Differences Between Correlation and Causation

One day at lunch a young woman was eating a large bowl of ice cream, and a fellow faculty member walked up to her and said, â€Å"You had better be careful, there is a high statistical correlation between ice cream and drowning.† She must have given him a confused look, as he elaborated some more. â€Å"Days with the most sales of ice cream also see the most people drown.† When she had finished my ice cream the two colleagues discussed the fact that just because one variable is statistically associated with another, it doesn’t mean that one is the cause of the other. Sometimes there is a variable hiding in the background. In this case, the day of the year is hiding in the data. More ice cream is sold on hot summer days than snowy winter ones. More people swim in the summer, and hence more drown in the summer than in the winter. Beware of Lurking Variables The above anecdote is a prime example of what is known as a lurking variable. As its name suggests, a lurking variable can be elusive and difficult to detect. When we find that two numerical data sets are strongly correlated, we should always ask, â€Å"Could there be something else that is causing this relationship?† The following are examples of strong correlation caused by a lurking variable: The average number of computers per person in a country and that country’s average life expectancy.The number of firefighters at a fire and the damage caused by the fire.The height of an elementary school student and his or her reading level. In all of these cases, the relationship between the variables is a very strong one.  This is typically indicated by a correlation coefficient that has a value close to 1 or to -1.  It does not matter how close this correlation coefficient is to 1 or to -1, this statistic cannot show that one variable is the cause of the other variable. Detection of Lurking Variables By their nature, lurking variables are difficult to detect. One strategy, if available, is to examine what happens to the data over time. This can reveal seasonal trends, such as the ice cream example, that get obscured when the data is lumped together. Another method is to look at outliers and try to determine what makes them different than the other data. Sometimes this provides a hint of what is happening behind the scenes. The best course of action is to be proactive; question assumptions and design experiments carefully. Why Does It Matter? In the opening scenario, suppose a well-meaning but statistically uninformed congressman proposed to outlaw all ice cream in order to prevent drowning. Such a bill would inconvenience large segments of the population, force several companies into bankruptcy, and eliminate thousands of jobs as the country’s ice cream industry closed down. Despite the best of intentions, this bill would not decrease the number of drowning deaths. If that example seems a little too far fetched, consider the following, which actually happened. In the early 1900s, doctors noticed that some infants were mysteriously dying in their sleep from perceived respiratory problems. This was called crib death and is now known as SIDS. One thing that stuck out from autopsies performed on those who died from SIDS was an enlarged thymus, a gland located in the chest. From the correlation of enlarged thymus glands in SIDS babies, doctors presumed that an abnormally large thymus caused improper breathing and death. The proposed solution was to shrink the thymus with high does of radiation, or to remove the gland entirely. These procedures had a high mortality rate and led to even more deaths. What is sad is that these operations didn’t have to have been performed. Subsequent research has shown that these doctors were mistaken in their assumptions and that the thymus is not responsible for SIDS. Correlation Does Not Imply Causation The above should make us pause when we think that statistical evidence is used to justify things such as medical regimens, legislation, and educational proposals. It is important that good work is done in interpreting data, especially if results involving correlation are going to affect the lives of others. When anyone states, â€Å"Studies show that A is a cause of B and some statistics back it up,† be ready to reply, â€Å"correlation does not imply causation.† Always be on the lookout for what lurks beneath the data.

Research Method Example

Essays on Research Method Coursework The result are analyzed in the excel worksheet showing all the required data. (2) In order to estimate the empirical model, the previous compile data in excel worksheet would be used. This method is used to test a claim o just hypothesis about a parameter of the entire population using the data measured in the previous sample. This will enable to determine whether the sample statistic would be selected in case the hypothesis regarding such population parameter is true (Barnard, 2007). Therefore the following null and alternative hypotheses would be tested based on the following formula as shown bellow. Using random method of data selection, let use consider the results for Algeria and United Kingdom for effective calculations. For United Kingdom: i. dlypci = ï  ¢1 + ï  ¢2lypc90i + ï  ¢3lsecedi + ï  ¢4govgdpi + ï  ¢5openi + ï  ¢6infli + ï  ¢7crediti + ui Since dlypc = ln (ypc05) – ln (ypc90), then dlypc=121.07-102.70=18.37 (a) H0:ï  ¢2=ï  ¢3=ï  ¢4=ï  ¢5=ï  ¢6=ï  ¢7=0 against H1: ï  ¢jï‚ ¹0 for at least one j ïÆ'Ž(2...7), using 0.05 significance level. =ï  ¢1+102.7*0+93*0+21742.5*0=36.97*0+ (112.62-65.79)*0+1.130044*0+0.05 18.37=ï  ¢1+0+0.05 ï  ¢1=18.32 where as for Algeria the results are: dlypci = ï  ¢1 + ï  ¢2lypc90i + ï  ¢3lsecedi + ï  ¢4govgdpi + ï  ¢5openi + ï  ¢6infli + ï  ¢7crediti + ui Since dlypc = ln (ypc05) – ln (ypc90), then dlypc=59.77-98.12=-38.35 =ï  ¢1+59.77*0+61*0+5189.55*0+90.65*0+ (49.95-44.38)*0+0.399052*0+0.05 -38.35=ï  ¢1+0+0.05 ï  ¢1=-38.4 Therefore, based on the above computation, the results shows that the data collected is statistically not significant and can be relied upon in making decision. This is because all the outcomes as per the hypothesis i.e. ï  ¢jï‚ ¹0. Thus, the population is a null hypothesis. ii. ï  ¢2=0 against H0:ï  ¢2ï‚ ¹0 using a significance level of 0.05 dlypci = ï  ¢1 + ï  ¢2lypc90i + ï  ¢3lsecedi + ï  ¢4govgdpi + ï  ¢5openi + ï  ¢6infli + ï  ¢7crediti + ui =ï  ¢1+102.7*0.1+93*0+21742.5*0+36.97*0+ (112.62-65.79)*0+1.130044*0+0.05 18.37= ï  ¢1+10.25 ï  ¢1=8.1 while for Algerian =ï  ¢1+59.77*0.1+61*0+5189.55*0+90.65*0+ (49.95-44.38)*0+0.399052*0+0.05 -38.35=ï  ¢1+5.977+0.05 ï  ¢1=-44.28 The hypothesis is not true is null in nature and can not be relied upon in making decision according to the model stated. (Wellek Stefan, 2003) iii. H0:ï  ¢3=0 against H0:ï  ¢30 using a significance level of 0.05 =ï  ¢1+102.7*0+93*0+21742.5*1+36.97*0+ (112.62-65.79)*0+1.130044*0+0.05 18.37=ï  ¢1+21742.5+0.05 ï  ¢1=-21724.2 where as for Algeria =ï  ¢1+59.77*0+61*0+5189.55*1+90.65*0+ (49.95-44.38)*0+0.399052*0+0.05 -38.35=ï  ¢1+5189.55+0.05 ï  ¢1=-5227.95 The test statistic results are used to determine the likelihood of results computed, for instance, the larger the value of statistic, then the further the number or distance sample mean or standard deviation. Though the result ca be relied on, but is further from the mean value. iv. H0:ï  ¢7=0 against H0:ï  ¢70 using a significance level of 0.1 =ï  ¢1+102.7*0+93*0+21742.5*0=36.97*0+ (112.62-65.79)*0+1.130044*1+0.1 18.37=ï  ¢1+1.130044+0.1 ï  ¢1=17.3 on the other side Algeria results are; =ï  ¢1+59.77*0+61*0+5189.55*0+90.65*0+ (49.95-44.38)*0+0.399052*1+0.1 -38.35=ï  ¢1+0.399052+0.1 ï  ¢1=-38.9 The above alternative hypothesis can be relied upon in making decisions, since the population Parameter is less than, more than or not equal to the stated values in the null hypothesis as per the model. This is contrary to null hypothesis (3) Based on the model determined in the previous computations, An appropriate advise can now be made on whether, the ministry of finance should spend extra money effectively on secondary school education, or consider bailing out banks can be easily arrived at in a more concise and appropriate manner keeping in mind that the main objective is to increase the overall growth in GDP per capita for the coming fifteen years. For instance, using Algeria and United Kingdom based on H0:ï  ¢7=0 against H0:ï  ¢70 using a significance level of 0.1, the results would be as follows, dlypci = ï  ¢1 + ï  ¢2lypc90i + ï  ¢3lsecedi + ï  ¢4govgdpi + ï  ¢5openi + ï  ¢6infli + ï  ¢7crediti + ui =ï  ¢1+102.7*0+93*0+21742.5*0=36.97*0+ (112.62-65.79)*0+ (1.130044+2)*1+0.1 18.37=ï  ¢1+3.130044+0.1 ï  ¢1=15.24 =ï  ¢1+59.77*0+61*0+5189.55*0+90.65*0+ (49.95-44.38)*0+ (0.399052+2)*1+0.1 -38.35=ï  ¢1+2.399052+0.1 ï  ¢1=-40.9 The ministry of finance should consider investing more money (USD 2 billion) on secondary education as opposed to being spent on bailing out the banks. An increase by $2billion has a considerable increase in the countries GDP. This is because education is the key pillar of economic stimulant which is channeled to other sectors of economy. In the longer run i.e. 15 years to come, all sectors including banking institutions will be much better having improved so much. Employment level will increase, income per person would also rise and as a result of this, the overall well being of all the citizens will be better. (4) Diagnostic test on the empirical model and the hypothesis tested previously in question two following; i. Linearity assumption. According to linearity assumption, the general rule is that, for any regression model which has an independent variable (Hardy Melissa1993). Then such variable is represented by both non-square and squared terms which have significance. If the value chosen is less than the significance level, then it is recommendable to accept the hypothesis that the entire population represented by the variable is statistically significant. This is contrary to the null hypothesis, which the reverse is true. Therefore based on the following hypothesis, H0:ï  ¢7=0 against H0:ï  ¢70 using a significance level of 0.1. As computed earlier the results should be accepted. dlypci = ï  ¢1 + ï  ¢2lypc90i + ï  ¢3lsecedi + ï  ¢4govgdpi + ï  ¢5openi + ï  ¢6infli + ï  ¢7crediti + ui For instance since the result obtained ï  ¢1=-40.9 which is less than 0 then the hypothesis should be accepted and be used in making decisions. ii. Homoscedasticity assumption The model assumes that all dependent variables have the same amount of variables across the range of values for each independent variable. It requires that, all independent variables be non-metric (Lehmann, 2010). Once this is the case, then it can be evaluated as one of the residual analysis in the multiple regressions. Since the diagnostic hypothesis test for homogeneity of the variances and the degree of confidence is 0.1 which is much higher, then the result can be relied upon. 18.37=ï  ¢1+3.130044+0.1, ï  ¢1=15.24, according to United Kingdom results, since the degree of confidence is over 0.05 as is the case with other hypothesis, the result is significant. iii. Normality assumption Statistical method includes hypothesis test for the linearity i.e. the thumb rule which assumes that a relationship is always linear if the difference between the non-linear and the linear correlation coefficient is small. If the transformations for an independent or a dependent variable are statistically significant, then the problem is linear (Koch, 1999). And incase, the transformation is not statistically significant, then there is no relationship at all. The results computed in the previously indicate that the problem is linear, thus statistically significant. (5) Dummy variable represents a number of data categories; it is used to find out if being in a certain category has a comparable difference with being in the other category. The value for dummy is either one or zero (Kooyman, 1976). For instance, the number of student enrolment in secondary school for different levels could be used based on the empirical model computed earlier. dlypci = ï  ¢1 + ï  ¢2lypc90i + ï  ¢3lsecedi + ï  ¢4govgdpi + ï  ¢5openi + ï  ¢6infli + ï  ¢7crediti + ui Supposed the value of students enrolled is 0 and the government share on GDP is 1, then this means that less money will be stent on education. Though in the normal case, the government incurs expenditures on its projects such as education before considering having its share. The same effect will be on other variable incase they are assumed to be dummy. The result will be that the overall per capita GDP of the country will be lower. In case the value changes from 0 to a much higher value like 3, then the result will no longer be dummy. A slighter change in any variable will significantly affect the entire result for per capita GDP. (6) The main objective of altering or changing of variables is necessary in testing the effectiveness and the accuracy of the empirical model and does not really change the decision of using the $2 billion to foster secondary education rather than investing the funds in the banking institution. In order to realize the value of $2 billions, the Ministry of finance should consider investing the funds in education sector which will in tern be realized in less than a span of 15 years. This is evidenced as per the tests and computations determined above. Reference: Barnard, C.  2007, Asking questions in biology: a guide to hypothesis-testing, analysis and presentation in practical work and research. Harlow: Pearson Education. Hardy Melissa1993, Regression with dummy variables, Newbury Park: Sage Publications.   Koch, 1999, Parameter estimation and hypothesis testing in linear models S.l: Springer.   Kooyman, M.   1976, Dummy variables in econometrics, Tilburg: Tilburg University Press. Lehmann, E.  2010, Testing statistical hypotheses. New York: Springer.   Wellek Stefan, 2003, Testing Statistical hypotheses of equivalence, Boca Raton, Fla: Chapman Hall/CRC.

Backup Free Essays

Backup or backing up refers to the process of making copies of data to save and restore the original data incase of loss event also known as disaster recovery. Backup is so important in view of the fact that loss of data often happen in most machine users as their computers are habitually in the high risk of going wrong, failure in the hard disk does happen. The most common problems today that may result in loss of data are the threat to viruses. We will write a custom essay sample on Backup or any similar topic only for you Order Now Although some viruses do not affect the file or the computer itself, some does and may even infect the hard disks of your computer resulting in data loss. David Smith estimated that 6% of all the personal computers suffer data loss every year (Boston Computing Network, 2010). Also, about 31% of PC users have experience data loss due to uncontrollable events (Boston Computing Network, 2010). When do you take backup (daily, weekly, on significant changes to data)? I personally do not set a specific schedule for backing up files because it has my habit to save a copy of important data. I also backup files when I make changes to them and so there is no need to have a schedule time for taking backup of files. In case of accidental loss of data, I do not have to worry since I have copies of all the files that are important to me. Do you schedule backups automatically? If so how? If not, how can you be sure to do them? No, I do not schedule automatic backups. As I have mentioned, it has been my habit to take backups whenever significant changes are made to my files. Thus, I am certain to have copies of all the files that I need. How do you take backup – manually, using the Copy facilities in the Windows Explorer? If so, describe the process; or do you have some other backup program? If so, what is it, and why do you like to use it? I take backups manually. I have to plug the storage device and manually save the data in order to create copies of it. I do not use back programs since I do not view taking backup as a task but rather I see it as an enjoyable thing to do since it gives me the certainly that in cases of data loss, I always have a copy. Where do you store the backup files (ie on what device and in what physical location) and why did you make that choice? Include costs, if any)? I have two primary devices used for data storage; flash disk and external hard disk. I used the flask disk for files that require changing in a short period of time, mostly school stuffs. On the other hand, I use the external hard disk for files that I rarely use as well as for large volume files. Still, I also use CDR’s for data that I wish not to be changed, mostly program files and installers. In the case that I am employed and required to use my personal computer for work related task, the only threat that I see is the privacy which can easily be handled by organizing files and folders. I believe that there would be no significant changes that must be done but I would need another hard disk in order to maintain that organization of my files. My original hard disk will be used for the backups of my personal files while the other will be used for work related documents. I would also have to change the allotment on my PC in order to cope with the changes. I will have to create partitions on the disks in order separate personal to work use: one partition for work use, one for personal use and another extra partition for other files. My flash disk would serve the same purpose as before, for files that often requires changes but both for personal and work related task. Thus, the only cost associated with the changes is another external hard disk for work related files as well as the time for making necessary changes. How to cite Backup, Papers

Wrestling free essay sample

I am often asked the question, â€Å"What is it like to wrestle and why do wrestlers cut weight?† I usually can never fully answer the question. Most likely because I do not know why wrestlers cut weight. As a wrestler you think losing weight and wrestling in a smaller weight class will give you an advantage, but I’ve never seen the advantage. In fact, I answer that question by saying, â€Å"Imagine playing your sport, but with half the energy and strength and going all out for three, two minute periods.† Now, some wrestlers do not cut weight at all, or just a couple of pounds, but the majority of us know the feeling of cutting weight. The feeling that gives you mood swings like a woman with PMS. The feeling that you have no strength and energy left inside of you. Your cheeks suck in like you are making a fish face and your lips begin to chap. I explain that boys love me and look at me as their little sister and their biggest support system. The question I always get first after my explanation is â€Å"HOW DID THAT START?† They ask in a different form of shock than before. It is a simple story. As I started my seventh grade year, I was shaky and nervous about middle school and popularity. I met this lanky awkward boy with big ears and bright blue eyes. I watched throughout the year as he went from lanky to built and brawn. I was amazed at his transformation and enthralled. I finally decided to speak to him. So a few months past and we were inseparable, but things were changing. He did not start texting me until later and later at night. I asked him what was going on and he said he wrestled for the high school. That weekend I attended my first tournament in order to watch him. From then on, I was hooked. Like a majority of people that are awestruck about me working with the wrestling team, my parents were dumbfounded. They could only think of sweaty boys and violence. It took a while for them to realize that this was not me participating, but rather managing and gaining leadership. I am often asked if I have ever wrestled myself. Honestly, I could never bring myself to show that determination and dedication to one sport and risk all that they do. Yet, I keep finding myself protecting the sport from the misunderstandings that frame it. Because of this, I have become a major advocate for male and female equivalency. I express my views without reservation, and I have learned to take in the opinions other than my own. I am stronger, not physically, but mentally from this special activity. I have learned that gender is not a matter of who can do what, but how hard the person must work. I love wrestling, and I think wrestling loves me too.