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13-1 Relationships Between Variables
 
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The topic of correlation is one of the most enjoyable parts of statistics, because everyone can understand correlation. We all know what it is to have relationships with other people and we know how our behavior can change when we are around others. Except that instead of relationships between people, we are describing relationships between variables.
Views: 3904 RStatsInstitute
Examining Relationships: Junk Science  Correlation  Causation
 
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Part of Entry Document for Statistics. Video used for educational purposes only.
Views: 168 Jazmine Castanon
AP Statistics: Scatterplots, Association, Correlation - Part 1
 
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This video covers the basis of examining the relationship between two quantitative variables.
Views: 29704 Michael Porinchak
Independent, Dependent and Confounding Variables in Quantitative Research
 
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http://youstudynursing.com/ Research eBook on Amazon: http://amzn.to/1hB2eBd Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam Related Videos: http://www.youtube.com/playlist?list=PLs4oKIDq23AdTCF0xKCiARJaBaSrwP5P2 Connect with me on Facebook: https://www.facebook.com/NursesDeservePraise Twitter: @NurseKillam https://twitter.com/NurseKillam Research questions should clearly identify the variables under study. In the above examples X symbolizes the independent variable and Y symbolizes the dependent variable. In this video we are going to examine the question "Is there a difference in GPA between nursing students who watch NurseKillam's videos and those who do not watch NurseKillam's videos?" The independent variable is the one that researchers think will have an effect on the dependent variable or variables under study. If the study is experimental the researcher will manipulate this variable. This manipulation means that the researchers will cause the variable to happen in a group of people. For example, if we used our question for an experimental study the researcher would show NurseKillam's videos to one group of students while ensuring that the other group of students did not see them. If the study is non-experimental the variable is assumed to happen naturally before or during the study. Instead of making some students watch the videos the researchers would measure or observe if the videos were watched. One way to do this would be to survey students to see if they had watched the videos. Like the name suggests, the dependent variable is assumed to be a result of or change based on the presence, absence or magnitude of the independent variable. One way to remember this relationship is that the outcome of the dependent variable depends on the independent variable. The question is worded in a way that identifies the variables to be studied. In our example the two variables are GPA and watching NurseKillam's videos. It would not make sense for an increased GPA to cause students to watch the videos. However, watching the videos may cause an increase a student's GPA. Therefore, the dependent variable in our example is the GPA. The one that is assumed to cause the change in the GPA is the independent variable. Even though a higher GPA may be assumed to be a result of watching NurseKillam's videos a causal relationship between the two variables cannot necessarily be proven. Instead, a relationship can be identified but awareness of other factors needs to be discussed. When examining the relationship between the independent and dependent variables researchers must also be aware of and control for as many confounding variables as possible. Confounding variables include anything that may confuse or confound the relationship that is being examined. It is because of confounding variables that non-experimental research cannot prove cause and effect relationships. Researchers also need to be careful not to claim cause and effect relationships too easily in experimental research because of these confounding variables. In experimental research an attempt is made to control as many confounding variables as possible. In our example question, what things other than watching NurseKillam's videos may cause an increased GPA? Anything other than the independent variable that could have caused the student's GPA to increase would be a confounding variable. To remember what these types of variables are and how to identify them think about relationships. The dependent variable is the one that depends on something and is expected to change. Independent variables, like independent people do not rely on others. Anything that may confuse this relationship is confounding. There are a couple of messages I want you to take from this example: 1) Please note that research questions may include many variables -- not just one independent and one dependent variable. 2) whether a particular variable like class attendance is independent or dependent may change depending on the role it plays in any given study.
Views: 102735 NurseKillam
Statistics Making Sense of Data Examining Relationships Between Two Categorical Variables
 
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Statistics Making Sense of Data Examining Relationships Between Two Categorical Variables
Views: 235 LummoMy
pandas best practices (4/10): Examining relationships
 
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This is part 4 of my pandas tutorial from PyCon 2018. Watch all 10 videos: https://www.youtube.com/playlist?list=PL5-da3qGB5IBITZj_dYSFqnd_15JgqwA6 This video covers the following topics: value counts, math with booleans, groupby multiple columns, correlation versus causation. NEW TO PANDAS? Watch my introductory series (30+ videos): https://www.youtube.com/playlist?list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y DOWNLOAD the dataset and notebook: https://github.com/justmarkham/pycon-2018-tutorial SUBSCRIBE to learn data science with Python: https://www.youtube.com/dataschool?sub_confirmation=1 LET'S CONNECT! - Newsletter: http://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/
Views: 2836 Data School
Pearson Correlation - SPSS
 
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I demonstrate how to perform and interpret a Pearson correlation in SPSS.
Views: 595284 how2stats
Relationships Between Two Variables: Scatterplots
 
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Discussion of relationships between two variables and an important tool for interpretting them (scatterplots), including how to interpret form, strength, direction, and outliers.
Views: 5660 Dan Ross
Junk Science Episode 10: Correlation / Causation
 
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When two data point sync up, it’s seems intuitive that they might be related, but in science that is far from the case. Subscribe! http://www.youtube.com/subscription_center?add_user=vocativvideo See more on our website: http://www.vocativ.com Follow us on Twitter: https://twitter.com/vocativ Like us on Facebook: https://www.facebook.com/Vocativ
Views: 17815 Vocativ
CCA
 
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CCA
Using CCA in PAST to examine patterns in taxa abundances (e.g. among samples or sites) and relate these to environmental variables.
Views: 6111 Keith McGuinness
Relationships between numeric variables
 
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We look at scatterplots and ways of viewing and interpreting them. After you’ve watched this video, you should be able to answer these questions •What is the standard way of displaying the relationship between 2 numeric variables? •What sort of variable is plotted against the vertical scale and what against the horizontal scale? •It is often useful to think of patterns in such plots in terms of 3 components. What are they? •What type of question should separate clusters of dots suggest to us?
Views: 1482 Wild About Statistics
Interpreting correlation coefficients in a correlation matrix
 
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/learn how to interpret a correlation matrix. http://youstudynursing.com/ Research eBook: http://amzn.to/1hB2eBd Related Videos: http://www.youtube.com/playlist?list=PLs4oKIDq23Ac8cOayzxVDVGRl0q7QTjox A correlation matrix displays the correlation coefficients among numerous variables in a research study. This type of matrix will appear in hypothesis testing or exploratory quantitative research studies, which are designed to test the relationships among variables. In order to interpret this matrix you need to understand how correlations are measured. Correlation coefficients always range from -1 to +1. The positive or negative sign tells you the direction of the relationship and the number tells you the strength of the relationship. The most common way to quantify this relationship is the Pearson product moment correlation coefficient (Munro, 2005). Mathematically it is possible to calculate correlations with any level of data. However, the method of calculating these correlations will differ based on the level of the data. Although Pearson's r is the most commonly used correlation coefficient, Person's r is only appropriate for correlations between two interval or ratio level variables. When examining the formula for Person's r it is evident that part of the calculation relies on knowing the difference between individual cases and the mean. Since the distance between values is not known for ordinal data and a mean cannot be calculated, Pearson's r cannot be used. Therefore another method must be used. ... Recall that correlations measure both the direction and strength of a linear relationship among variables. The direction of the relationship is indicated by the positive or negative sign before the number. If the correlation is positive it means that as one variable increases so does the other one. People who tend to score high for one variable will also tend to score high for another varriable. Therefore if there is a positive correlation between hours spent watching course videos and exam marks it means that people who spend more time watching the videos tend to get higher marks on the exam. Remember that a positive correlation is like a positive relationship, both people are moving in the same direction through life together. If the correlation is negative it means that as one variable increases the other decreases. People who tend to score high for one variable will tend to score low for another. Therefore if there is a negative correlation between unmanaged stress and exam marks it means that people who have more unmanaged stress get lower marks on their exam. Remember that A negative correlation is like a negative relationship, the people in the relationship are moving in opposite directions. Remember that The sign (positive or negative) tells you the direction of the relationship and the number beside it tells you how strong that relationship is. To judge the strength of the relationship consider the actual value of the correlation coefficient. Numerous sources provide similar ranges for the interpretation of the relationships that approximate the ranges on the screen. These ranges provide guidelines for interpretation. If you need to memorize these criteria for a course check the table your teacher wants you to learn. Of course, the higher the number is the stronger the relationship is. In practice, researchers are happy with correlations of 0.5 or higher. Also note that when drawing conclusions from correlations the size of the sample as well as the statistical significance is considered. Remember that the direction of the relationship does not affect the strength of the relationship. One of the biggest mistakes people make is assuming that a negative number is weaker than a positive number. In fact, a correlation of -- 0.80 is just as high or just as strong as a correlation of +0.80. When comparing the values on the screen a correlation of -0.75 is actually stronger than a correlation of +0.56. ... Notice that there are correlations of 1 on a diagonal line across the table. That is because each variable should correlate perfectly with itself. Sometimes dashes are used instead of 1s. In a correlation matrix, typically only one half of the triangle is filled out. That is because the other half would simply be a mirror image of it. Examine this correlation matrix and see if you can identify and interpret the correlations. A great question for an exam would be to give you a correlation matrix and ask you to find and interpret correlations. What is the correlation between completed readings and unmanaged stress? What does it mean? Which coefficient gives you the most precise prediction? Which correlations are small enough that they would not be of much interest to the researcher? Which two correlations have the same strength? From looking at these correlations, what could a student do to get a higher mark on an exam? Comment below to start a conversation.
Views: 45467 NurseKillam
Statistics Making Sense of Data Relationships Between Quantitative and Categorical Variables
 
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Relationships Between Quantitative and Categorical Variables
Views: 210 LummoMy
Relationships between categorical variables
 
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In this video you’ll learn how to plot data on two categorical variables so that you can look for relationships between them. After you’ve watched this video, you should be able to answer these questions •We distinguished between an outcome variable and predictor variables. What is an outcome variable and what are predictor variables? •What are the strengths and weaknesses of using separate bar graphs of a response variable for each predictor group? •What are the strengths and weaknesses of using side-by-side bar graphs to show the relationship between a response variable and a predictor variable? •Which type of graph should you look at? •In terms of separate bar graphs for each predictor group, what would you expect to see if there was no relationship between the outcome variable and the predictor variable? •What are you looking for when you use side by side bar charts?
Views: 4937 Wild About Statistics
Stats 3.1a Examining Relationships
 
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Made with Doodlecast Pro from the iTunes App Store. http://www.doodlecastpro.com
Views: 1173 Jeremy Haselhorst
Examining Relationships Between Gas Fluxes and Plant Zones
 
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Tuesday 12:00pm Speaker: Serena Moseman-Valtierra, URI
Views: 62 WaquoitBayReserve
Relationship between categorical variables in a 2 way table
 
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I show you how to analyze catagorical data in a 2 way table. We will find marginal distributions, conditional probabilities, and bar graphs. Find free review test, useful notes and more at http://www.mathplane.com
Views: 7280 ProfRobBob
Simple Linear Regression:  Checking Assumptions with Residual Plots
 
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An investigation of the normality, constant variance, and linearity assumptions of the simple linear regression model through residual plots. The pain-empathy data is estimated from a figure given in: Singer et al. (2004). Empathy for pain involves the affective but not sensory components of pain. Science, 303:1157--1162. The Janka hardness-density data is found in: Hand, D.J., Daly, F. , Lunn, A.D., McConway, K., and Ostrowski, E., editors (1994). The Handbook of Small Data Sets. Chapman & Hall, London. Original source: Williams, E.J. (1959). Regression Analysis. John Wiley & Sons, New York. Page 43, Table 3.7.
Views: 122740 jbstatistics
Bivariate Analysis: Categorical and Numerical (ANOVA Test)
 
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How to do Bivariate Analysis when one variable is Categorical and the other is Numerical Analysis of Variance ANOVA test My website: http://people.brunel.ac.uk/~csstnns
Views: 6422 Noureddin Sadawi
Relationship of Two Categorical Variables ☆ Statistics Lecture
 
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lll➤ Gratis Crypto-Coins: https://crypto-airdrops.de ) More about the relationship of two Categorical variables in this Accelerated Statistics Lecture. That´s what you will learn in this lesson. Also have a look at the other parts of the course, and thanks for watching. In statistics, a categorical variable is a variable that can take on one of a limited, and usually fixed, number of possible values. In computer science and some branches of mathematics, categorical variables are referred to as enumerations or enumerated types. Commonly (though not in this article), each of the possible values of a categorical variable is referred to as a level. http://en.wikipedia.org/wiki/Categorical_variable This video was made by another YouTube user and made available for the use under the Creative Commons licence "CC-BY". His channel can be found here: https://www.youtube.com/user/mathrapper
Analyzing trends in categorical data | 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/categorical-data/e/trends-in-categorical-data?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Watch the next lesson: https://www.khanacademy.org/math/probability/statistical-studies/categorical-data/v/frequency-table-independent-events?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/statistical-studies/categorical-data/v/video-games-and-violence-bivariate-data?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: 111155 Khan Academy
What Is A Confounding Variable?
 
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Confounding variables (aka third variables) are variables that the researcher failed to control, or eliminate, damaging the internal validity of an experiment. What is a confounding variable? Youtubeassessing bias the importance of considering confoundingextraneous and variables3. Confounding occurs when the experimental controls do not allow experimenter to reasonably eliminate plausible alternative explanations for an observed relationship between independent and dependent variablesa drug manufacturer tests a new cold medicine with 200 volunteer statistics can be powerful tool in helping researchers understand solve problems. Confounding variable simple definition and example statistics confounding wikipedia. This extraneous influence is used to the outcome of an experimental design. Bias, confounding and effect modification. If you think you've got it, then this isn't the post for. Confounding variable third explorable what is a confounding variable? Psychology in actionconfounding variables statistics definition & examples video and example. If the influence of such factors is not considered in design experiment or analysis results, outcome could show an effect factor you are testing that real no when it really has examining relationship between explanatory and outcome, we interested identifying may modify factor's on (effect modifiers). But if you're confounded by the idea of a confound, confounding. In order to achieve the a confounded or confounding variable (also called confound) is one that varies (changes) with an independent. Hundreds of step by statistics videos and articles in statistics, a confounder (also confounding variable or factor) is that influences both the dependent independent causing spurious association. Confounding variable (also called a confound) is one that varies (changes) with an independent. Definition of confounding variable? (psychology) the student roomlrd dissertation. Simply, a confounding variable is an extra entered into the equation that was not accounted for. Confounding 30 oct 2011 whether you're conducting research, reading about or learning research methods so you can ace your course, need to know exactly what a confounding variable is. 718 entry terms confounding factor (epidemiology) factor, confounding extraneous and confounding variables and systematic vs non systematic error experimental psychology modules extraneous variables are undesirable variables that influence the relationship between the variables that an experimenter is examining. 15 nov 2017 definition for confounding variable in plain english. Year introduced 1990 tree number(s) n05. Therefore, if changing the independent variable change a dependent variable, then you cannot tell which or confounded produced change, because definition of confounding variable? (psychology) student room. I'm not so sure about that bit. Confounding variable examples softschools. Confounding is a causal concept, and as such, cannot be described in terms of
Views: 53 Christen Vaca Tipz
Dependent and Independent Variables - X or Y - Science & Math - Linear, Inverse, Quadratic
 
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This math and science video tutorial shows you how to identify the dependent and independent variables so you which data to plot on the x axis and on the y-axis. This video is useful for kids taking physics and chemistry. It contains plenty of examples and practice problems such as word problems. It shows you if the dependent variable and independent variable is x or y and it discusses the relationship of certain graphs such as linear curves, inverse relationships, and quadratic or parabolic functions. Examples include Newton's second law of motion between force, mass, and acceleration, boyle's law of pressure and volume in chemistry, and distance vs time under constant acceleration in physics. It also explains the difference between the control group and the experimental group. The slope of the line for the force mass example is acceleration.
Crosstabulations and their Interpretation. Part 1 of 2 on Crosstabulations and Chi-square
 
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A lecture on using tables and crosstabulations in quantitative research by Graham R Gibbs taken from a series on quantitative data analysis and statistics given to undergraduate students at the University of Huddersfield. This is part 1 of 2 and covers using crosstabulations or contingency tables to examine the relationship between two categorical variables. Credits: Music: Kölderen Polka by Tres Tristes Tangos is licensed under an Attribution-ShareAlike 3.0 International License. http://freemusicarchive.org/music/Tres_Tristes_Tangos/ Image: Ice-ferns by Schnobby, Wikimedia Commons, licensed under the Creative Commons Attribution-Share Alike 3.0 Unported license.
Views: 31420 Graham R Gibbs
Are The Control And Constant The Same Thing?
 
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I say one factor because usually in an experiment you try to change thing at a time 9 oct 2015 variables that are constant among the control and groups type, water, sun exposure will all remain same for every group controlled things keep of your experiments. Examples of controlled variables might include using the things that are changing in an experiment called. A control variable is any factor you or hold constant during an experiment. There still needs to be constant monitoring and checks, but due diligence will 25 aug 2011 fyi controlled variables are not the same as a control for your investigation. Instead, they must control for variables using statistics. Differences between independent variable, dependent variable is there any different 'control variable' and researchgatewhat's the difference an controlled variables in your science fair project buddiescontrolling for a wikipedia. Control, constant, dependent and an 16 sep 2015 controlled variable have nothing done to it. Controlled variables are quantities that a scientist wants to remain constant, and she so, you should keep all the other same (you control them) so variable (or scientific constant) in experimentation is experimental essentially, what kept throughout experiment, it not of primary concern outcome statistics, controlling for attempt reduce effect confounding on an observational study. 25 apr 2017 definitions of control, constant, independent and dependent while those that stay the same, such as acceleration due to gravity at a certain 25 apr 2017 while different in nature, controls and constants serve the same purpose. Difference between a control variable and group. Just remember that variables are things can change. The items in an experiment that are kept the same identified as controls 1 oct 2015 control group (sometimes called a comparison group) is used conditions must be exactly for all members. Controlled variables the scientists tries to avoid interferencevancleave's. The only difference between members must be the item or thing you are and other important factors held constant for every member in group controlled variables that is often overlooked by researchers. Control, constant, dependent and an definitions of control, independent what are constants & controls a science project experiment is the difference between constant control in scientific method vs youtube. Dependent variable you observed measured. Cycle are just some of the variables that must be same between experiments. They reveal the impact of variables in an experiment by eliminating i always thought they were same thing. A control variable is also 12 dec 2012 independent variable, dependent constant, and c a constant that can are kept to be the same moderating starting at time as iv in which both should held order examine relationship controlled variables, sometimes called variables try maintain pressure maybe volume when spraying your plants. It means that when looking at the effect of one varia
Views: 158 Your Question I
The difference between Concepts Models and Theories
 
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Get my eBook "Research terminology simplified: Paradigms, axiology, ontology, epistemology and methodology" on Amazon: http://amzn.to/1hB2eBd OR the PDF version here: http://books.google.ca/books/about/Research_terminology_simplified.html?id=tLMRAgAAQBAJ&redir_esc=y http://youstudynursing.com/ Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam Related Videos: http://www.youtube.com/watch?v=BZw-OXXEUlQ&feature=share&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 Knowing the difference between concepts, models and theories will help you understand research and communicate intelligently with other professionals. There are a number of ways to define concepts. Essentially a concept is a description of an event, situation or experience. Typically these descriptions of concepts are abstract and complex. Some concepts are more understood than others. It is important to define concepts so we can communicate. For example, my husband and I have different views on what defines a garage and a shed. Since we own properties with both our different definitions have caused some communication issues. When I talk about needing to clean the garage sometimes the wrong building gets cleaned. Research studies should present a conceptual or theoretical framework in the introduction portion of the article. These frameworks tell you where the researcher is coming from and define the concepts under study. It helps to know how a researcher views the concepts under study so you can determine if you or other studies you are looking at define these concepts in the same way. That way you know if they are studying a shed or a garage. Sometimes researchers will describe what is known about the concepts without a model or theory. In this case it is called a conceptual framework. Other times they will present at specific model or theory to describe how the concepts that they will be studying relate to one another. However, before models and theories can be developed, concepts need to be defined. There are specific methods to define concepts for research. The methods chosen will depend on your beliefs about the nature of the concept. If you believe concepts are concrete, measurable and do not change your approach will search for the truth about the concepts. Concepts that are defined in this way may be the variables under study in quantitative research. However, if you think that concepts are influenced by context, recognisable (but not measurable) and dynamic your approach would be much different. Concepts that are defined in this way would be more congruent as a framework for qualitative research. To find out more about how concepts are developed please visit the video on your screen. Often research will examine the relationships between and among various concepts. A framework that shows these relationships may be in the form of a model or a theory. Models are usually developed based on qualitative research. They demonstrate the researcher's interpretation of how concepts are related to one another. You may find models in the findings section of some research articles. Sometimes models are based on understandings that are not from specific research studies. For example, the picture on this slide represents a model of my understanding of the difference between concepts, models and theories. Theories on the other hand are more tested than models. They are systematic and are used to explain, predict, describe and prescribe phenomena. They look a lot like models so sometimes people confuse these two terms. Theories are tested and measured, most of the time with quantitative research, in order to prove the relationships between concepts. It takes a lot more than one research study to prove a theory. Qualitative research can also be done to support theories. The more research that supports a theory the better it is. Remember, Concepts need to be defined in order to build a model or a theory. Both models and theories show proposed relationships between concepts. The difference between a model and a theory is the amount of "proof" that exists for them, which stems from how they were developed. Models are not considered proven. Theories are considered proven and supported by multiple research studies. That is why they are viewed as a more systematic representation of phenomena. For more please visit the videos on this slide and subscribe. Thank you for watching. Don't forget to like this video if you found it helpful. Music from http://www.freestockmusic.com/
Views: 84412 NurseKillam
How to Show That Two Variables are Correlated
 
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In this video I will show you a simple method which you can use to determine if two variables are likely to be correlated. This is a good first port of call and typically works well for sociology and psychology. The calculation of Pearson's r can easily be done with built in functions in excel and other programs like OpenOffice Calc (which is used here). A scientific calculator is also able to easily perform the calculation with a built in function. However, it is likely to miss more complex relationships (i.e. logarithmic relations). Thus this method is good for showing that variables are likely to be correlated but not very good at proving rigorously that they uncorrelated.
R - Exploring Data (part 4) - Bivariate Summaries
 
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We continue to discuss the used cars data from part 1,2, and r of this Module. Here we start looking at some relationships among the features in our data. We create a scatterplot and side-by-side boxplot.
Views: 7102 Jalayer Academy
Factor Variables - Data Analysis with R
 
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This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 4941 Udacity
Introduction to Production Introduction (Average Product, Marginal Product, Total Product)
 
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Visual explanation of Production Theory, Total Product, Average Product, and Marginal Product of Labor used in economics classes. This is the first of three videos on the play list. Like us on: http://www.facebook.com/PartyMoreStudyLess
Views: 119289 Economicsfun
R coplot: visualizing the interaction between two continuous variables
 
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Using the coplot package to visualize interaction between two continuous variables
Views: 974 DWR447
Correlations: are 2 variables related??
 
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Does eating more relate with gaining weight?? this is the type of question we can answer using a correlation. In this video I show you how to do this on SPSS. Its all done in 2 mins; its entertaining... my words it is funny..What stats are funny?? No stats are fabulously useful tools that allow researchers to systematically examine relationships between variables. The presentation of the idea is funny...
Views: 192 Andy Lane
Normal Distribution - Explained Simply (part 1)
 
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I describe the standard normal distribution and its properties with respect to the percentage of observations within each standard deviation. I also make reference to two key statistical demarcation points (i.e., 1.96 and 2.58) and their relationship to the normal distribution. Finally, I mention two tests that can be used to test normal distributions for statistical significance. normal distribution, normal probability distribution, standard normal distribution, normal distribution curve, bell shaped curve
Views: 911989 how2stats
Lock5 Statistics: One Quantitative Variable: Measures of Spread (Marist Professor Carla Hill)
 
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Lock5 Statistics: One Quantitative Variable: Measures of Spread (Marist Professor Carla Hill)
Views: 312 MaristMedia
Categorical Response Variables in R
 
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Learn all about statistical modeling at https://www.datacamp.com/courses/statistical-modeling-in-r-part-2 In the previous segment, we talked about effect sizes. An effect size is a number that summarizes how the output of a model changes when we change the input. When we are looking at the effect of a quantitative input X on the output Y, the effect size is a rate, and has units of Y divided by X. But for an effect size involving a categorical input on an output Y, the effect size is a difference and has the same units as Y. What happens when the response variable is categorical, that is, when the output is one of a set of named levels instead of a number? This is more than a technical question. It goes to the heart of what should be the output of the model function for a categorical response variable. It turns out that providing a category as output, while natural, is very limiting. Better to give a number or set of numbers: the probabilities according to the model, of the class of interest or of all the classes. [[3.05B]] As an example, consider a model of the categorical variable married as a function of explanatory varibles like age, education and sex. As always, we need to have a model from which to calculate the effect size. We'll compare the model output for two different ages. [[3.06]] As you can see, the output is the same for both ages. Does this mean that the effect size of age on married is zero: no effect of age? Not really. Changes in categorical outputs are all or nothing: either a change or no change at all. It's as if we were tracking one individual over the years: "no change this year", "no change the next year", "still no change", "finally, a change". But our models are really about groups. For any individual, marriage is all or nothing, but for groups we can talk about the probability of an individual being married. [[3.07]] Many model architectures for categorical outputs do calculate the probability of each possible level of the output. The model indicates that an extra year of age is associated with a 16 percentage point increase in the probability of being married.
Views: 1574 DataCamp
Linear Correlation - 33 Coefficient of Correlation of Grouped Data
 
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#Linear Correlation #Coefficient of Correlation for Grouped Data #Coefficient of Correlation for Bivariate Frequency Distribution Case Distribution of marks out of 70 in an examination: Marks in Marks in Physics Maths 10-20 20-30 30-40 40-50 50-60 15-25 6 3 - - - 25-35 3 16 10 - - 35-45 - 10 15 7 - 45-55 - - 7 10 4 55-65 - - - 4 5 Find out the coefficient of correlation. MBA, MCA, BE, CA, CS, CWA, CMA, CPA, CFA, BBA, BCom, MCom, BTech, MTech, CAIIB, FIII, Graduation, Post Graduation, BSc, MSc, BA, MA, Diploma, Production, Finance, Management, Commerce, Engineering - www.prashantpuaar.com
Views: 14579 Prashant Puaar
Categorical Variables or Factors in Linear Regression in R (R Tutorial 5.7)
 
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Learn how to include a categorical variable (a factor or qualitative variable) in a regression model in R. You will also learn how to interpret the model coefficients. The video provides a tutorial for programming in R Statistical Software for beginners. You can access and download the "LungCapData" dataset here: Excel format: https://bit.ly/LungCapDataxls Tab Delimited Text File: https://bit.ly/LungCapData Here is a brief overview of the topics addressed in this video:
Views: 59135 MarinStatsLectures
Create and Graph Stock Correlation Matrix  Python and Pandas
 
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http://alphabench.com/data/python-correlation-tutorial.html Quickly download data for any number of stocks and create a correlation matrix. It may take me 10 minutes to explain, but it will only take you 3 to see the power of Python for downloading and exploring data quickly primarily utilizing Numpy and Pandas A copy of the notebook used is available at the link above Download the Anaconda Platform at: https://www.anaconda.com/
Views: 526 Matt Macarty
Interactions among explanatory variables in R
 
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Learn more about statistical modeling at https://www.datacamp.com/courses/statistical-modeling-in-r-part-2 In thinking about effect size, keep in mind that there is not necessarily a single effect size for each explanatory variable. Often, the effect size of one variable depends on the value of the other variables. This plot shows the probability of being married for the people in the CPS85 data set as a function of age, education, and sector. The effect size of sex --- that is, the difference between marriage rates for men and women --- is big in the clerical sector, but small in the service sector. Young men in the professional sector are more likely to be married than young men in the other sectors. It's as if the various explanatory variables work together to determine the effect size of sex. There's a name for this: statisticians call it an interaction effect. It might seem obvious that the effect size of one explanatory variable might depend on the levels of the other explanatory variables. But it's easy to get confused. Many people end up thinking that the interaction is how one explanatory variable shapes or causes another explanatory variable. In some model architectures such as lm(), you will only see an interaction effect if you ask specifically for it. In some other architectures, such as rpart(), the interaction is just an ordinary part of the story. Historically, the lm() architecture has dominated scientific research, and so the decision of whether to include an interaction effect is relevant. Let's take a moment to examine how interaction effects are included in lm() models. As an example, consider a simple story: world records in the 100-m freestyle swim race. The graph shows these records over the course of the 20th century. Two features are evident from the data points: Swimmers have gotten faster over the years. Men's records are faster than women's. And, even without formally training a model, you may be able to see that the effect size of sex --- the difference between men and women --- has gotten smaller over the years. That change in effect size is an interaction effect. Let's look at two different models of the swim-record data: one having an lm() architecture and the other an rpart() architecture. The rpart model clearly shows an interaction effect; the difference between the sexes changes over the years. But the step-wise nature of the model is jarring. The model output changes only over decades, not years. The linear model is more satisfactory, showing a gradual improvement in record times. But there's no interaction effect: the two model lines are parallel; the model says records are getting better at the same rate for men and women. The reason the linear model doesn't show the interaction is because it never does unless you ask for it specifically. You ask for an interaction effect to be included by using a model formula with a star to connect the variables you want to involve in the interaction. So, here, the model formula says "sex star year" rather than "sex plus year". You can see the interaction in the graph in two ways: the effect size of sex decreases with year and, equivalently, the slope giving the effect size with year is different for the two sexes. There are good reasons why the lm() architecture includes interactions only if you specifically ask for them. To a large extent, this has to do with the demands of small data sets. That's a subject for another course. But at this point it's entirely adequate to work with some rules of thumb: rpart() includes interactions naturally as part of the way they work lm() and other model construction methods that we haven't yet discussed (such as glm()) will include interactions only if you ask for them. Including interactions sometimes help, sometimes don't help, and sometimes hurts the performance of a model. When in doubt, cross validation is a good way to assess performance, so you always have a way to decide whether interactions are helping or not.
Views: 2815 DataCamp
The puzzle of motivation | Dan Pink
 
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http://www.ted.com Career analyst Dan Pink examines the puzzle of motivation, starting with a fact that social scientists know but most managers don't: Traditional rewards aren't always as effective as we think. Listen for illuminating stories -- and maybe, a way forward. TEDTalks is a daily video podcast of the best talks and performances from the TED Conference, where the world's leading thinkers and doers give the talk of their lives in 18 minutes. TED stands for Technology, Entertainment, Design, and TEDTalks cover these topics as well as science, business, development and the arts. Closed captions and translated subtitles in a variety of languages are now available on TED.com, at http://www.ted.com/translate. Follow us on Twitter http://www.twitter.com/tednews Checkout our Facebook page for TED exclusives https://www.facebook.com/TED
Views: 7289064 TED
Stacked Bar Charts, Grouped Bar Charts and Mosaic Plots in R (R Tutorial 2.5)
 
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Learn how to produce and customize "stacked bar charts", "clustered bar charts" and "mosaic plots" for examining the relationship between two categorical variables in R. You will learn to use the "table", "barplot", "mosaicplot", "beside",and "legend" commands. This video is a tutorial for programming in R Statistical Software for beginners. You can access and download the "LungCapData" dataset here: Excel format: https://bit.ly/LungCapDataxls Tab Delimited Text File: https://bit.ly/LungCapData Here is a quick look at topics that are addressed in this video: 0:00:09 when is it appropriate to use "stacked bar charts", "clustered bar charts" and "mosaic plots" for our data 0:00:34 how to produce "stacked bar charts" in R using the "barplot" command 0:00:37 how to access the Help menu for "bar charts" in R 0:00:48 how to produce "contingency table" in R using the "table" command 0:01:22 how to produce "clustered bar charts" in R using "beside" argument 0:01:39 how to express the "bar chart" in terms of "conditional probabilities" 0:02:02 how to add a legend to "bar chart" using the "legend.text" argument 0:02:35 how to add a title to "bar chart" in R using the "main" argument 0:02:44 how to label the x-axis or y-axis of a "bar chart" using the "xlab" or "ylab" arguments 0:02:55 how to rotate the values on the y-axis of a "bar chart" by using the "las" argument 0:03:03 how to change the colours (color) of the bars in "bar chart" in R using the "col" argument 0:03:17 how to produce a "mosaic plot" in R using the "mosaicplot" command
Views: 79503 MarinStatsLectures
Examples on probability of a continuous random variable 2 (In Arabic)
 
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Examples on probability of a continuous random variable 2
Views: 183 CS with Dhemy