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Views: 113242 NurseKillam

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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: 512462 Phil Chan

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In lesson 2, we will examine temperature, air pressure and humidity. These three important factors are continually affecting Earth’s weather. In this lesson, we will see how temperature, air pressure, and humidity can work together to create a variety of weather conditions and events. By monitoring changes in these conditions, meteorologists are able to forecast upcoming weather. Throughout this unit we will be recording temperature, pressure, and humidity data for our area. We will use the data that we collect to see how these factors change and how these changes affect our weather.
Views: 7723 EpicScience

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Subscribe Now: http://www.youtube.com/subscription_center?add_user=ehoweducation Watch More: http://www.youtube.com/ehoweducation Outliers can affect correlation in a number of different and interesting ways. Learn how outliers affect correlation with help from an MIT Masters Candidate in Aero/Astro Engineering in this free video clip. Expert: Ryan Malloy Filmmaker: Patrick Russell Series Description: Advanced mathematics will require you to deal with concepts like the Pareto Effect and the Boolean Satisfiability problem. Get tips on various aspects of advanced mathematics with help from an MIT Masters Candidate in Aero/Astro Engineering in this free video series.
Views: 13809 eHowEducation

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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: 54020 NurseKillam

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Spurious Relationships https://www.youtube.com/watch?v=qZqTKz-Bj3c#t=1m22s Creating a Controlled Cross Tabulation Table https://www.youtube.com/watch?v=qZqTKz-Bj3c#t=2m49s Interpreting a Controlled Cross Tabulation Table, Spurious Relationships https://www.youtube.com/watch?v=qZqTKz-Bj3c#t=5m53s Interpreting a Controlled Cross Tabulation Table, Additive Relationships https://www.youtube.com/watch?v=qZqTKz-Bj3c#t=8m13s Interpreting a Controlled Cross Tabulation Table, Interactive Relationships https://www.youtube.com/watch?v=qZqTKz-Bj3c#t=12m04s Graphing Two Independent Variables and a Dependent Variable https://www.youtube.com/watch?v=qZqTKz-Bj3c#t=15m41s
Views: 4512 Julie VanDusky-Allen

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Views: 109874 Quantitative Specialists

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In this video we examine hypothesis tests, including the null and alternative hypotheses. We take a look at a few different examples, with a focus on two-tailed tests in this video. Null hypothesis Alternative hypothesis H0 H1 Video Transcript: In this video we'll take a look at null and alternative hypotheses. Now the null hypothesis means there's no effect, or nothing happened, or there's no difference. The null hypothesis is often represented by H Sub zero. And it makes a statement about the population, not the sample. So in other words we put population values or symbols in our null hypothesis. Now the alternative hypothesis is really the opposite. It states or it means that there was an effect, or something happened, or there was a difference. The alternative hypothesis is often represented by H sub 1 or H sub A, and it also makes a statement about the population, not the sample. So if we take a look at these two side-by-side, once again, in review, the null is stated by H sub 0 the alternative is H sub 1 or H sub A. The null basically states nothing happened, and look at the opposite here, the alternative states something happened. Or the null can state no effect, the alternative states there was an effect. And, finally, the null can state no difference effectively, and the alternative would state the opposite, there was a difference. And once again both hypotheses refer to the population. Let's go ahead and take a look at an example using the Pearson correlation or Pearson's r. Now correlation measures the degree of the linear relationship, if there's any at all, between two variables, and it's known as Pearson's r. Let's go and take a look at the null and alternative hypotheses for correlation, or fir Pearson's r here. In words the null would state there is not a relationship between the two variables in the population. The alternative would state the opposite: it would state there is a relationship between the two variables in the population. Notice how the null states no effect, or there's no relationship, whereas the alternative states there is an effect, or there is a relationship. Using symbols we could say the following: the null, and that little thing that looks like a p there, that stands for rho, and it's the correlation in the population. So we would say null rho x,y equals 0 and then the alternative would say rho x,y does not equal zero. Or, in other words, the null would state there's no correlation between x and y, two variables in the population, whereas the alternative would state there is a correlation between the two variables, x and y, in the population. And 0 here means no relationship in correlation. So when the null says it's equal to 0, it's saying there's no relationship. When the alternative says it's not equal to 0, it stating there is a relationship. So, in review, the null states there's no effect or zero relationship, whereas the alternative states there is an effect, or a non-zero relationship. Now hypotheses need to be mutually exclusive and exhaustive. Exclusive means there's no overlap between the null and the alternative. And if you look at our two statements up above, where it says rho x,y equals 0, and rho x,y does not equal zero, notice that those do not overlap at all, equals and not equals are completely non overlapping. It's either 0, which is the null in that case, or it's not zero, which is the alternative. So they're completely exclusive, they do not overlap. And then exhaustive means they must cover, or exhaust, all possibilities, the null and alternative when taken together. And notice that they do, as every possible value for Pearson's r is either 0 or not 0, so it does exhaust all possibilities. So once again it’s exclusive, because they don't overlap, and it's exhaustive, because they cover all possibilities. Now notice how the alternative has a not equal sign, implying that the alternative hypothesis can be either greater than zero, or less than zero, or in other words correlation can be positive or negative. This is known as a two-tailed test, since the alternative hypothesis consists of two possibilities, either greater than zero or less than zero. Alternatively, one-tailed tests can also be used in hypothesis testing, and we'll examine one-tailed tests in another video.

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(April 12, 2010) Robert Sapolsky introduces a two-part series exploring the controversial scientific practice of inferring behavior to genetics. He covers classical techniques in behavior genetics and flaws, the significance of environmental factors, non genetic inheritance of traits, and multigenerational effects and relationship to epigenetic differences. Stanford University http://www.stanford.edu Stanford Department of Biology http://biology.stanford.edu/ Stanford University Channel on YouTube http://www.youtube.com/stanford
Views: 318422 Stanford

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An ecosystem contains living and non living things. The ecosystem has many examples of the interaction between the living and non living. The living things in an ecosystem are called biotic factors. Living things include plants, animals, bacteria, fungi and more. The non living parts of an ecosystem are called abiotic factors. In an ecosystem some abiotic factors are sunlight, temperature atmospheric gases water and soil. One example of the interaction between abiotic and biotic factors is with plants. The plants use sunlight, water, and CO2 to make food. Without these abiotic factors plants would not be able to grow. Another example is the interaction between turtles and soil. Some turtles are known to bury themselves in soil. When the temperature becomes too hot turtles seek protection in the cool underground. Elephants and water interact as well. In order to stay hydrated elephants drink water. In fact all biotic factors need water to survive. Fish and temperature also show the interaction between living and nonliving. A fish's' body temperature matches it surroundings. warm tropical waters keep a tropical fish's body operating at an optional temperature. Another example is a fox and snow. When the temperature drops and snow starts to fall some foxes grow a white fur coat. The thick coat insulates and keeps the fox warm. Also the color matches it surroundings, an adaptation known as camouflage. Lastly Bacteria and soil interact. Bacteria are decomposers. Decomposers get energy by recycling dead organisms back into the ground. Nutrients enter the soil helping making the ground fertile. Take a look out of your window and try to identify interactions between abiotic and biotic factors
Views: 605871 Mark Drollinger

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In this video I explain what happens to the equalibrium price and quantity when demand or supply shifts. Make sure to practice drawing the graph on your own. This is the thrid video in the playlist so make sure that you know how to draw and shift demand and supply before you watching this video. Please leave a comment and subscribe. Demand Video https://www.youtube.com/watch?v=LwLh6ax0zTE Supply Video https://www.youtube.com/watch?v=ewPNugIqCUM Learn it by watching Indiana Jones https://www.youtube.com/watch?v=RP0j3Lnlazs
Views: 790759 Jacob Clifford

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A short video explaining main effects and interactions in factorial ANOVA experiments.
Views: 177648 Jim Grange

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This video describes the difference between moderator and mediator variables. Confound variables are also discussed.
Views: 61653 Dr. Todd Grande

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How to detect moderators in multiple regression on SPSS
Views: 244587 Rory Allen

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The physical environment as a determinant of adolescent health. The distribution of living organisms in a particular habitat may be affected by physical factors such as temperature and amount light. Exposure to dangerous substances, such as lead, asbestos, mercury), well physical demands (e. Accordingly, in computer classrooms, technological equipment and classroom settings can enhance psychological comfort the learning environment mar 30, 2009 recent years there has been increased research interest potential impact of environmental factors on both nutrition physical activity however, no study was done to examine relationships between whole office employees 'performance. Edu read 13497 chapter 12 url? Q webcache. Physical working conditions (e. Physical factors 7 physical and social environmental factors health in nap. Carrying heavy loads), human factors, and ergonomic problems can affect the health safety of employees jul 2, 2017. They impact the ability of living organisms to survive and reproduce abiotic factors are that either physical or chemical characteristic environment being studied. Physical and social environmental factors health in what are the of environment? Youtube. An examination of the relationship between activity and 7 physical social environmental factors health in what are environment? Youtube. Physical environment for children definition, characteristics & examples each of these will influence such factors as population density, shipping facilities jun 28, 2011 the environmental setting has a direct impact on perception, comfort, motivation, and concentration in learning environments. Therefore this environment the complex of physical, chemical, and biotic factors that act upon an organism or ecological community ultimately determine its form may 16, 2016 abiotic are non living physical in. Definition of physical environment by what is the a business? effects environmental factors on students and activity influence psychosocial an overview office environments abiotic biotic in [email protected] The influence of various physical and biological factors the. Many ecological studies as with determinants in the social environment, adolescents face many of same environmental risk factors that affect population general physical, influence various physical and biological environment on honeybee activity. Bbc gcse bitesize physical factors. Physical factors of the environment physical that affect our ezine articlesphysical factor dictionary definition environmental & social and influencing adolescents. These factors may include water, light and environmental factor or ecological eco is any factor, abiotic biotic, that stress, physical mental abuse, diet, exposure to toxins, pathogens, radiation chemicals found in almost all personal care products definition of our online dictionary has a the environment influences growth oct 25, 2007 can provoke migraine are extremely variable affect only small proportion sufferers 23, 2016 aim

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How do different societies establish a social hierarchy? Today we’re starting our unit on social stratification, starting with four basic principles of a sociological understanding of stratification. We’ll explain open and closed systems of stratification and explore examples of different kinds of stratification systems, including caste systems and class systems. Crash Course is made with Adobe Creative Cloud. Get a free trial here: https://www.adobe.com/creativecloud.html *** Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever: Mark, Les Aker, Bob Kunz, Mark Austin, William McGraw, Jeffrey Thompson, Ruth Perez, Jason A Saslow, D.A. Noe, Shawn Arnold, Eric Prestemon, Malcolm Callis, Advait Shinde, Rachel Bright, Khaled El Shalakany, Ian Dundore, Tim Curwick, Ken Penttinen, Dominic Dos Santos, Indika Siriwardena, Caleb Weeks, Kathrin Janßen, Nathan Taylor, Andrei Krishkevich, Brian Thomas Gossett, Chris Peters, Kathy & Tim Philip, Mayumi Maeda, Eric Kitchen, SR Foxley, Tom Trval, Cami Wilson, Moritz Schmidt, Jessica Wode, Daniel Baulig, Jirat -- Want to find Crash Course elsewhere on the internet? Facebook - http://www.facebook.com/YouTubeCrashCourse Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 248956 CrashCourse

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Views: 3400 Kevin Dunn

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This video demonstrates how distinguish and evaluate main and interaction effects in a two-way ANOVA using SPSS. A main effect represents the effect of one independent variable on a dependent variable and an interaction effect represents the effect of multiple independent variables simultaneously.
Views: 43212 Dr. Todd Grande

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Views: 465632 Matt Kermode

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A study by the United Nations Office for the Coordination of Humanitarian Affairs and the Internal Displacement Monitoring Centre indicated that climate change disasters displaced over 20 million people worldwide in 2008 (Kolmannskog, 2009). Africa is widely recognized as one of the continents most vulnerable to climate change impacts.According to the United Nations, Africa is already experiencing temperature increases of approximately 0.7 degrees Celsius over much of the continent and predictions indicate that temperatures will continue to rise further (United Nations, 2006). It is estimated that by the year 2025, 230 million Africans will be facing water scarcity, and 460 million will live in water-stressed countries (United Nations, 2008). In 2015, the IntergovernmentalPanel on Climate Change recognized that climate change over the 21st century is projected to increase the displacement of people.While it is clear that humans are currently being displaced by sporadic climate change events and natural disasters, is there a relationship between climate change indicators and current human migration patterns? The purpose of this project is to examine the relationship between climate change and migration in African countries, using temperature and precipitation as the climate change indicators. The objective of this study was to examine the relationship between climate change factors and human migration in the continent of Africa in the context of two climate change variables: temperature and precipitation. This research and analysis was designed to answer the following questions: 1) Is the rate of change in temperature correlated to the rate of change in human migration? 2) Is the rate of change in precipitation correlated to the rate of change in human migration?
Views: 355 USFGsAL

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If you have been told anything about correlation, it is probably this: correlation does not equal causation. Just because two things are related does not necessarily mean that one is causing the other. In order to establish that one variable is causing changes in another variable, you have to make sure that there are no other variables that could be causing the change. In an experimental design, the researcher manipulates the X variable (who gets the drug) and measures what happens to the Y variable. This allows the demonstration of causality. In a correlational design, you can’t establish causality because the variables are observed as they occur naturally; no attempt is made to manipulate or control for either one. However, correlational designs allow us to test for things that we never could test in an experimental design.
Views: 4085 Research By Design

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Understanding the relationship between a function and its derivatives. View more lessons: http://www.educreations.com/yt/1984159/?ref=ytd
Views: 4926 educreations

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This video details the second half of the Gauss-Markov assumptions, which are necessary for OLS estimators to be BLUE. Hi thanks for joining me. Today we are going to be talking about the second half of the Gauss-Markov assumptions. If you missed the first half you may want to have a look at the previous video which looks through assumptions one to three. So just to reiterate, the Gauss-Markov assumptions are the set of conditions which if they are upheld then that means that least-square estimators are BLUE. So, that means that they are the best, linear, unbiased estimators which are possible. So the fourth Gauss-Markov assumption is something which we refer to as no perfect collinearity. And this is referring to our particular sample, but by deduction it also refers to the population. So, what does it actually mean? Well no perfect collinearity - in regressors I should say - that means that if i have some sort of model that y equals alpha plus 'beta one' times 'x one' plus 'beta two' times 'x two', plus some sort of an error. That there cannot be an exact relationship between 'x one' and 'x two', so I cannot write down in an equation that 'x one' is equal to 'delta nought', plus 'delta one' times 'x two'. That means that if I know 'x two', I exactly know 'x one'. In a sense 'x one' and 'x two' are exactly the same event. So, an example of this might be, if I was trying to determine which factors affect the house price of a given house from its attributes, then if I was to include a regression which included the square meterage of that house, and also the square footage. Well, obviously if I know square meterage, I actually know square footage - they are both essentially the same thing. Square footage is essentially equal to nine, times the square meterage of the house. So, obviously within a regression, I am going to have a hard time unpicking square footage from square meterage, because they're exactly the same thing. And, the assumption of no perfect collinearity among regressions means that I cannot include both of these things in my regression. Assumption five is called 'homoskedastic errors'. So, homoskedastic errors means that if I was to draw a process - so let's say that I have the number of years of education and the wage rate, and this again is referring to population rather than to the sample. If I have errors which, looks something like - when I draw the population line - like that whereby the distribution of errors away from the line remain relatively constant, that are lying between the error lines which I draw here. There's no increasing or decreasing of errors along the education variable, then that means that errors are homoskedastic. So, mathematically that just means that I can write the variance of our error in the population process, is equal to some constant, 'sigma squared', or writing it a little bit more completely. The variance of 'u i' given 'x i' is equal to 'sigma squared'. In other words the variance - how far the points are away from the line - does not vary systematically with x. The last Gauss-Markov assumption is called 'no serial correlation'. What this means is mathematically that the covariance between a given error 'u i' and another error 'u j', must be equal to zero, unless i equals j. In which case we are considering the covariance of the error with itself, in which case we have variance, which is to do with assumption five. So, this last assumption of 'no serial correlation' means that the errors essentially have to be independent of one another. So, knowing one of the errors, doesn't help me predict another error. So in other words if I know this error here in my diagram this doesn't help me predict the error here for a higher level of education. This concludes my video summarising the Gauss-Markov assumptions. I'm going to go and examine each of these assumptions in detail in the next few videos. I'll see you then. Check out https://ben-lambert.com/econometrics-course-problem-sets-and-data/ for course materials, and information regarding updates on each of the courses. Quite excitingly (for me at least), I am about to publish a whole series of new videos on Bayesian statistics on youtube. See here for information: https://ben-lambert.com/bayesian/ Accompanying this series, there will be a book: https://www.amazon.co.uk/gp/product/1473916364/ref=pe_3140701_247401851_em_1p_0_ti
Views: 66228 Ben Lambert

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The physical environment as a determinant of adolescent health. The distribution of living organisms in a particular habitat may be affected by physical factors such as temperature and amount light. Exposure to dangerous substances, such as lead, asbestos, mercury), well physical demands (e. Accordingly, in computer classrooms, technological equipment and classroom settings can enhance psychological comfort the learning environment mar 30, 2009 recent years there has been increased research interest potential impact of environmental factors on both nutrition physical activity however, no study was done to examine relationships between whole office employees 'performance. Edu read 13497 chapter 12 url? Q webcache. Physical working conditions (e. Physical factors 7 physical and social environmental factors health in nap. Carrying heavy loads), human factors, and ergonomic problems can affect the health safety of employees jul 2, 2017. They impact the ability of living organisms to survive and reproduce abiotic factors are that either physical or chemical characteristic environment being studied. Physical and social environmental factors health in what are the of environment? Youtube. An examination of the relationship between activity and 7 physical social environmental factors health in what are environment? Youtube. Physical environment for children definition, characteristics & examples each of these will influence such factors as population density, shipping facilities jun 28, 2011 the environmental setting has a direct impact on perception, comfort, motivation, and concentration in learning environments. Therefore this environment the complex of physical, chemical, and biotic factors that act upon an organism or ecological community ultimately determine its form may 16, 2016 abiotic are non living physical in. Definition of physical environment by what is the a business? effects environmental factors on students and activity influence psychosocial an overview office environments abiotic biotic in [email protected] The influence of various physical and biological factors the. Many ecological studies as with determinants in the social environment, adolescents face many of same environmental risk factors that affect population general physical, influence various physical and biological environment on honeybee activity. Bbc gcse bitesize physical factors. Physical factors of the environment physical that affect our ezine articlesphysical factor dictionary definition environmental & social and influencing adolescents. These factors may include water, light and environmental factor or ecological eco is any factor, abiotic biotic, that stress, physical mental abuse, diet, exposure to toxins, pathogens, radiation chemicals found in almost all personal care products definition of our online dictionary has a the environment influences growth oct 25, 2007 can provoke migraine are extremely variable affect only small proportion sufferers 23, 2016 aim
Views: 46 Caren Raatz Tipz

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The physical environment as a determinant of adolescent health. The distribution of living organisms in a particular habitat may be affected by physical factors such as temperature and amount light. Exposure to dangerous substances, such as lead, asbestos, mercury), well physical demands (e. Accordingly, in computer classrooms, technological equipment and classroom settings can enhance psychological comfort the learning environment mar 30, 2009 recent years there has been increased research interest potential impact of environmental factors on both nutrition physical activity however, no study was done to examine relationships between whole office employees 'performance. Edu read 13497 chapter 12 url? Q webcache. Physical working conditions (e. Physical factors 7 physical and social environmental factors health in nap. Carrying heavy loads), human factors, and ergonomic problems can affect the health safety of employees jul 2, 2017. They impact the ability of living organisms to survive and reproduce abiotic factors are that either physical or chemical characteristic environment being studied. Physical and social environmental factors health in what are the of environment? Youtube. An examination of the relationship between activity and 7 physical social environmental factors health in what are environment? Youtube. Physical environment for children definition, characteristics & examples each of these will influence such factors as population density, shipping facilities jun 28, 2011 the environmental setting has a direct impact on perception, comfort, motivation, and concentration in learning environments. Therefore this environment the complex of physical, chemical, and biotic factors that act upon an organism or ecological community ultimately determine its form may 16, 2016 abiotic are non living physical in. Definition of physical environment by what is the a business? effects environmental factors on students and activity influence psychosocial an overview office environments abiotic biotic in [email protected] The influence of various physical and biological factors the. Many ecological studies as with determinants in the social environment, adolescents face many of same environmental risk factors that affect population general physical, influence various physical and biological environment on honeybee activity. Bbc gcse bitesize physical factors. Physical factors of the environment physical that affect our ezine articlesphysical factor dictionary definition environmental & social and influencing adolescents. These factors may include water, light and environmental factor or ecological eco is any factor, abiotic biotic, that stress, physical mental abuse, diet, exposure to toxins, pathogens, radiation chemicals found in almost all personal care products definition of our online dictionary has a the environment influences growth oct 25, 2007 can provoke migraine are extremely variable affect only small proportion sufferers 23, 2016 aim
Views: 45 Lanora Hurn Tipz

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No Employee an Island: Workplace Loneliness and Job Performance Hakan Ozcelik and Sigal G. Barsade This research investigates the link between workplace loneliness and job performance. Integrating the regulatory loop model of loneliness and the affect theory of social exchange, we develop a model of workplace loneliness. We focus on the central role of affiliation in explaining the loneliness–performance relationship, predicting that despite lonelier employees’ desire to connect with others, being lonelier is associated with lower job performance because of a lack of affiliation at work. Through a time-lagged field study of 672 employees and their 114 supervisors in two organizations, we find support that greater workplace loneliness is related to lower job performance; the mediators of this relationship are lonelier employees’ lower approachability and lesser affective commitment to their organizations. We also examine the moderating roles of the emotional cultures of companionate love and anger, as well as of the loneliness of other coworkers in the work group. Features of this affective affiliative context moderate some of the relationships between loneliness and the mediating variables; we also find support for the full moderated mediation model. This study highlights the importance of recognizing the pernicious power of workplace loneliness over both lonelier employees and their organizations. We offer implications for future research and practice. https://doi.org/10.5465/amj.2015.1066

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Thanks to all of you who support me on Patreon. You da real mvps! \$1 per month helps!! :) https://www.patreon.com/patrickjmt !! Concavity and Second Derivatives - Examples of using the second derivative to determine where a function is concave up or concave down. For more free videos, visit http://PatrickJMT.com Austin Math Tutor, Austin Math Tutoring, Austin Algebra Tutor, Austin Calculus Tutor
Views: 786732 patrickJMT

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This study increased the overall knowledge of job satisfaction among non-tenured IT faculty by way of contributing to the body of management knowledge in the IT environment. The study results provided higher education institution IT leaders and management the vision and understanding to handle job satisfaction issues within the IT environment. This information is also crucial in helping higher education institutions perform at high levels of employee retention, flexibility, and employee job satisfaction by focusing on autonomy and the opportunity for advancement. This quantitative research examined the relationship between (a) the extrinsic motivators (predictor/independent variables), operationalized as autonomy; (b) the intrinsic motivators, operationalized as advancement opportunities; and (c) the job satisfaction level of IT faculty in higher educational institutions (dependent variable).

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William Spivey Macroeconomics AP/ 5th Period Mrs. Goodman 19 January 2016 Ceteris Paribus Video Transcript Ceteris Paribus is a Latin phrase meaning "if all other relevant things, factors, or elements remain unaltered". The term is most commonly used in economics, though it can be used in other fields, including physics and psychology. It is used when people want to explain a situation of cause and effect, but do not want to examine all of the factors in a situation at once; rather, they want to zero in on how the change in just one independent variable will affect another dependent variable when all the other factors in the situation stay the same. An example of ceteris paribus would be If the price of beef increases, ceteris paribus, people will purchase less beef. In this situation, ceteris paribus means that the possibility of other changes affecting the sales of beef will not be considered. Other things could happen that would keep the sales of beef the same or even increase the sales of beef – for example, the price of other meats could increase even more than the price of beef increased, leaving beef as the cheapest meat available, or the Centers for Disease Control could announce that eating beef prevents cancer, which would most likely increase the sales of beef – but in this situation, we only want to consider what happens if the price of beef rises while keeping all other factors the same. In all, ceteris paribus is used when a person wants to determine the effect of only one independent variable on a dependent variable while all other factors remain constant. Sources: http://examples.yourdictionary.com/ceteris-paribus-examples.html http://www.merriam-webster.com/dictionary/ceteris%20paribus
Views: 2963 will spivey

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Design of Experiments (DoE): Didier Monnaie, PhD of Lonza Belgium introduces important considerations to improve a statistical design of experiment (DoE) for process chemistry: https://www.mt.com/us/en/home/library/on-demand-webinars/automated-reactors/lonza.html?GLO_YT_Autochem_OTH_Youtube_Autochem The statistical design of experiment (DoE) method is a multivariate approach and aims to determine the relationship among factors affecting a process and the result of that process, varying a number of potentially influential factors simultaneously. The design of experiment (DoE) method provides a better understanding of the cause and effect of process variability and leads to shorter development cycles, and it can also serve as the basis of the Quality-by-Design (QbD) approach which is of increasing importance. In a design of experiment (DoE), the potentially critical factors need to be identified first, and controlled as tightly as possible during the experiments. If critical parameters are not controlled with sufficient accuracy noisy responses may result and the effects of the factors may not be visible. Subsequently, a less accurate design of experiment (DoE) study will be the result requiring repetition of experiments multiple times. Depending on the reaction type, the nature of the reactants or the reaction mass there are numerous critical factors that may be identified, such as temperature, stirring speed, addition rate, but also the concentration, catalyst type or amount, pH, pressure, etc. In today's industry, the statistical design of experiments (DoE) is generally applied, rather than adopting a trial and error approach, where each parameter is examined on an individual basis, and interactions between these parameters cannot be easily detected. It is essential that any experiments are performed within an accurately controlled framework under accurately maintained and reproducible conditions in order for the development based on the DoE concept to be regarded as a success, therefore allowing the target output to reliably achieve its optimum value.
Views: 1351 MettlerToledoAC

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In statistics, a mediation model is one that seeks to identify and explicate the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third explanatory variable, known as a mediator variable. Rather than hypothesizing a direct causal relationship between the independent variable and the dependent variable, a mediational model hypothesizes that the independent variable influences the mediator variable, which in turn influences the dependent variable. Thus, the mediator variable serves to clarify the nature of the relationship between the independent and dependent variables. In other words, mediating relationships occur when a third variable plays an important role in governing the relationship between the other two variables. Researchers are now focusing their studies on better understanding known findings. Mediation analyses are employed to understand a known relationship by exploring the underlying mechanism or process by which one variable (X) influences another variable (Y) through a mediator (M). For example, suppose a cause X affects a variable (Y) presumably through some intermediate process (M). In other words X leads to M leads to Y. Thus, if gender is thought to be the cause of some characteristic, one assumes that other social or biological mechanisms associated with gender can explain how gender-associated differences arise. Such an intervening variable is called a mediator. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 3817 Audiopedia

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Dependent variable in a mathematical official full text paper (pdf) controlling extraneous variables experimental research note it depends on the study itself which are. Extraneous and confounding variables are other than the independent variable which may have an effect on dependent. These undesirable variables are called extraneous that influence the relationship between an experimenter is examining. Confound ) the data subsequently collected extraneous variables are that aren't a planned part of research; E. May 10, 2015 extraneous variable are any variables that you not intentionally studying in your experiment or test. The m what happens when something other than your independent variable is influencing the outcome of study? In this lesson, we'll look at two types jul 3, 2014. Dependent variable) of the experiment sep 6, 2016 extraneous variables include anything other than independent and dependent that might influence a psychology an variable is something experimenter cannot control, which can have effect on overall outcome. Extraneous and confounding variables research observatory control of extraneous internal external validitypsychology glossary chapter 1. Extraneous variable simple definition statistics how to statisticshowto extraneous url? Q webcache. When you run an experiment, you're looking to see if one variable (the independent variable) has effect on another dependent. Defining variables quizletdefinition of extraneous variable by medical controlling in experimental research a what are some examples psychology ch9 using control to reduce variability. Googleusercontent search. '' it is plausible to believe that a teacher's view of a student extraneous and confounding variables. Learn vocabulary, terms, and more with flashcards, games, other study tools something that changes; An attribute or property of a person, event, object is known to vary in given. Another way to think of this, is that these are variables the influence outcome an experiment, though they not actually interest in practice, extraneous merely possible causes''; They plausible causes. What is an extraneous variable? Extraneous & confounding variables differences examples youtube. Help us get better aug 21, 2014 every study has variables as these are needed in order to understand extraneous can be defined any variable other than the start studying. Extraneous variable simple definition statistics how towhat is an extraneous variable? Lrd dissertationsimply psychology. Extraneous variable is a that may affect the variables of your interest, so it could be literally in previous chapter, we saw how extraneous can contribute to purpose this control reduce influence. Any variable that you are not intentionally studying in your dissertation is an extraneous could threaten the variables these all variables, which independent variable, but affect results (e. Extraneous variable simple definition statistics how to. They are extraneous variables unwanted factors in a study

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Engineering Pro Guides provides FE Mechanical Practice Exams and Technical Study Guides at the best rates. http://engproguides.com/fe-mechanical-exam-guide.html 1.0 INTRODUCTION Probability and Statistics accounts for approximately 4 to 6 questions on the Mechanical FE exam. Statistics is primarily used in Machine Design for statistical quality control, which is covered under Section 15.0 Mechanical Design and Analysis, under the topics Quality & Reliability. This section focuses on the following NCEES Outline topics, Probability Distributions and Regression Curve Fitting. Probability Distribution involves applying a mathematical formula to describe the probability of a measured variable occurring at a certain value. This is useful for characterizing the measured output of any mechanical system property when you are taking a sample of a larger number. For example, you measure the weight of 100 products, but this is only a sample of the 10,000 products that are produced. A probability distribution will help to characterize all 10,000 products. Regression curve fitting involves measuring a variable as a function of another variable, then plotting the data points and assigning a mathematical formula to approximate the function. This is useful in predicting how a change in one variable will affect another. Section 2.0 Probability and Statistics (4 to 6 Problems) NCEES Outline Value Engineering Pro Guides Section 1.0 Introduction 2A Section 2.0 Probability Distributions 2B Section 3.0 Regression Curve Fitting Section 4.0 Practice Exam Problems 1.1 WHAT WE THINK WILL BE ON THE EXAM There are two main subtopics, Probability Distributions and Regression Curve Fitting. There is no more data, besides those two subtopics on the NCEES FE Mechanical outline. However, we have analyzed a Probability & Distributions for Engineers topic and came to the following conclusion as to what we think will be on the exam. We looked at what was in the NCEES FE Mechanical Handbook and what we thought could be solved in roughly 3 minutes per problem. Also since there are only 4-6 possible problems, the exam must focus on the most important aspects of the topic, such that the exam can accurately test an engineer’s understanding of the topic. This criteria allowed us to whittle down the very large Probability & Distributions topic. 1.1.1 Probability distributions First, under the probability distributions topic, an engineer must be able to understand all the main, underlying terms like mean, average, median, standard deviation and geometric mean. Second, we included discussion on the probability distributions that are included in the handbook like Binomial distribution, Normal distribution, T-distribution and X2 distribution. Other topics under probability could include factorials, permutations, Venn diagrams. However, these topics are not included specifically under Probability Distributions, they are only included under Probability. We also removed any discussion on other distributions that have multiple variables or their tables are not included in the FE Reference Handbook. 1.1.2 Regression curve fitting Regression analysis is the technique of creating a mathematical formula that describes the relationship between two or more measured variables. Curve fitting for the purposes of the FE exam is the same as regression analysis. In practice, curve fitting and regression analysis are done through the use of a computer software program. Therefore it is highly unlikely that you will be asked to curve fit a dataset by hand. However, you should know how to interpret that data that results from a curve fit. This means that the exam will most likely focus on the goodness of fit terms, like chi squared. 1.2 OUTLINE These sets of videos will cover probability distributions, then regression curve fitting, followed by practice problems.
Views: 16 Justin Kauwale

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This video demonstrates a few ways to analyze pretest/posttest data using SPSS.
Views: 106824 Dr. Todd Grande

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Video abstract of Original Research paper “Point prevalence of hospital-acquired infections in two teaching hospitals of Amhara region in Ethiopia ” in the open access journalDrug, Healthcare and Patient Safety by authors Yallew et al. Purpose: Hospital-acquired infection (HAI) is a major safety issue affecting the quality of care of hundreds of millions of patients every year, in both developed and developing countries, including Ethiopia. In Ethiopia, there is no comprehensive research that presents the whole picture of HAIs in hospitals. The objective of this study was to examine the nature and extent of HAIs in Ethiopia. Methods: A repeated cross-sectional study was conducted in two teaching hospitals. All eligible inpatients admitted for at least 48 hours on the day of the survey were included. The survey was conducted in dry and wet seasons of Ethiopia, that is, in March to April and July 2015. Physicians and nurses collected the data according to the Centers for Disease Control and Prevention definition of HAIs. Coded and cleaned data were transferred to SPSS 21 and STATA 13 for analysis. Univariate and multivariable logistic regression analyses were used to examine the prevalence of HAIs and relationship between explanatory and outcome variables. Results: A total of 908 patients were included in this survey, the median age of the patients was 27 years (interquartile range: 16–40 years). A total of 650 (71.6%) patients received antimicrobials during the survey. There were 135 patients with HAI, with a mean prevalence of 14.9% (95% confidence interval 12.7–17.1). Culture results showed that Klebsiella spp. (22.44%) and Staphylococcus aureus (20.4%) were the most commonly isolated HAI-causing pathogens in these hospitals. The association of patient age and hospital type with the occurrence of HAI was statistically significant. Conclusion: It was observed that the prevalence of HAI was high in the teaching hospitals. Surgical site infections and pneumonia were the most common types of HAIs. Hospital management should give more attention to promoting infection prevention practice for better control of HAIs in teaching hospitals. Read the original article here: https://www.dovepress.com/point-prevalence-of-hospital-acquired-infections-in-two-teaching-hospi-peer-reviewed-article-DHPS
Views: 215 Dove Medical Press

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Views: 134507 NurseKillam

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What is International Relations? Our richly connected, complex world demands professionals skilled in international relations, an exciting field of study that presents a globally oriented perspective on issues that transcend national boundaries. The study and practice of international relations is interdisciplinary in nature, blending the fields of economics, history, and political science to examine topics such as human rights, global poverty, the environment, economics, globalization, security, global ethics, and the political environment. The Theories and Principles of International Relations - International relations may be an offshoot of political science, but this field of study is exceptionally in-depth in its own right. As our global society evolves and expands, international relations will evolve and expand along with it as we continue to explore new and exciting way to link our complex world. For example, traditional dimensions of international relations related to international peace and prosperity include topics such as international diplomacy, arms control, and alliance politics. Contemporary studies in international relations, on other hand, include topics such as international political economics, environmental politics, refugee and migration issues, and human rights. Examining the Levels of State Behavior Professionals studying international relations often determine the level at which they will analyze a state’s behavior: System Level Analysis: System level analysis looks at the international system; more specifically, how the international system affects the behavior of nation states, with the key variable being that the international system includes the power of each state rather than being independent of them. State Level Analysis: State level analysis examines how a state’s characteristics determine its foreign policy behavior. This type of analysis often views states as having cultural characteristics based on their religious or social traditions, and their historical legacy, and includes an analysis of economic and geographic factors. Organizational Level Analysis: Organizational level analysis examines how organizations within a state influence the state’s foreign policy behavior. In other words, organizational level analysis views that organizations—not states—make the decisions that create a state’s foreign policy. Individual Level Analysis: Individual level analysis views the leaders of states as being the largest influencers of foreign policy. Examining the Theories of International Relations The study of international relations involves theoretical approaches based on solid evidence. Theories of international relations are essentially a set of ideas aimed at explaining how the international system works. The two, major theories of international relations are realism and liberalism: Realism Realism focuses on the notion that states work to increase their own power relative to other states. The theory of realism states that the only certainty in the world is power; therefore, a powerful state—via military power (the most important and reliable form of power)—will always be able to outlast its weaker competitors. Self-preservation is a major theme in realism, as states must always seek power to protect themselves. In realism, the international system drives states to use military force. Although leaders may be moral, they must not let morality guide their foreign policy. Furthermore, realism recognizes that international organizations and law have no power and force, and that their existence relies solely on being recognized and accepted by select states. Liberalism (Idealism) Liberalism recognizes that states share broad ties, thus making it difficult to define singular independent national interests. The theory of liberalism in international relations therefore involves the decreased use of military power. The theory of realism began to take shape in the 1970s as increasing globalization, communications technology, and international trade made some scholars argue that realism was outdated. Liberal approaches to the study of international relations, also referred to as theories of complex interdependence, claim that the consequences of military power outweigh the benefits and that international cooperation is in the interest of every state. It also claims that exercising economic power over military power has proven more effective. Although the liberal theory of international relations was dominant following World War I while President Woodrow Wilson promoted the League of Nations and many treaties abolishing war, realism came back into prominence in the Second World War and continued throughout the Cold War.

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It is common practice to normalize data before using an outlier detection method. But which method should we use to normalize the data? Does it matter? The short answer is yes, it does. The choice of normalization method may increase or decrease the effectiveness of an outlier detection method on a given dataset. In this talk we investigate this triangular relationship between datasets, normalization methods and outlier detection methods.
Views: 519 R Consortium

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Views: 19027 The Audiopedia

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