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Search results “Examining relationships among variables science”

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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

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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

07:48
Views: 109049 NurseKillam

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Statistics Making Sense of Data Examining Relationships Between Two Categorical Variables
Views: 258 LummoMy

12:44
Views: 1527 Amr Arafat

01:23
Part of Entry Document for Statistics. Video used for educational purposes only.
Views: 180 Jazmine Castanon

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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?

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Views: 5181 Data School

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Made with Doodlecast Pro from the iTunes App Store. http://www.doodlecastpro.com
Views: 1285 Jeremy Haselhorst

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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

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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

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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

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Views: 1936 Robert Emrich

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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

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Views: 795 Kimberly Brehm

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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

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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

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Views: 4234 DataCamp

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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.

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Views: 91236 NurseKillam

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Views: 3392 Amr Arafat

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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

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Views: 1116 Steven Post

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Views: 1950 Sarah Peterson

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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

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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

01:40:03
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

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Views: 980785 mathantics

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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

01:02:58
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

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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

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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

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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.

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Views: 2832 Simplilearn

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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

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Views: 1145 msimps22

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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

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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

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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

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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

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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

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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:

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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:

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The following video provides an overview of how to identify discrete and continuos relations.
Views: 341 Clayton Rainsberg

11:48

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Tuesday 12:00pm Speaker: Serena Moseman-Valtierra, URI
Views: 63 WaquoitBayReserve

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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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Views: 66 youngteacher74

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