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Exploring relationships between categorical variables
 
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This video discusses numerical and graphical methods for exploring relationships between two categorical variables, using contingency tables, segmented bar plots, and mosaic plots.
Views: 26188 Mine Çetinkaya-Rundel
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: 5732 Research By Design
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: 109049 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: 258 LummoMy
Examining Relationships: Junk Science  Correlation  Causation
 
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Part of Entry Document for Statistics. Video used for educational purposes only.
Views: 180 Jazmine Castanon
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: 1539 Wild About Statistics
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 JOIN the "Data School Insiders" community and receive exclusive rewards: https://www.patreon.com/dataschool LET'S CONNECT! - Newsletter: http://www.dataschool.io/subscribe/ - Twitter: https://twitter.com/justmarkham - Facebook: https://www.facebook.com/DataScienceSchool/
Views: 5181 Data School
Stats 3.1a Examining Relationships
 
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Made with Doodlecast Pro from the iTunes App Store. http://www.doodlecastpro.com
Views: 1285 Jeremy Haselhorst
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. There is a sequel to give more practice choosing and illustrations of the different types of test with hypotheses.
Views: 713717 Dr Nic's Maths and Stats
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: 6989 Dan Ross
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: 7559 ProfRobBob
Examining Relationships
 
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Views: 1936 Robert Emrich
Relationship between Variables
 
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This is a lecture by Dr. Alemi on relationships among variables. This is part of the course on managerial statistics which is available without password protection at http://openonlinecourses.com/statistics
Views: 2120 Farrokh Alemi
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: 7697 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
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: 4234 DataCamp
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: 7785 Jalayer Academy
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: 91236 NurseKillam
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: 503145 Phil Chan
02 - Normal Distribution
 
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Materials Looking at Data - Distributions Slides: Looking at Data Lecture Normal Distributions Lecture Looking at Data - Relationships Slides: Looking at Data - Relationships Lecture Producing Data Slides: Producing Data Lecture Objectives Examine distributions. Summarize and describe the distribution of a categorical variable in context. Generate and interpret several different graphical displays of the distribution of a quantitative variable (histogram, stemplot, boxplot). Summarize and describe the distribution of a quantitative variable in context: a) describe the overall pattern, b) describe striking deviations from the pattern. Relate measures of center and spread to the shape of the distribution, and choose the appropriate measures in different contexts. Apply the standard deviation rule to the special case of distributions having the "normal" shape. Explore relationships between variables using graphical and numerical measures. Classify a data analysis situation (involving two variables) according to the "role-type classification," and state the appropriate display and/or numerical measures that should be used in order to summarize the data. Compare and contrast distributions (of quantitative data) from two or more groups, and produce a brief summary, interpreting your findings in context. Graphically display the relationship between two quantitative variables and describe: a) the overall pattern, and b) striking deviations from the pattern. Interpret the value of the correlation coefficient, and be aware of its limitations as a numerical measure of the association between two quantitative variables. In the special case of linear relationship, use the least squares regression line as a summary of the overall pattern, and use it to make predictions. Recognize the distinction between association and causation, and identify potential lurking variables for explaining an observed relationship. Recognize and explain the phenomenon of Simpson's Paradox as it relates to interpreting the relationship between two variables. Sampling. Examine methods of drawing samples from populations Identify the sampling method used in a study and discuss its implications and potential limitations. Designing Studies. Distinguish between multiple studies, and learn details about each study design. Identify the design of a study (controlled experiment vs. observational study) and other features of the study design (randomized, blind etc.). Explain how the study design impacts the types of conclusions that can be drawn.
Views: 1785 Lollynonymous
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: 2055 DataCamp
03 - Looking at Data Relationships
 
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Materials Looking at Data - Distributions Slides: Looking at Data Lecture Normal Distributions Lecture Looking at Data - Relationships Slides: Looking at Data - Relationships Lecture Producing Data Slides: Producing Data Lecture Objectives Examine distributions. Summarize and describe the distribution of a categorical variable in context. Generate and interpret several different graphical displays of the distribution of a quantitative variable (histogram, stemplot, boxplot). Summarize and describe the distribution of a quantitative variable in context: a) describe the overall pattern, b) describe striking deviations from the pattern. Relate measures of center and spread to the shape of the distribution, and choose the appropriate measures in different contexts. Apply the standard deviation rule to the special case of distributions having the "normal" shape. Explore relationships between variables using graphical and numerical measures. Classify a data analysis situation (involving two variables) according to the "role-type classification," and state the appropriate display and/or numerical measures that should be used in order to summarize the data. Compare and contrast distributions (of quantitative data) from two or more groups, and produce a brief summary, interpreting your findings in context. Graphically display the relationship between two quantitative variables and describe: a) the overall pattern, and b) striking deviations from the pattern. Interpret the value of the correlation coefficient, and be aware of its limitations as a numerical measure of the association between two quantitative variables. In the special case of linear relationship, use the least squares regression line as a summary of the overall pattern, and use it to make predictions. Recognize the distinction between association and causation, and identify potential lurking variables for explaining an observed relationship. Recognize and explain the phenomenon of Simpson's Paradox as it relates to interpreting the relationship between two variables. Sampling. Examine methods of drawing samples from populations Identify the sampling method used in a study and discuss its implications and potential limitations. Designing Studies. Distinguish between multiple studies, and learn details about each study design. Identify the design of a study (controlled experiment vs. observational study) and other features of the study design (randomized, blind etc.). Explain how the study design impacts the types of conclusions that can be drawn.
Views: 751 Lollynonymous
Math Antics - Exponents & Square Roots
 
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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: 980785 mathantics
Macroeconomics- Everything You Need to Know
 
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In this video I quickly cover all the concepts and graph that you will see in an AP macroeconomics or college-level introductory macroeconomics course. Dn't take notes. Just get the big picture. *Note* At 25:48, the signs are reversed. I talk about scarcity, opportunity cost, the PPC, comparative advantage, supply and demand, GDP, unemployment, inflation, aggregate demand and supply, LRAS, Phillips Curve, economic growth, fiscal policy, money, banking, monetary policy, the Money Market, loanable funds, the balance of payments, and exchange rates. Wow! That's a lot of stuff. Good luck on your test! Get the Ultimate Review Packet http://www.acdcecon.com/#!review-packet/czji Macroeconomics Videos https://www.youtube.com/watch?v=XnFv3d8qllI Microeconomics Videos https://www.youtube.com/watch?v=swnoF533C_c Watch Econmovies https://www.youtube.com/playlist?list=PL1oDmcs0xTD9Aig5cP8_R1gzq-mQHgcAH Follow me on Twitter https://twitter.com/acdcleadership
Views: 668509 Jacob Clifford
Beyond Where: Modeling Spatial Relationships and Making Predictions
 
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This workshop will cover regression analysis concepts for the analysis of geographic data. Using these statistical methods in many areas (e.g., business, public health, natural resources) allows you to examine, model, and explore data relationships to help answer questions such as “why do we see so much disease in particular areas?” Regression analysis also allows you to predict spatial outcomes for other places or time periods. Application and use of ordinary least squares regression (OLS) and geographically weighted regression (GWR) will be demonstrated. You will learn how to build a properly specified OLS model and interpret the results and diagnostics. The latest advancements in regression and prediction in ArcGIS will be covered.
Views: 1020 Esri Events
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: 34942 Graham R Gibbs
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: 51013 NurseKillam
Significance Testing  Contingency Tables and Correlations
 
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This webinar will describe and discuss two commonly used methods in criminal justice research and evaluation for examining the relationship between two categorical variables (contingency table analysis) or two continuous variables (correlation analysis). Examples will be presented in SPSS. JRSA research staff will deliver this webinar, the third in a new series of skill-building courses meant to provide a review of basic statistical and research methods.
Linear Regression in R | Linear Regression in R With Example | Data Science Algorithms | Simplilearn
 
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This "Linear regression in R" video will help you understand what is linear regression, why linear regression, you will see how linear regression works using a simple example and you will also see a use case predicting the revenue of a company using linear regression. Linear Regression is the statistical model used to predict the relationship between independent and dependent variables by examining two factors. The first one is which variables, in particular, are significant predictors of the outcome variable and the second one is how significant is the regression line to make predictions with the highest possible accuracy. Now, lets deep dive into this video and understand what is linear regression. Below topics are explained in this "Linear Regression in R" video: 1. Why linear regression? ( 00:28 ) 2. What is linear regression? ( 03:09 ) 3. How linear regression works? ( 03:48 ) 4. Use case - Predicting the revenue using linear regression (10:05) 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/HBso29 Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning Become an expert in data analytics using the R programming language in this data science certification training course. You’ll master data exploration, data visualization, predictive analytics and descriptive analytics techniques with the R language. With this data science course, you’ll get hands-on practice on R CloudLab by implementing various real-life, industry-based projects in the domains of healthcare, retail, insurance, finance, airlines, music industry, and unemployment. Why learn Data Science with R? 1. This course forms an ideal package for aspiring data analysts aspiring to build a successful career in analytics/data science. By the end of this training, participants will acquire a 360-degree overview of business analytics and R by mastering concepts like data exploration, data visualization, predictive analytics, etc 2. According to marketsandmarkets.com, the advanced analytics market will be worth $29.53 Billion by 2019 3. Wired.com points to a report by Glassdoor that the average salary of a data scientist is $118,709 4. Randstad reports that pay hikes in the analytics industry are 50% higher than IT The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice. 1. Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R. 2. Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing. 3. As a part of the data science with R training course, you will be required to execute real-life projects using CloudLab. The compulsory projects are spread over four case studies in the domains of healthcare, retail, and the Internet. Four additional projects are also available for further practice. The Data Science with R is recommended for: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Linear-Regression-in-R-2Sb1Gvo5si8&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/simplilearn/ - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 2832 Simplilearn
Logistic Regression with R: Categorical Response Variable at Two Levels (2015)
 
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Provides an example for carrying out logistic regression analysis with R. Includes, - use of a categorical binary output variable - misclassification errors or confusion matrix - cut-off values R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.
Views: 12526 Bharatendra Rai
The Science of Exposure and Metering
 
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This Filmmaker IQ course is proudly sponsored by Northeast Community College, whose Media Arts program offers degree concentrations in Audio Recording, Broadcasting, and Digital Cinema. Please check out their program by following the link: http://northeast.edu/media-arts/ Help this channel by becoming a patron on Patreon! http://patreon.com/FilmmakerIQ Let's go deep beyond the exposure triangle and look at the pathway which light travels from the scene to the sensor. In this course we will examine the elements of scene illuminance and luminance, various lens modifications which affect exposure, the exposure itself in terms of aperture and shutter speed, the sensor sensitivity, and lastly how cameras and light meters put all this information back together to obtain the proper exposure. Full Filmmaker IQ Course with Quiz and Sauce: https://filmmakeriq.com/courses/science-exposure-metering/ **ERRATA - There are 10.76 lux per footcandle, not the other way around. I got them flipped because I was thinking there are about 10 square feet in a square meter. I spelled Optical wrong at 12:00 though if my petition with Webster's is granted, it may just be the proper way to spell it in the coming years.
Views: 46165 Filmmaker IQ
Multiple Linear Regression in R... Part 2 Categorical Variables 3
 
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This video will give the background for adding multiple category predictor variables into a linear regression model. This video doesn't show any R coding, just the ideas about adding the multiple category predictor variable so that everyone understands the information is needed for the next video. Link to the datasets: http://bit.ly/2EQkJzM
Views: 33 Ed Boone
Quantitative Research: An Overview
 
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TRANSCRIPT:  Quantitative Research Design Myrene Magabo Penn State University  What is Quantitative Research? Cohen, Manion and Morrison, in their book published in 2004 define quantitative research as a systematic and scientific investigation of data and their relationships.  As many of us already may already know, the goal of quantitative research is prediction.  The objective of Quantitative Research • is to develop and employ mathematical models, theories and hypotheses pertaining to natural phenomena. • Measuring is key in quantitative research. It is because it shows the relationship between data and observation.  There are four types of Quantitative Research As listed in “Key Elements of A Research Proposal” at bcps.org: 1. The first one is “Descriptive” 2. The second type is “Correlational” 3. The third is “Causal-Comparative or Quasi – Experimental” 4. And, the fourth type is "Experimental Bcps.org. (n.d.). 'Key Elements Of A Research Proposal - Quantitative Design'. Web. 7 Sept. 2015.  NOW • How do we decide what to use for our Quantitative Research? • When should we go descriptive? • When do we go for correlational research? • When do we conduct a Causal-Comparative or Quasi – Experimental Research? • When do we go for experimental research? We ask a series of questions starting from: Will there be intervention or treatment? If not, Ask: Is the primary purpose an examination of relationships? If NOT, then you go descriptive. If YES, ask: Will the sample be studied as a single group? If YES, then you go for a co-relational design; if not--then that would be a descriptive design. If YES, there will be an intervention, ASK: Is the treatment tightly controlled by the researcher? If not, then you go for a quasi-experimental design [Ex Post Facto Design]. If YES, there will be an intervention, ASK: Will a randomly selected control group be used? If YES, then you go for an experimental design. If not, then that will be a quasi-experimental design.  To recap, we went over • What is quantitative research; • What are the four types of quantitative research; and, • The proper decision-making process that must be followed in choosing which type of design we will use.  Each of the four types of quantitative research design may have its distinct uses and possibly its advantages and disadvantages. And all these can be discussed in another presentation.  References • Bcps.org. (n.d.). 'Key Elements Of A Research Proposal - Quantitative Design'. Web. 7 Sept. 2015. • Research Methods in Education 5th Edition, Louis Cohen, Lawrence, Manion and Keith Morrison 2004, New York • Study.com,. (2015). Ex Post Facto Designs: Definition & Examples - Video & Lesson Transcript | Study.com. Retrieved 11 September 2015, from http://study.com/academy/lesson/ex-post-facto-designs-definition-examples.html
Views: 34002 Mhyre MMagabo
Correlation and Covariance in R (R Tutorial 4.9)
 
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Learn how to calculate "Pearson's", "Spearman's rank" and "Kendall's rank" correlation and create "confidence intervals" and "hypothesis tests" using "cor" and "cor.test" command. Also learn how to calculate covariance using "cov" command, produce pairwise plots using "pairs" command and a correlation or covariance matrix using the "cor" and "cov" commands. This video provides a beginner introduction to programming in R Statistical Software. 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 overview of the topics addressed in this video: 0:00:08 what is "Pearson's correlation" 0:00:16 what is "Spearman's rank correlation" 0:00:24 what is "kendall's rank correlation" 0:00:54 how to access the help menu in R for correlation commands 0:01:05 how to produce a scatterplot in R to explore the relationship between variables using "plot" command 0:01:39 how to calculate the correlation between variables using the "cor" command 0:01:46 how to calculate "pearson's correlation" in R using "method" command 0:02:17 how to calculate "Spearman's rank correlation" in R using "method" argument 0:02:24 how to calculate "kendall's rank correlation" in R using "method" argument 0:02:34 how to produce a confidence interval and test the hypothesis for the correlation using the "cor.test" command 0:03:21 how to calculate the "p value" when there are exact values in dataset using "exact" argument 0:03:42 how to change the alternative hypothesis using the "alt" argument 0:04:03 how to change confidence level using the "conf.level" command 0:04:13 how to calculate the covariance in R using the "cov" command 0:04:27 how to produce all possible pair-wise plots using the "pairs" command 0:04:50 how to produce a "pairs" plot only for some of the variables in the dataset by sub-setting data using square brackets 0:05:26 how to produce a correlation matrix using the "cor" command and "method" argument 0:05:37 how to deal with categorical variables in the dataset when creating correlation matrix by subsetting data using square brackets 0:06:18 how to produce the covariance matrix using the "cov" command
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: 7713 Keith McGuinness
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:
Categorical Variables in Linear Regression in R, Example #2 (R Tutorial 5.8)
 
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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, and produce a related plot. 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:
Lesson 2.1 - Examining Discrete and Continuous Relations
 
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The following video provides an overview of how to identify discrete and continuos relations.
Views: 341 Clayton Rainsberg
Pearson's chi square test (goodness of fit) | Probability and Statistics | Khan Academy
 
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Pearson's Chi Square Test (Goodness of Fit) Watch the next lesson: https://www.khanacademy.org/math/probability/statistics-inferential/chi-square/v/contingency-table-chi-square-test?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Missed the previous lesson? https://www.khanacademy.org/math/probability/statistics-inferential/chi-square/v/chi-square-distribution-introduction?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it! About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content. For free. For everyone. Forever. #YouCanLearnAnything Subscribe to KhanAcademy’s Probability and Statistics channel: https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1 Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy
Views: 1003731 Khan Academy
Examining Relationships Between Gas Fluxes and Plant Zones
 
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Tuesday 12:00pm Speaker: Serena Moseman-Valtierra, URI
Views: 63 WaquoitBayReserve
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: 1020728 how2stats

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