...Correlation does not imply causation Almost daily we are in (mainly) news media whose owner has a structure similar to some of the following: One study claims that the more A, the more B A study says that those who are to have less B. A study says that since A is, then B is the other way. In principle, all these headlines indicate that basically what it says A is causing B to happen, or what is the same, that B is a consequence of A. Normally, when one reads the news, just realizing that so there is a correlation between A and B (come on, a relationship between these two events ), but in principle, without any indication that either one of them, even in this case, we cause the other B. (Oakes, 2012) The study of the correlation between two variables is one of the issues in question in Statistics. To summarize a bit, the question would be something like the following: - From certain data from each of these variables one estimates if there is any relationship between them. The one most frequently studied is called linear regression (by which we seek if there is no linear relationship between the variables), but there are many more possible types: quadratic, exponential, logarithmic... - With these data a function (which, for example, is a straight linear regression) that determines us exactly what the relationship between these variables is calculated. - The actual correlation between them is studied (ie, how strong is the relationship that we calculated based on the...
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...Analysis of How Much Land Does a Man Need by Leo Tolstoy Anna Gregor, Yahoo! Contributor Network Dec 23, 2009 "Share your voice on Yahoo! websites. Start Here." * More: * Tolstoy * Leo Tolstoy * * tweet * Print FlagPost a comment AdChoices | | In the short story "How Much Land Does A Man Need" by Leo Tolstoy, Pahom is a peasant living on a small plot of land. When his wife brags that a peasant's life is safer than having money, because with money comes temptation, Pahom agrees, adding that he would not be "afraid of the devil himself" if he only had more land. He buys more land, but is unhappy, for no matter how much more land he gets, he wants more. He becomes greedier and greedier until he loses control of his life and, in the end, loses everything. This story shows us that even if we have enough to get by, the prospect of becoming wealthier is so alluring it can cause us to risk all the good things we already have. At the start, Pahom is a content, hardworking men. Unfortunately, he makes the mistake of thinking that more land would make his life better. Pahom says to himself "our only trouble is that we haven't land enough. If I had plenty of land, I shouldn't fear the devil himself"! When an opportunity arises for Pahom to acquire more land, he jumps at the chance, convinced that more would make him happy. He paid off his debts and had plenty of fertile land to grow his crops and raise his cattle, but he was only satisfied for a short...
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...Work Sheet for ”Debbie & Julie” Short story by Doris Lessing, 1987 A 1. How does Julie look? - Julie is a young pregnant girl, normally small of size but because of the pregnancy she perceives herself as fat in the mirror. We are not giving that much information about her looks, but she is smaller than Debbie. 2. What do you think the paperback (p. 2, 1. 5) is about? - it’s a book about pregnancy and giving birth. Julie uses the book as guidance. 3. What is in Julie´s carrier bag (see p. 3, 1. 13 - 19)? - in her well-prepared bag, Julie carries a torch, pieces of string wrapped in a piece of plastic, two pairs of knickers, a cardigan, a thick towel with an old blouse of Debbie´s, some sanitary pads and two guest towels. 4. Why does Julie feel full of trouble and worry and ill-prepared (p. 3, 1. 21)? - Julie is not sure why she feels the way she does, it could be because she´s afraid what would happen to herself and the unborn child. 5. How does Debbie earn her living? - Debbie could be a prostitute or escort, she has many “visits” from many different men, and she travels to New York with a man. Julie and Debbie have a strange relationship. It seems like Julie is relying on Debbie to take care of her as she did at first when Julie arrived to the city. Debbie always paid for Julie and taught her about life and men - from her own point of view. Debbie’s motto in life was “Take what you want and don’t pay for it” (p. 3 l. 30) but Julie knows better regardless...
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...[Class] [Date] Violent Video Games do not CAUSE VIOLENCE Introduction Correlation does not prove Causation. Since 1992, there have been 387 school shootings in America. In the wake of these tragedies, the investigators and our society have looked to find somewhere to point the blame. Some blame gun laws, some blame our school system, but almost all conclusions place at least some blame on video games. When 90% of today's teenagers video games are 90% likely to include mature levels of violence, there's clearly an issue. However, the issue is mistakenly simplified down to the equation that violence in games = violence in real life, mistakenly because correlation does not prove causation. Today, I want to talk about why video games do not cause violence, and can even be a healthy way to release aggression. body Research is inconclusive. Unfortunately, research on video games and violence is inconclusive and does not ever come to a consensus. For every study that you find that proves video games cause violence, you have a study that proves that video games have no correlation to real-life violence. Media psychologists, communication scientists, and pediatricians disagree greatly among themselves as to whether or not there is a causal relationship between gaming and violence. There is more than a 20% variation in opinion over whether or not gaming produces real-life violence. That variation does not sound convincing to us, who like to pretend we are 100% certain about things,...
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...look at this information and create rewards and maybe a salary change if needed to satisfy their employees. Correlational research is basically a way to determine the relation between two different variables. The way that correlational research works is to figure out a prediction about one thing based on another. For example gender and department could form a correlational relationship. The reason why I chose these two is because there may be a certain dominant gender that works in a certain department and by looking at the data you will be able to see if that is true (Lanthier, 2002). One thing that I learned about correlation and causation was that they are not the same and correlation does not show causation. I learned that just because something happens at the same time does not mean that the one thing cause the other to happen all the time (Mason,2011). References Lanthier, E. (March 29, 2002). Correlation. Retrieved from http://www.nvcc.edu/home/elanthier/methods/correlation.htm Mason, G. (2011). What is the difference between causation and correlation? Retrieved from...
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...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 percentage of students who study hard and they pass their exams. 4. Other factors that may lead to exams failure such as fatigue. 5. Amount of work the students had to study fro. This scenario represents a case of positive correlation in that the higher the number of students who study, the...
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...Correlation, Predication, Confidence, and Errors: Analysis of Statistics in the News In statistics, there are a number of ways to compare how data is associated with the subject matters being discussed. In this paper, I will discuss how three different magazine articles use statistics to improve and expound on their theories, the methodology used, and if their use of statistics made the article more or less convincing. The statistical methods that will discuss statistics in the news include correlation, predication, confidence, and errors. Correlation “exists between two variables when higher values of one variable consistently go with higher values of another variable or when higher values of one variable consistently go with lower values of another variable. (Bennet, Briggs & Triola 2014)” It is only used with quantifiable data where the numbers are meaningful (height, weight, age, etc), therefore, it can’t be used for categorical data (gender, favorite foods, jobs, etc). Predication is defined by Britannica.com as the attribution of characteristics to a subject to produce something meaningful. This combines verbal elements and those that exist in name only (Britannica 2015). Confidence in statistics is defined as “a group of continuous or discrete adjacent values used to estimate a statistical parameter as a means of variance” (Merriam-Webster 2015). Statistical errors means that when null hypothesis is proven incorrect, then the alternative hypothesis is accepted...
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...might suggest that the treatment or finding does not make enough of a difference to justify its use or to be practical. Yes, it is possible for a study to have statistical significance but not a practical significance. If the source of the data can benefit from the results of the study, it is possible that an element of bias is introduced so that the results are favorable to the source. A voluntary response sample is a sample in which the subjects themselves decide whether to be included in the study. A voluntary response sample is generally not suitable for a statistical study because the sample may have a bias resulting from participation by those with a special interest in the topic being studied. Even if we conduct a study and find that there is a correlation, or association, between two variables, we cannot conclude that one of the variables is the cause of the other. There does appear to be a potential to create a bias. There does not appear to be a potential to create a bias. There does not appear to be a potential to create a bias. There does appear a potential to create a bias. The sample is a voluntary response sample and is therefore flawed. 2. 3. 4. 5. 6. 7. 8. 9. 10. The sample is a voluntary response sample and is therefore flawed. 11. The sampling method appears to be sound. 12. The sampling method appears to be sound. 13. Because there is a 30% chance of getting such results with a diet that has no effect, it does not appear to have statistical significance...
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...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 over a certain period of time. Correlation Correlation is an action or occurrence that can be linked to another. The action does not always result to another action or occurrence but you can see that there is a relationship between them. Although the action does not make the other thing happen, the possibility of having something happen is great. Correlation can be easily...
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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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...regression equation produced in the Excel spreadsheet. * The linear correlation coefficient is positive. * The r^2 linear correlation coefficient is 0.785 * The linear regression equation is y=1.5608x+55.767 3. What would be the value of Pearson’s r (simply the square root of r2)? * R^2 is 0.785, which means that 78.5% of the total variation in y can be explained by the relationship between x and y. The other 21.5% remains unexplained. 4. Would Pearson’s r be positive or negative? What does this imply about the relationship between the factors in this study? * Pearson’s r is positive and equal to 1.0. Since Pearson r is positive this implies that there is a strong correlation between x and y. 5. What is the implication of any correlation found between the variables in the study you picked? * X & Y have a strong positive correlation. It seems the more hours put into studying the better the grade percentages will be. 6. Does this correlation imply a causal relationship? Explain. * There is a casual relationship, because on the graph it shows positive correlation, because the slopes are increasing. That implies if you spend more time studying your results are a higher grade. 7. Are there other variables that you think should have been examined that would have improved this study or help to pinpoint what factors are causal? * The environment/ home setting (single family home, number of siblings) can affect an individual’s...
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...the linear regression equation produced in the Excel spreadsheet. * The linear correlation coefficient is positive. * The r^2 linear correlation coefficient is 0.785 * The linear regression equation is y=1.5608x+55.767 3. What would be the value of Pearson’s r (simply the square root of r2)? * R^2 is 0.785, which means that 78.5% of the total variation in y can be explained by the relationship between x and y. The other 21.5% remains unexplained. 4. Would Pearson’s r be positive or negative? What does this imply about the relationship between the factors in this study? * Pearson’s r is positive and equal to 1.0. Since Pearson r is positive this implies that there is a strong correlation between x and y. 5. What is the implication of any correlation found between the variables in the study you picked? * X & Y have a strong positive correlation. It seems the more hours put into studying the better the grade percentages will be. 6. Does this correlation imply a causal relationship? Explain. * There is a casual relationship, because on the graph it shows positive correlation, because the slopes are increasing. That implies if you spend more time studying your results are a higher grade. 7. Are there other variables that you think should have been examined that would have improved this study or help to pinpoint what factors are causal? * The environment/ home setting (single family home, number of siblings) can affect an individual’s studying...
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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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...Each of these things needs to be done to have a good organization. Departmentalization is needed to do each group jobs efficiently. It can and does cause problems between workers. Such as when another group of workers is supposed to do a job and fail to do so, it then may fall onto another set of workers to complete. Where I work we are required to do housekeeping, v and our own job s of taking care of our patients. This causes problems because when we don’t have time to do something such as the nightly cleaning of the wheelchairs, and lifts. It causes upper management to get mad because it wasn’t done. Matrix organizations, is taking employees from every department and making a group that lets their upper management know how everything is going for those in their respectively departments. This does not make them tattle tells, as I am sure a lot of people would call them. They are there to help the upper management learn if changes need to be made to help the employees do a better job. These can cause problems because the other employees in a group may think the person doing the reporting is doing nothing but trying to get the other employees in trouble. Project teams are employees from all parts of the organization, and are grouped together to do a set project. This can be good for each group to interact with those outside of their normal groups. Project teams can cause problems though when those chosen don’t get along well, are not being corruptive with those who...
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...Alcohol is a depressant that slows down the body’s reactions. It also affects the working of the brain and the nervous system. The more a person drinks, the more their ability to make important decisions become impaired. After just one drink a person can lose their ability to perform everyday tasks. There are many causes of people drinking, but the major two are causal drinking and peer pressure. Causal drinking occurs when a person goes to a party or bar and he or she does not intend on drinking, but they do anyway. The next cause is peer pressure. Why does peer pressure cause drinking? The first reason is when someone’s friends keep going on at them until they drink. The second reason is when someone’s friends’ drink, they think it is cool and they don’t want to be left out, so they drink too. The effects alcohol can have on a body are numerous. It increases the workload of the heart, causing irregular heartbeat and high blood pressure. It poisons the liver, causing extensive damage and failure. It can stop the kidneys from maintaining a proper balance of body fluids and minerals; it can also affect the brain, causing unclear thinking. Large amounts of it can cause unconsciousness or death. Even knowing this some people will still make bad and dangerous choices when it comes to alcohol. One of the most dangerous things you can do is, driving under the influence (D.U.I). Not only are you endangering yourself, you are endangering your passenger, other road users, and pedestrians...
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