working with Variables and Data in R | R Tutorial 1.8 | MarinStatslectures
 
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Working with Variables and Data in R and Produce Summaries; Practice with Dataset: https://goo.gl/tJj5XG Install R and RStudio Video https://bit.ly/2PYO8jS More Statistics and R ProgrammingTutorials here: https://goo.gl/4vDQzT How to check variable names, variable types, extract a variable from a dataset, and produce summaries for data based on the type of data in R. ▶︎ How to check variable names for datasets in R? We will use names function in R ▶︎ How to extract a variable from a dataset in R? We will learn to use $ or Attach function in R ▶︎ How to check the variable type (numeric or categorical) in R? We will use class function in R ▶︎ How to ask R for different levels/categories of a categorical variable? levels function in R will be used ▶︎ How to produce summary for variable in R? summary function in R will produce summary of variables based on they type, for example numeric values will be summarized by mean, median and quartiles, and factors or categorical variables will be summarized as frequencies. ▶︎▶︎Download the dataset here: https://statslectures.com/r-stats-datasets ▶︎▶︎Watch More: ▶︎Getting Started with R: https://bit.ly/2PkTneg ▶︎Graphs and Descriptive Statistics in R: https://bit.ly/2PkTneg ▶︎Probability distributions in R: https://bit.ly/2AT3wpI ▶︎Bivariate analysis in R: https://bit.ly/2SXvcRi ▶︎Linear Regression in R: https://bit.ly/1iytAtm ▶︎Intro to Statistics Course: https://bit.ly/2SQOxDH ◼︎ Table of Content 0:01:06 How to use the dollar sign "$" to extract the variable within a dataset in R 0:02:25 How to make objects/variables within a data frame accessible in R? introducing the "attach" function 0:03:20 How too un-attach the data in R? working with the "detach" function 0:04:04 How to check the type or class of a variable in R? using the "class" function in R 0:05:04 How to use the "levels" function in R to find out the different levels/ categories for a factor/categorical data 0:05:34 How to produce summaries for data in R? learn to use the "summary" function in R 0:06:30 How to convert a numeric variable to categorical/factor variable in R using "as.factor" function This video is a tutorial for programming in R Statistical Software for beginners. Follow MarinStatsLectures Subscribe: https://goo.gl/4vDQzT website: https://statslectures.com Facebook:https://goo.gl/qYQavS Twitter:https://goo.gl/393AQG Instagram: https://goo.gl/fdPiDn Our Team: Content Creator: Mike Marin (B.Sc., MSc.) Senior Instructor at UBC. Producer and Creative Manager: Ladan Hamadani (B.Sc., BA., MPH) These videos are created by #marinstatslectures to support a course at The University of British Columbia (UBC) (SPPH400: #IntroductoryStatistics and #RTutorial for Health Science Research), although we make all videos available to the public for free. Thanks for watching! Have fun and remember that statistics is almost as beautiful as a unicorn!
Understanding the p-value - Statistics Help
 
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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: 712300 Dr Nic's Maths and Stats
Panel Data Models with Individual and Time Fixed Effects
 
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An introduction to basic panel data econometrics. Also watch my video on "Fixed Effects vs Random Effects". As always, I am using R for data analysis, which is available for free at r-project.org Link to the data: http://www.burkeyacademy.com/my-forms/Panel%20Data.xlsx Link to previous video: http://www.youtube.com/watch?v=ySTb5Nrhc8g Support this project on Patreon! https://www.patreon.com/burkeyacademy Or, a one-time donation on PayPal is appreciated! http://paypal.me/BurkeyAcademy My Website: http://www.burkeyacademy.com/ Talk to me on my SubReddit: https://www.reddit.com/r/BurkeyAcademy/
Views: 188345 BurkeyAcademy
Math Antics - Mean, Median and Mode
 
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Learn More at mathantics.com Visit http://www.mathantics.com for more Free math videos and additional subscription based content!
Views: 769811 mathantics
Enter data and define variables in SPSS
 
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How to define variables and enter data into SPSS (v20) ASK SPSS Tutorial Series
Views: 408246 BrunelASK

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