...Correlation November 21, 2011 How does a College basketball team make it to the finals or win a championship? Could it be the coach’s plays or is it the player’s defense techniques at obtaining rebounds. That is a question a coach may ask his players when giving a motivational speech before a game or during practices. Either solution or both can be a determinant. If the coach has plays that involve gaining rebounds then the plays and defense techniques can work together. Obtaining the ball after a missed shot gives that team another chance at making a shot to gain more points to win the game. The more rebounds you have the better chance you have at winning the game. In an effort to determine if there is a correlation between games won and the number of rebounds obtained each game, 50 college teams from the 2010/2011 school year were analyzed to see if a high number of rebounds had an effect on the teams that made it to the finals or won a championship title. After gathering the needed information from well-known resources, the information was put into the computer software program Minitab. Minitab calculated a variety of equations and the construction of a scatterplot graph that was used to conclude if there was a correlation between games won and rebounds. The scatterplot graph with the regression line showed a positive correlation between the games won and rebounds, as rebounds increased games won also increased. The upward slope indicated a positive value, but conclusions...
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...Correlation Name: Institution: Correlation The main purpose of linear correlation is to show how strongly two variables affect each other. If the increase in one variable leads to no definite change in the other variable, we say that there is no correlation between the two variables. If the increase in one leads to the increase in the other, we say there is a positive correlation. However if the increase in one leads to a decrease in the other, there is negative correlation. The strength in of the relationship between two variables is show by the preciseness of the shift in one variable as the other increases. In a perfect linear correlation, all the points fall in a straight line. If the data however forms a straight vertical or horizontal line, there is no correlation as one variable has no effect on the other. An example of correlation in my daily life is the relationship studying and passing of exams. Students who study hard are more likely to pass as compared to those who do not. This does not mean a causality relationship as studying does not always result to high grades. Scientific methods involve the formulation of hypothesis, testing, and analyzing the results and formulating a new hypothesis based on the results. When trying to establish a causal relationship in the above example, one needs to be aware of the following factors. 1. The percentage of students who study hard for their exams. 2. The percentage of students who pass their exams. 3. The...
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...Correlation and Causation The Link Between Sleep and Weight Correlation is the association between two variables, when there is an increase or decrease in one variable what effect does it has to the other variable (Triola, 2010). In this research the author looks on the relationship between a person who do not sleep or get quality sleep and their body weight. There was a study which highlighted a correlation between lack of sleep and increase in body weight. In the study with the women 40 – 60 years old, it was concluded that after studying their eating and sleeping pattern for 5-7 years women who had trouble falling asleep gained approximately 11 pounds. In the other study with the younger men, they studied their sleeping patterns for two consecutive days one day eight hour sleep and the other day four hour sleep. The researchers reported an increase in calorie intake (approximately 560 more) after sleeping for four days significantly more than the person who slept for eight hours. The two variables we have in this scenario is lack of sleep and increase in body weight, for these two variables to be correlated they must be linked or dependent on each other. Although the writer shows studies to show that when there is lack of sleep there is increase in appetite there can be other variables that determine the results such as location, body mass, individuals’ state of mind and age. According to the text the relationship between these two variables is weak and negative. The study...
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...Think about how correlation is often miscast as causation. That is, cause is attributed to a variable when in fact, there is no causal relationship but only a correlation between the two variables. Consider any current news articles or media stories (especially political articles or stories) that involve correlations. The correlation is often miscast as causation, especially when political issues are taken into consideration. Recently the policy of President Obama health care services has been criticized tremendously. The correlation was miscast as causation. Describe how correlation might be interpreted inappropriately as causation. Also describe the possible explanations for the correlation. The Republicans claim the policy is ineffective because it led to the deterioration of the national health care system. This criticism replaces causation by correlation. What they fail to realize is that the President’s policy is not the cause of the problems we have with the health care system in this country. The deterioration of the national health care system is determined by multiple factors, which may be correlated and interdependent, such as the economic recession and the ineffective organization of the health care and insurance system. I would therefore say, President Obama’s policy on the health care system is an example of the case when correlation is replaced by causation. In actuality the current policy not the cause but is one of the factors that affects the performance...
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...Wk: 4 Correlation Response Paper Stacy Harris BSHS/382 0ctober 31, 2011 University of Phoenix Staci Lowe Correlation Describe at least two different methods of establishing correlation between variables and provide an example of each. According to the text, “There are four forms of Karl's Pearson's product-moment r. They are Pearson r, Spearman rho (rs), Point-biserial r(pb), and phi coefficient. Each method views variables not in isolation, but instead as systematically and meaningfully associated with, or related to, other variables. For example, using correlation coefficient which indicates the strength of association between two variables the (X,Y). it also describes correlation that reflect mutual relations between X and Y resemble a straight line also known as linearity. In addition, values of r of 1.0 (positive or negative) indicates an perfect linear relation, while 0 indicates that that neither X or Y can be predicted by a linear equation. In these types of cases when the r is positive then there is an increase in both X and Y. but if the r is negative its only an increase in the X and a decrease in Y. Another method that's commonly used is the dichotomous variable also known as the discrete variable which has two separate...
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...A correlation between two variables x and y shows how to measure the linear relationship. If one variable changes it causes a proportional change in the other variable. The r is a coefficient of correlation that is a numerical descriptive measurement of the linear association between x and y. It measures the strength of the linear relationship. The symbol r, ranges from -1 to +1, where a prefect correlation is 1 whether it is positive or negative. This means as one variable increases or decreases so does the other variable at the same scale. With a correlation of 0, the variables have no relationship, so if a variable increases, the other many also increase or decrease. “A value of r near or equal to 0 implies little or no linear relationship between y and x. In contrast, the closer r comes to 1 or –1, the stronger the linear relationship between y and x. And if r = 1, or r = –1, all the sample points fall exactly on the least squares line. Positive values of r imply a positive linear relationship between y and x; that is, y increases as x increases. Negative values of r imply a negative linear relationship between y and x; that is, y decreases as x increases” (James T. McClave, 2011). References Correlation. (n.d.). Retrieved from http://edl.nova.edu/secure/stats/lesson6.htm James T. McClave, P. G. (2011). Chapter 10: Simple Linear Regression. In P. G. James T. McClave, Statistics for Business and Economics (pp. 589-590). Pearson...
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...Causation and Correlation Jennifer PSY/285 Darren Iwamoto July 17, 2013 Causation and Correlation Correlation does not imply causation. According to “statistical Language Correlation and Causation” (Correlation is a statistical measure (expressed as a number) that describes the size and direction of a relationship between two or more variables. A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable.) And (Causation indicates that one event is the result of the occurrence of the other event; i.e. there is a causal relationship between the two events. This is also referred to as cause and effect.) Causation and correlation can be difficult to discern from one another because they are so closely related to one another. Wealthy People are thin. Causation or correlation? The statement “Wealthy people are thin” is a correlation. Not all wealthy people are thin however there may be more thin wealthy people versus non wealthy people due to the fact that wealthy people can afford personal trainers, better food, and healthier lifestyles. People with long hair do better on audio memory tests. Causation or correlation? The statement “People with long hair do better on audio memory tests” is in fact a correlation, it is not a very strong correlation but it is indeed one. Ice cream melts when heated. Causation or correlation? The statement “Ice cream melts...
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...------------------------------------------------- Causation and Correlation ------------------------------------------------- Courtney Clark July 19, 2015 psy/285 Taryn Stevenson July 19, 2015 psy/285 Taryn Stevenson 1. 2. 3. Home > 4. Health & Medicine > 5. Disease 6. > 7. Causation and Correlation 1. < Back to Health & Medicine Causation and Correlation Correlation does not imply causation * By missweetie * May 11, 2012 * 511 Words * 146 Views There are many similarities between causation and correlation but there are also just as many differences. Causation is when one or more factors contribute to the effect. As said in the PowerPoint review, for example, if you switch a light switch on it causes the light turns on. The one factor of flipping the light switch on causes the effect of the light to turn on. Correlation is when two or more factors contribute to one effect. There is two different types of correlation. One type of correlation is high correlation which is when the factors all match up in a row to cause the effect. Low correlation is when the results of one factor are scattered but a pattern can be recognized. The similarities between causation and correlation are that they both require factors that can point to a result. But remember that correlation is not causation. One factor does not mean it will make the effect happen. The difference between causation and correlation is causation has a direct factor...
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...Individual Correlation Discussion BSHS/382 July 24, 2013 Vanessa Byrd Pearson product Moment is a method that is used often in order to compute any correlation between two variables showing a linear relationship. This could be used to look at how the consumption of carbohydrates plays a role in weight loss. The advantages of using this method are that an individual is able to make predictions when there is information on the correlation. When or if two variables are correlated then predictions can be made based on one variable or the other. A disadvantage of this is that even though correlation is similar to causation it does not determine the cause and often times that is forgotten. This helps to determine that there is a relationship between the variables but it cannot determine the cause. This can only be applied of the variables are not dichotomous. Spearman Rank Correlation is the most popular method used with non linear correlations and just like Pearson's it measures the relationship between two variables. Because the data used is in the form of ranks the correlation will continuously remain the same. The disadvantage is that this method is best used for data that is continuous and normally distributed which creates the ranks and cannot be used with actual data. This form could be applied when looking at test scores or a business’s monthly reports. Point-Biserial Correlation is used to estimate the degree of the relationship between a natural occurring dichotomous...
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...Ivory Raaen PSY/285 August 31, 2014 Causation and Correlation Causation and correlation may seem like the same thing, but they are not. Causation is ‘caused’ by one or more factors or a combination of factors. Such as ice cream melting, ice cream is a frozen treat, being in a warmer environment will cause the ice cream to melt. Correlation is different from causation because correlation is the similarities or relationship between variables. Correlation can be strong or weak, positive or negative. An example of correlation is people that are wealthy are thin. There really isn't any data to prove that wealthy people are always thin, but it can be based on money. One of the factors in this is that wealthy people can afford many different resources that will cause them to lose weight. Be it surgery, personal trainers, or expensive medical treatments, wealthy people can afford it and so they have the ability to change their appearance. Another example of correlation is that people with fewer cloths perform worse on standardized tests than those with more cloths. Again there isn't any data that can successfully prove this theory one way or another, but it can be attributed to poverty. Students that live in poverty do worse than those that don’t because they don’t have the resources of those that don’t live in poverty. They may also lack the education needed...
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...MODULE 6 EXERCISE Linear Correlation IRINA QUENGA EG 381 STATISTICS 02/22/2015 ITT TECHNICAL INSTITUTE Task 1: Listed below are baseball team statistics, consisting of the proportions of wins and the result of this difference: Difference (number of runs scored) - (number of runs allowed). The statistics are from a recent year, and the teams are NY—Yankees, Toronto, Boston, Cleveland, Texas, Houston, San Francisco, and Kansas City. Difference 163 55 –5 88 51 16 –214 Wins 0.599 0.537 0.531 0.481 0.494 0.506 0.383 A) Construct a scatter plot, find the value of the linear correlation coefficient r, and find the critical values of r from Table VI, Appendix A, p. A-14, of your textbook Elementary Statistics. Use α = 0.05. B) Is there sufficient evidence to conclude that there is a linear correlation between the proportion of wins and the above difference? Task 2: A classic application of correlation involves the association between temperature and the number of times a cricket chirps in a minute. Listed below are the numbers of chirps in 1 minute and the corresponding temperatures in °F: Chirps in 1 Min 882 1188 1104 864 1200 1032 960 900 Temperature (°F) 69.7 93.3 84.3 76.3 88.6 82.6 71.6 79.6 A) Construct a scatter plot, find the value of the linear correlation coefficient r, and find the critical values of r from Table VI, Appendix A, p. A-14, of your textbook Elementary Statistics. Use α = 0.05. B) Is there a linear correlation between the number of chirps...
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...Correlation is a relationship in which two or more are mutual or complimentary. Two variables that correlate together means they change together. The "correlation coefficient" is a numerical way of summarizing the strength of the association between two correlating variables that you could represent on a scatterplot. Meaning it is to tell if the correlation between the variables show a positive trend and are STRONG or show a negative trend and are WEAK and the other wary around. An Example of when correlation and causation is the likely the same would be if you took the exercise and amount of food consumed. x=exercise , y=food consumed. These are correlated because the more and harder you exercise the more calories you burn and your metabolic rate is amped, and your body needs more calories replaced to feed what you burned and to repair the muscle you damaged/worked. So bodybuilders tend to eat A LOT of food, as appose to a yoga enthusiast. If you compared a bodybuilder with a yogi and their food intake, you would see that a bodybuilder would eat a lot more than the yogi. The correlation coefficient would be stronger with the bodybuilder and the weaker with the yogi. An example of correlation and causation being the same is...
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...Difference Between Causation and Correlation Causation vs Correlation The two terms “causation” and “correlation” are usually interchanged, yet they are not interchangeable. Particular confusion arises in their understanding in the fields of health and scientific studies. EXAMPLE No 1: Every time we see a link between an event or action with another, what comes to mind is that the event or action has caused the other. This is not always so, linking one thing with another does not always prove that the result has been caused by the other. Causation Causation is an action or occurrence that can cause another. The result of an action is always predictable, providing a clear relation between them which can be established with certainty. Causation involves correlation which means that if an action causes another then they are correlated. The causation of these two correlated events or actions can be hard to establish but it is certain. Establishing causality between two correlated things has perplexed those that are involved in the health and pharmaceutical industries. The fact that an event or action causes another must be obvious and should be done with a controlled study between two groups of people. They must be from the same backgrounds and given two different experiences. The results are then compared and a conclusion can then be drawn from the outcome of the study. The process of observation plays a significant role in these studies as the subjects must be observed...
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...Introduction One of the problems linked with the assessment of research based on statistical analysis involves the determination of whether a true cause and effect connection between variables exists, or is it just a statistical relationship known as correlation. While there may be correlation between variables, there is not a causal relationship unless the variation in the independent variable or variables actually causes the variation in the dependent variable. Often correlation is misinterpreted as causation, as is the case in the examples presented in this essay. The first example is from a journal article that says watching TV increases a persons risk of heart disease and non-cancer related deaths. The second two examples are related to transportation, one saying that speeding causes car crashes and the other saying population in a traffic analysis zone (TAZ) causes trips produced. Although correlation is necessary it is not sufficient, it is important that a true causal relation exists before making conclusions. Body (Example A) The first study is presented in the journal, Circulation-Journal of the American Heart Association with the title, Television Viewing time and Mortality: The Australian Diabetes, Obesity and Lifestyle Study (AusDiab). The baseline data for the study was gathered between the years of 1999 and 2000. The locations for data collection were chosen based on Census Collector Districts in each of the Australian states and in the Northern...
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...Correlation as a measure of association summary BSHS/435 January 24 2016 Correlation as a measure of association summary Introduction In this essay I will describe correlation is a measure of association as well as describe different methods of establishing a correlation between variables. In this essay I will also explain advantages and disadvantages of each method, were each must be applied, and provide particular circumstances and examples in which a researcher may want to establish correlation Describe correlations as measured of association "A correlation is a statistical to determine the tendency or pattern for two (or more) variables or two sets of data to very consistently" (Creswell, (2012). any relationships between variables can be positive, negative, or curvilinear. “Measures of association describe the nature of relationships between variables, particularly the strength of the relationship or how closely the variables are related. The strongest relationship is a perfect one, and which a given change is one variable is always associated with a particular change and the other bearable. Rarely, however, are perfect relationships found in human service research" (Monette, Sullivan & DeJong, (2011). Describe different methods of establishing correlations between variables. There are different ways of establishing a correlation between variables such as nominal data. "some data are dichotomous in inform- that is, the variables have only two...
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