The Comparison Of Forest Of Ar :: essays research papers

The Comparison of Forest of Arden versus Forest of Eden   Ã‚  Ã‚  Ã‚  Ã‚  It is the innate desire of man to seek a place of freedom, .an utopia, which allows him to escape into fantasies and away from the hardships of reality. In the play 'As you like it'; by William Shakespeare, the playwright indirectly compares the Forest of Arden to the Forest of Eden by exploring the theme of illusions, the idea of the forest as a place of refuge and Arden's mystical powers. The two forests are not only related by the fact that they sound similar but the many imageries and ideas that were mentioned in the play are somewhat related to the story of Genesis. In Duke Senior's first speech, he refers to 'the penalty of Adam'; , reminding the reader of the Fall of Man and the Garden of Eden. Therefore from the start, Arden is associated with a world of innocence and simplicity, in which man did not need to work. In the forest of Eden, the life of man is not controlled by time and ultimately, death. Whereas there are also suggestions of timelessness in the forest of Arden, as when Orlando points out that there is 'no clock in the forest'; . According to geographic sources, the Forest of Arden is in the Ardennes, France. In the play, Duke Senior and his followers live in the forest itself, while the shepherds live in the more open country on the edge of the forest. However, some elements in the play can be found in neither France nor England. For example: the deer, the oak trees, palm-trees; and also exotic animals such as a green-and-golden and a lioness roam the forest. Clearly then, the landscape belongs to the realm of fantasy. Although Arden seems as to be an idealistic world and resembles no known place, it is not merely an escape from real life. It is true that people flee to Arden from the cruelty of the world. The play opens outside Arden, in a world where cruelty, tyranny, and injustice reign; Arden provides a refuge from these evils. However, it is not an easy salvation. It is not even easy to get there: Rosalind, Celia, and Touchstone, and then Orlando and Adam, and finally Oliver, all arrive in Arden in a state of physical exhaustion. This relates to the fact that it is just as hard for us to enter the kingdom of God.

Young Goodman Brown Essay -- essays research papers

  Ã‚  Ã‚  Ã‚  Ã‚     Ã‚  Ã‚  Ã‚  Ã‚  The main focus of the story â€Å"Young Goodman Brown† by Nathaniel Hawthorne is the triumph of evil over good. A supposedly good man is tempted by evil and allows himself to be converted into a man of evil. This is much like the situation that arises in Hawthorne’s The Scarlet Letter, where two people are tempted to sin and give in thus submitting themselves to the power of the devil. In this novel, the area where the devil resides is strictly parallel to that in â€Å"Young Goodman Brown†.   Ã‚  Ã‚  Ã‚  Ã‚  As Goodman Brown sets off on his walk into the forest, he believes that there is more good within his community than evil, and that he himself is a good man. He believes that he will follow his wife to heaven. Yet as he talks with his fellow traveler his thoughts are soon swayed. Goodman Brown talks of his father and grandfather and how they never went into the woods on such an errand as himself, for they were honest men and good Christians. Yet his companion tells him of many a sin his relatives had committed and the walks he had taken with them, therefore proving to Goodman Brown that his father and grandfather before him were not perfectly innocent, good people. Goodman Brown says to his companion that the people of New England are a good people of prayer and abide no wickedness. Again, the traveler returns with comments of the church deacon drinking the communion wine and the town selectmen selling...

Michael Jordan :: essays papers

Michael Jordan Critical Essay On A CNN Article At: http://cnn.com/CNN/bureaus/chicago/special/9806/bulls/mj.htm CNN resource reports the Michael Jordan effect on the economy. Michael Jordan is as CNN states "golden marking tool ". Michael Jordan makes much less that he is worth. With the impact he put on the economy Michael Jordan should be able to receive any amount of payment he ask for. Michael Jordan is Mr. Indorse. Michael is an advertising icon. Today Michael Jordan indorses for some of the major companies in the field today. Michael Jordan indorses for the top name brand shoe in the word Nike. He also peddles products for McDonalds, Oakley, Hanes, long distance company WorldCom, and Gatorade. Michael Jordan also have his own clothing line. Michael Jordan also have his on fragrance. Michael owns his own restaurant in Chicago . where he often attend twice a week to evaluate the service. Michael Jordan recently Open up his on golf company which is name after him. This newest venture Attracts millions of golfers. Michael Jordan also has his own line of golf clothing. As you can see Michael Jordan is a very busy man. He is as active off the court as he is on the court. On the court Michael Jordan is the same type of man. He takes care of his busy first then after it time for fun. Michael Jordan is as the CNN states the greatest athlete ever to play the game, he is best there is, the best there was the best there every will be. Michael Jordan is basketball; basketball would completely drop a level when or if he leave. The NBA doesn't have a player that has the skills, accomplishments, or character of a Michael Jordan. Everything he brings to the game is unique. Michael Jordan is 35 years of age. Jordan ranked 1st in the NBA in points per game averaging (28.7), ranked 16th in the NBA in steals per game averaging (1.72), ranked 17th in the NBA in minutes per game averaging (38.8). These are just the 1997-1998 season statistics. With this short introduction of Michael Jordan one would ask himself why would one consider him as over paid. Why won't I give him what he deserves as a busy man and an athlete. Jordan is the greatest of the game. The NBA deprive not only themselves but also the fans of seeing the phenomenal, the dominant, the supreme, and the optimum of

Chinese Philosophy and Poetry Essay

One of the most prevalent beliefs of the Chinese philosophies is that men are born good. People are naturally good unless they fail to develop their feelings and senses. Confucianism teaches that a lack of knowledge can be the cause to evil. In Poem 238, a woman named Chiang Yuan gave birth to the human race by sacrificing and praying to God. She bore her child easily because she sought after blessings from God. Confucianism teaches that good things will come to those who are good and do good. A. N. Whitehead’s quotation of a Cambridge vicar says, â€Å"For well-conducted people, life presents no problems.† The mother in the poem seemed to be well-conducted and therefore she was blessed with a painless, easy childbirth. It says that God gave her ease and blessed her because he was pleased with the sacrifice and prayer. The poem paints a picture of how even the animals protected the baby. This might be because of their view of being born flawless and without evil. This baby was protected by the animals because there was no evil in him. Hou Chi, the baby boy, grew up to be a wise man and continued to be blessed and prospered through his crops and farming. He would be considered to be the â€Å"superior man† because of his moral wisdom and his ability to tell right from wrong. Because he lived by his mother’s example and gave sacrifices to God, good things came to him. The power of moral example is strongly shown here. His mother first taught him the importance of sacrifice and prayer and through that, he learned to do the same and show respect and fear God. Another philosophy is the importance of filial piety and reverence. Parents are revered because they give life to their children and sacrifice much for them. The child brought honor to his mother by keeping her religious traditions. God was pleased by his actions and blessed him his entire life. This is a very ideal form of what a man can be like, but it is very unrealistic. If man were actually born good, then evil would not have such a major influence and affect everyday lives.

Team Work Effectiveness

There are many standard to evaluate where a team work effectively. According to Mcshane, Olekalns, Travaglione (2011), a team is effective when it benefits its organization, its member and its own survival. Based on my experience I feel that a team work effectively not only when it finishes its objective on time and accurately, but it also motivates its member in order to survival. Motivation can increase responsibility of members in working. Being responsible in teamwork is very important because teamwork is a group of people who depend on each other to finish objective. As a result, when any members are not responsible for their tasks other people will feel uncomfortable or even angry, which can influence negatively to results. For example, in week 5, our team played role as ML team which had to provide ID and pass of keepandshare website for whole class. Unfortunately, we got trouble when account had not worked. Additionally, this is the first time we work in team; therefore, some problem still happen. To be more specific, sometimes in our team, there are still some arguments contributes to debate and then, members feel uncomfortable and we did the tasks not very well. What is more, an effective team also depends a lot on leadership. Mcshane, Olekalns, Travaglione (2011) claim that an effective leader always want to create good working environment for members and support them willingly instead of put leadership on the position of power. During short-time I led my team, I’ve learned that this point is reality because, for example, sometimes I did not understand feeling of other members in my team so some feel unconfident and results were affected negatively. There are some solutions to make a team more effective. Firstly, an effective team should have clarity of direction. Each team member has to have a specific purpose when working so as to focus on the main jobs. When everyone has a clear goal, the team can work efficiently, so the team effectiveness could be greater. Secondly, it is necessary to create an appropriate structure and strict rules to build an effective team. There are some different people in one team so that there are competing interests and personality clashes in team and that could lead to conflicts between members. Therefore, leader has a very important role in the team. The leader must contribute some rules to clarify for the team what is and is not acceptable behavior. Furthermore, leadership requirement for contributing an effective team is that the team has the appropriate skills to succeed. A common think in constructing teams is that having enough members to work on the objectives will lead to team effectiveness. It is a common mistake because more people does not mean more effective if they are not the suitable people for what is being asked to do. The leader is responsible for making sure that the team has sufficient skills and resources to fulfill its submissions. Finally, one of the most important factors of contributing team effectiveness is the respect. Each team members in one team need to listen many different ideas of other members and respect their ideas. Arguments in the team are good when that lead to an agreement between team members at the end. Each team members should be polite in communication to avoid conflicts which can make the team ineffective.

Z Score

MN 215 A & B October 02, 2012 Z Scores, Z Tests and t Tests Overview and Review At the beginning of the course we learned that there are two branches of statistics, namely, parametric and non-parametric. Further we learned that parametric statistical processes are broken down into two other categories, namely descriptive statistical processes and inferential. We learned also that descriptive statistics (mean, mode, median, standard deviation, and frequencies) are only to be used to describe the characteristics of the data rather than draw conclusions of make inferences from the measurement data collected.However, the importance of descriptive statistics cannot be undermined as they form the basis for the workings of inferential statistical processes – especially the mean. In data analysis one of the most important concepts to remember is that regardless of the topic or issue being investigated all is based on the mean of a data set. Although we cannot draw conclusion or make p redictions from descriptive statistics their usefulness in inferential statistics is significant.As stated inferential statistics is a branch of statistics that is used in making inferences about traits or characteristics of a greater population on the basis of sample measurement data. The primary goal of inferential statistics is to leap beyond the measurement data at hand and make inferences about a greater population. Take for example a psychologist who is interested in knowing whether a new behavior modification product will likely be a seller in a certain market area.Knowing that the entire consumer population cannot be queried as to market acceptance, the psychologist would select a representative sample for the area, administer whatever measurement instrument is necessary to garner the data and, on the basis, of the sample data results, determine whether or not the new product will be profitable. The statistic used to determine whether or not the sample is representative of t he entire market population would be an inferential statistics.When using inferential statistical processes to generate information in order to make predictions about a larger population the chosen sample must always be on the basis of random selection or random assignment. Without random sampling or random assignment the mathematical values received by way of the statistical analysis are in err. Or, another way of putting is to say that the results would be â€Å"Lies, damn lies† about the data analyzed. For convenience purposes throughout the remainder of this course the following symbols will be used most extensively.Statisticians, regardless of area, use English letters to denote sample statistics and Greek letters to symbolize population parameters. NameSample StatisticPopulation Parameter _ MeanX  µ (mu) VarianceSD? ? 2 (sigma squared) Standard DeviationSD ? (sigma) Correlationr ? (rho) Proportionp ? (pi) Regression Coefficient b ? ( beta)? ? When trying to arrive at conclusions that extend from the measurement data alone, inferential statistics are the data analysis tools of choice.For example, inferential statistics are used to infer from the sample data to the larger population data or when there is an need to make judgments of the probability that an â€Å"observed† difference between groups is an accurate and dependable one and not those that happened by chance alone. In order too accomplish that which inferential statistics were designed two models are available: estimation testing and hypothesis testing. In the estimation model the sample measurement data is used to estimate a parameter (population) and a confidence interval about the estimate is created.The confidence interval is basically the range of values that has a high likelihood of containing the parameter. The parameter is a numerical value that measures some part or the population measurement scores or values. The second use of inferential statistical processes is in hypo thesis testing. The most common manner in which a hypothesis is tested is by developing what is commonly called a â€Å"straw man† which is what a null hypothesis is call when looking at a situation where in the research investigator wants to determine if the data collected and analyzed is strong enough to reject the null or â€Å"straw man† hypothesis.Always remember that a null hypothesis is stated that no differences, effects or relationships will occur between and or amongst the events, occurrences, phenomenon, items, or situations being evaluated and measured as a result of some variable. A simple example of a business null hypothesis would be something like the following: There exists no statistically significant difference between widgets made of alloy A and those made of Alloy B in terms of tinsel strength acceptability. Data Requirements When Using Inferential Statistics.Thinking back to the first part of the course we learned that statistical processes must u se certain forms of numeric measurement data and this data is expressed as nominal, ordinal, interval and ratio. For descriptive statistics (frequencies and measures of central tendency) it is nominal data that is used. For inferential statistics the measurement data types to be used are either interval or ratio. However, in the social sciences and business arenas ordinal data is often times treated like interval. This is particularly true when studies attempt to assess situations by way of a Lickert scale.For convenience and review the scale presented below will help to clarify the differences between the four scales of measurement discussed earlier in the course. Indications Indicated Direction ofIndicates Amount of Absolute Difference Difference Difference Zero NominalX OrdinalX X IntervalX X X RatioX X X On the basis of the information contained in the table above the following two conditions apply when using inferential statistical processes: * Participants selected for partici pation in a study should be selected randomly. If sampling is not random, then biases occur and contaminate the accuracy of the findings. The most commonly used inferential statistics that behavioral research uses are those statistical processes that provide for the determination of relationships (correlations), differences and effects between and amongst that which is being measured or evaluated. The specific tests used are the Pearson Correlation Coefficient, Chi Square, Student â€Å"t† Test, ANOVA (Analysis of Variance), and regression. All of these techniques not only require the use of a null hypothesis but independent and dependent variables as well. Z ScoresCalculating the Z Score for Research Purposes. One of the most often used statistical processes in the behavioral sciences is the Z Score. What a Z Score accomplishes is in taking a raw measurement value or score and transforms it into a standard form which then provides a more meaningful description of the individ ual scores within the distribution. This transformation is based on knowledge about the population's mean and standard deviation. Take for example an educational psychologist who is interested in determining how individual students are comparing to the overall group of students with respect to grades.As we have learned before raw scores alone cannot provide insightful information to the psychologist how well an individual student is performing. However, what the psychologist can easily do is calculate a Z Score for each student and determine whether or not an individual student is functioning above or below the mean grade of all students together. When determining the placement of each individual, the Z Score permits the psychologist to calculate how many standard deviations, or the distance, each student is above or below the mean grade of all students together.If there is an academic standard the psychologist is using as a comparative base a different statistical formula is used c ompared to the formula needed when comparing individual performance to a local sample of student. The formulas for each are presented below. Comparing Individual to Population Standard Comparing Individual to Sample Standard The construction of the two formulas is the same with the exception that one uses the mean and standard deviation of a population and the other of a sample.What is very important to remember, especially for the psychologist, is that comparing an individual to a local academic setting may have entirely different results when the same student is compared to the industry standard. Although this might appear to be a dilemma, it is actually a possible â€Å"blessing in disguise†. Take for example the same psychologist compares all his students’ rate of academic success in a local facility and determines they are all functioning well above average, or above the mean, in their grades.What happens if the same students are compared to an academic standard a nd the results show their grade is well below the industry standard or population mean? The conclusion drawn is, therefore, that the students, although having grades are not in line with other educational facilities and corrective programming to increase the performance rate must ensue. For ease of understanding let us look at a business situation. Example. Suppose an employee is producing 3. 5 widgets per hour and the sample average number of widgets per hour is 2. 3 with a standard deviation of 0. 33. The Z Score would be calculated as follows: X = raw score X bar = mean s = standard deviation From this we can conclude that the employee’s widget production rate per hour of 3. 5 lays 1. 73 standard deviations above the mean. We can conclude further that this employee is function above the mean all others together on the production line in terms of widget production and that the employee is doing better than 95% of the other employees and only 5% of the total employees are pr oducing more widgets.NOTE: The percentages are easily found on the back of the very last page of your text book. As stated earlier caution must be exercised when drawing conclusions about a single business sample as the statistical information garnered might not be representative of industry standards. Looking at the same employee on an industry standard basis the information might possibly be different. Taking the same employee with an average widget production rate of 3. 5 widgets per hour with a hypothetical population or industry standard mean of 4. 9 and a population standard deviation of 1. 15 the results would be as follows using the formula stated above: X = Employee raw production score  µ = Population standard mean ? = Population standard deviation Z = (3. 5 – 4. 79) / 1. 15 = -1. 12 What can be readily seen by way of the negative value Z Score is that the employee falls below the standard industry mean with respect to the number of widgets produced in one hour. C oncluding further we can say that this employee standing is surpassed by 64% of the entire population workforce for he company. Needless to say, the manager needs to take a serious look at the quality of workers in his/her plant. Interpreting the Z Score for Research Purposes. When using Standard Z Scores one must always remember that comparisons are made between individual measurement values and sample or population mean values. At no time can a one use Z Score values to make predictions or drawn inferences about any given situation. To accomplish this, inferential statistical processes must be used.The value of the Z Score lies in the idea that individual tracking is necessary and trends can be plotted. Also, one must always keep in mind that X values do not have to be simple individual raw scores but can also reflect any investigative variable the researcher chooses to investigate. â€Å"Z† Test When to use the Z Test over the â€Å"t† Test in Research. Although bot h the Z test and the â€Å"t† test are used in research decision hypothesis testing each is used under a different set of circumstances than the other. The primary distinction between the two lies in the sample size requirement.Where â€Å"t† tests can be used for small samples the Z Test cannot and is, therefore, reserved for sample situations that are larger. Both, however, perform the same function, namely to determine whether or not there are differences between the samples being evaluated or comparisons between sample and population measurements. In addition both the Z and â€Å"t† tests make use of the mean scores for raw measurement data when calculating differences. Presented below are some examples of using both the Z test and â€Å"t† test in business today. Z Test: A product safety engineer wants to investigate the average number of possible defective products in worldwide production. A sample is drawn sample (in excess of 30) and mean of the s ample is compared to the population mean for evaluation. * Z Test: A psychologist wants to investigate whether or not a 10 hour shift will record more safety accidents in product production compared to the company wide population standard of eight hour shifts. * Z Test: A human resource manager wants to investigate whether or not a new employee training program will increase production numbers company wide. â€Å"t† Test: A psychologist wants to investigate whether or not the sample of 20 line employees of plant A are producing a significantly greater number of products than the sample of 20 employees of plant A. * â€Å"t† Test: A consumer product safety manager wants to investigate whether or not his small firm is producing an equal number of safe products compared to the industry standard. * â€Å"t† Test: A human resource manager is interested in knowing if customer service skills of employees in department A are the same as in department B.What is most impo rtant to remember is that both the t and Z tests are formulated to arrive at the same conclusion but under different sampling conditions. Keep in mind as well that the Z test is used when the population mean is known. In addition when using a â€Å"t† test with a small sample base it is assumed the distribution of the data is normal; however, in larger samples the distribution does not have to be normal and a Z test can be used for comparative purposes. Further, in both situations the samples drawn must be on a random basis.The unfortunate limitation of both tests is in the fact that neither permit any conclusions to be drawn if not differences are found between the sample means or sample and population mean. However, one must always keep in mind that Z and â€Å"t† tests are basically the same as they compare two means to determine whether or not both samples come from the same population. Calculating the Z Test. The example presented below not only provides you with a formula for both population mean testing but sample mean testing as well.What must be closely watched is the effect on sample size with respect to any resulting Z value: Remember that the Z test requires a large sample and should a small sample be used the resulting Z value is contaminated. Formula: Sample vs. PopulationSample vs. Sample __ __ __ Z = / Z = X1 – X2 N 2(1/N + 1/N) Example Sample vs. Population:Suppose a product manager is interested in knowing if the number of faulty washing machines being produced in his/her plant in August is indicative of the over-all number of washing machines produced in all plants during the month of August. The product manager draws two samples from his/her assembly line: a sample of 10 and a sample of 100. The example being created is to show how the size of the sample bears directly on the resulting Z Test value. Formula: __ _ Z = / N Data.Sample Test Mean = 30 Population Mean = 25 (Industry Requirement) Population = 15 N = 10 __ Z = / N = 30-25 / 15 / 3. 16 = 15 / 4. 75 Z = 1. 58 Sample Test Mean = 30 Population Mean = 25 (Industry Requirement) Population = 15 N = 100 _ Z = / N = 30-25 / 15 / 10 = 5 / 1. 5 Z = 3. 33 Conclusion: The conclusion the production manager can draw from the above measurement example (N=10 and N=100) is relative to the size of the sample used to determine whether or not the sample is representative of the overall faulty washing machine production in August.Had the production manager set the level of confidence at 0. 01 (99%) the Z test score needed in order to reject the null hypothesis that no differences exist in washing machine production is +1. 96. A Z test value for the 10 sample situation of +1. 58 does not meet or exceed the required value of +1. 96. Therefore, the production manager concludes there is not statistically significant difference in the August faulty washing machine production rate for his/her plant and the overall faulty washing machine production rate of all plan ts.However, when the sample size is increased the resulting Z test value is extremely different. The 100 sample case, using the same values as in the 10 sample case, provides an entirely different scenario. By increasing the sample size tenfold the resulting Z test value is +3. 33. Obviously this numeric value far exceeds the required +1. 96 value and the production manager can safely conclude that statistically significant differences exist between the faulty washing machine productions in the production manager’s plant compared to the average faulty washing machine production rate of all plants.The reason for the difference in Z test values in knowing that as sample size increases so does the Z test value. Although not shown in this example, but also extremely important, is in knowing that when the variance of the sample differs from the population variance there will exist a lower Z test value. In the 100 sample test, should the resulting Z test value not met the required 1. 96 value the production manager could have concluded that the faulty washing machine production rate of his/her plant meets the production rate of all other plants together for the month of August.As scientific research and applied statistics application are not equipped to lend explanation as to why no differences are determined the only conclusion to be drawn is that the lack of differences is a direct result of sample size and variance. Example Sample vs. Sample vs. Sample Formula: __ __ Z = X1 – X2 2(1/N + 1/N) Example: Suppose the same product manager is interested in knowing if the number of faulty washing machines being produced in his/her plant in August is indicative of the number of faulty washing machines produced in a neighboring plant during the month of August.The product manager draws two samples: one from his/her assembly line and one from the neighboring plant: a sample of 100 is drawn from both plants. _ Data: : Sample 1: N=100 X=30 _ Sample 2: N=100 X=25 = 15 (known or assumed) _ _ Z = X1 – X2 2(1/N + 1/N) = 30 – 25 / (15)? (1/100 + 1/100) = 5 / v (225) (. 01 + . 01) = 5 / 4. 5 = 5 / 2. 12 = 2. 35 Conclusion: On the basis of the Z test value above the production manager would have to conclude that there exists a statistically significant difference in the production rate of the two plants at the . 1 confidence level (99%) as the required critical value of 1. 96 was matched and exceeded. As such it can be stated that the two washing machine samples are not representative of each other and differences occur. Should the product manager replicate the study and use only 10 washing machines per sample the resulting Z test value would be 1. 11 and the conclusion drawn would be that no statistically significant differences are present between the two groups and the population.Again this is an example of how sensitive the Z test is to sample size. One must always keep in mind that re-testing a product or service with artificia l conveyances (smaller sample size) in order to show that differences are not present is scientifically and professionally unacceptable. Research results must be allowed to fall wherein the statistical analysis places them. Doing otherwise is using the statistical process for reasons other than that which they were intended Drawing Conclusions from the Z Test.Business situations are not unlike any other professional situation, including the behavioral sciences, wherein the researcher or investigator is seeking information as to possible differences between samples or sample and the general population. When business managers or psychologists at any level are interested in making comparisons between products and or services the best-fit statistical tool for large sample situations is the Z test. However, the statistical value is only as good as the controls placed on it and at no time will the actual values give a reason as to why something has happened or why something has not.With r egard to the utilization of the Z test in business decision-making the following rules are always to be remembered: * Z Tests can be used to compare a sample to a population or sample to a sample for general population inference. * Z Tests are extremely susceptible to size of sample and variance and not useful when population variance is unknown. * Z Tests work best with very large samples but not with small samples as the correction factor cannot accommodate for the error associated with small samples. Z Tests are natural introductions to t Tests. * Z Tests work with only one (1) dependent variable. * Z Tests cannot work with correlated data. * Z Tests do not permit the making of strong inferences about differences or effects of the testing instrument or situation. * Z Tests have a non-parametric counterpart wherein small samples can be used. â€Å"t† Test 1a. Introduction to Difference Testing. Difference testing is used primarily to identify if there is a detectable differ ence between products, services, people, or situations.These tests are often conducted in business situations to: * Ensure a change in formulation or production introduces no significant change in the end product or service. * Substantiate a claim of a new or improved product or service * Confirm that a new ingredient/supplier does not affect the perceived attributes of the product or service. * Track changes during shelf-life of a product or the length of time of a service. Differences Between Two Independent Sample Means: Coke vs. Pepsi. Let us again look at a business example wherein the independent sample t-tests are sed to compare the means of two independently sampled groups. Example: do those drinking Coke differ on a performance variable (i. e. numbers of cans consumed in one week) compared to those drinking Pepsi. The individuals are randomly assigned to the Coke and Pepsi groups. With a confidence interval or ?. 05 (corresponding probability level of 95 %) the researcher c oncludes the two groups are significantly different in their means (average consumption rate of Coke and Pepsi over a one week period of time) if the t test value meets or exceeds the required value.If the t value does not meet the critical t value required then the research investigator simply concludes that no differences exist. Further explanation is not required. Presented below is a more useable situation. Example: As a manager of production let us suppose you are wanting to determine whether or not work performance is significantly (statistically) different in a noise related production line vs. a non-noise related production line. Individual Noise Production Non-Noise Production difference: 1-2 38 32 6 2 10 16 -6 3 84 57 27 4 36 28 8 5 50 55 -5 6 35 12 23 7 73 61 12 8 48 29 19 Mean 46. 8 36. 2 10. 5 Standard dev 23 19 12 Variance 529 361 N = 16 Using the raw data and formula above to calculate the t test value the actual t test value, when calculated properly, is 2. 43 . Always remember that S = Standard deviation and that the mean is often times shown by the capital letter M rather than a bar mark over a capital X.By going to the appropriate t tables in your text book find the critical value for t at the . 05 confidence interval. The value you should find is 1. 761 Differences Between Two Means of Correlated Samples: Red Bull vs. Power Drink. Again using a business example correlated t test statistical processes are used to determine whether or not there is a relationship of a particular measurement variable on a pre and post test basis. Often times when there exists a statistically significant relationship on a pre and post test basis the business manager can use the first measurement values to predict the second in future situations without having to present a post test situation.Example: Using the same data presented above let us assume that there are not two independent groups but the same group under two different conditions – noise p roduction environment and non-noise production environment. Individual Noise Production Non-Noise Production difference: 1-2 1 38 32 6 2 10 16 -6 3 84 57 27 4 36 28 8 5 50 55 -5 6 35 2 23 7 73 61 12 8 48 29 19 N = 8 The first step is to compute the mean of the differences: _ D = ? D N The second step is to square the differences: (6)? + (-6)? + (27)? + (8)? + (5)? + (23)? + (12)? + (19)? The third step is to calculate the standard error of the difference: SED = _ ?D – D? / n -1 n The last step is to compute the t test value: _ t = D / SED Using the raw data and formula above to calculate the t test value the actual t test value, when calculated properly, is 3. 087. By going to the appropriate t tables in your text book you can find the critical value to be, at the ? .05 confidence interval is 1. 895.The conclusion drawn is that the differences are statistically significantly different. When to Use Independent Mean or Correlated Sample Difference Testing. In research inv estigation situations the choice of using an independent sample t test of a correlated sample test is dependent upon whether or not the investigator is seeking to determine differences or relationships. In some situations the need to know whether or not a difference exists between two products or services is more important than knowing if there is a relationship between the two. For example: take the consulting psychologist wants to know if training program A has better success in training managers than training method B.The psychologist would select a sample of each training situations (generally ;30) and test the success of each sample and compare the success of program A with program B. The results would confirm if one training programs was better that the other. If, however, the psychologist was interested in determining how each program compared to the industry standard the programs would be compared, independently, to the population program mean. On the other hand should the c onsulting psychologist wants to determine whether or not a relationship exists, or predictability can be determine, from one program in two different situations or under two different situations a correlated t test is used.However, knowing the relationship in pre and post test situations are generally reserved for improvement situations. Drawing Conclusions for the t Test. Any conclusion drawn for the t test statistical is only as good as the research question asked and the null hypothesis formulated. â€Å"t† tests are only used for two sample groups, either on a pre post-test basis or between two samples (independent or dependent). The t test is optimized to deal with small sample numbers which is often the case with behavioral scientists in any venue. When samples are excessively large the t test becomes difficult to manage due to the mathematical calculations involved.

Positive contributions of the Aged to the community

Group you have studied describe the positive contributions that the group makes to the wider community The Aged make various positive contributions to the community, many which go unseen. Although the Aged are perceived to be a burden to society due to the image in which society depicts, their contribution to benefiting those around them is unending. The Australia bureau of statistics defines the Aged as those who are 65 years and older.As this stage in the lifespan is usually associated with retirement and spare time the elderly find themselves giving back to he community through educating younger generations, participating in volunteer work and providing informal services by assisting in caring for young family. The Aged have much to contribute to the community as they have a wealth of experience and are able to pass on education to the younger generations. In doing so, they are positively influencing society and guiding them to be better people and to live their life to the fulles t.As young people aren't aware of the consequences of some of their actions, the Aged can contribute their knowledge on this aspect of their lives, which minimizes the risks that younger generations are faced with, and In turn prepares them for their future. Many elderly people also devote their time and knowledge to younger generations by being guest speakers at organizations and schools through sharing their experiences, knowledge and skills. This positively Influences and educates the community which leads to not only an Improved relationship between the Aged and society, but also provides guidance and support to younger people.One of the greatest resources that they provide the community with Is their time. Many Aged people devote their time to society by continuing to work, which contributes to the economic growth, or by participating In volunteer work. Once the Aged retire they find themselves feeling lonely and Isolate from society. In order to overcome this many turn to cont ributing to the community by doing volunteer work. Volunteer work has become Increasingly popular amongst the Aged as It provides the elderly with social Interaction with employees, and also those In the community.A study from the Australian Bureau of Statistics found that In 1997 older people spent round 30% of their working hours on unpaid volunteer work. The Aged also contribute to the community by participating In fund raising. Even though the Aged are usually characterized with qualities such as deteriorating health and Increased body aches they still manage to give up their time to the community and contribute to the economic growth. The Aged provide an Informal service by assisting with care for grandchildren and other young people. In today's society women are Increasingly focusing more on their careers Instead of child bearing.As this means that but others and fathers are spending more of their time on work, It proves to be very helpful to have the Aged to care for their ch ildren. This not only benefits the community but also allows the elderly people to collapse with their grandchildren and/or family. Positive contributions of the Aged to the community By Kristin minimizes the risks that younger generations are faced with, and in turn prepares sharing their experiences, knowledge and skills. This positively influences and educates the community which leads to not only an improved relationship betweenOne of the greatest resources that they provide the community with is their time. Contributes to the economic growth, or by participating in volunteer work. Once the Aged retire they find themselves feeling lonely and isolate from society. In order to Volunteer work has become increasingly popular amongst the Aged as it provides the elderly with social interaction with employees, and also those in the community. A study from the Australian Bureau of Statistics found that in 1997 older people spent contribute to the community by participating in fund raisi ng.Even though the Aged are usually characterized with qualities such as deteriorating health and increased to the economic growth. The Aged provide an informal service by assisting with care for grandchildren and other young people. In today's society women are increasingly focusing more on their careers instead of child bearing. As this means that but mothers and fathers are spending more of their time on work, it proves to be very community but also allows the elderly people to socialist with their grandchildren