How To Clean The Bathroom Essay -- essays research papers

The first thing you need to do is gather all the supplies you will need. There are many different cleaners in which you will be using. These cleaners are glass cleaner, surface cleaners, soft scrub, and ammonia. You will not only need cleaners but also implements to clean with. These are a mop and bucket, broom, cobweb brush, toilet brush, rags, a sponge, a squeegee and a step latter. Last but not least, you will need a cleaning carrier to put your supplies in to make it easier to move them around . In you carrier should be all the cleaners except the ammonia, the toilet brush, sponge, a squeegee and a few rags. Now that you have the cleaners in carrier you are ready to get started. Start by useing a web-brush to get down any cobwebs that many be in the corners of the ceiling. Fill the sink with hot water and let any soap dishes or toothbrush holder soak. Now get the bottle of glass cleaner, sponge, squeegee, a glass rag, and a terry cloth rag and get into the shower. Begin by spraying down the tile and use the ruff side of the sponge to loosen any soap scum or build up. Using a terry cloth rag and wipe the tile clean. Now spray down the glass and again use the sponge to loosen any build up. Use the squeegee to take off the majority of the cleaner off and a glass rag to wipe any marks left. Make sure to look at the glass from all directions to be sure you didn’t leave any marks. Polish the shower head and knobs so they shine. Now scrub floor well and wipe dry as you get out...

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.

Child s Mind And Brain Process New Information - 1566 Words

Jean Piaget (n.d.) has mentioned that â€Å"Knowing reality means constructing systems of transformations that correspond, more or less adequately, to reality.† This believed to apply to every human being including children. Cognitive development is a neuroscientific and psychologic study of a child’s development on how they process the information, uses of the mental resources and skills, learning different languages and other characteristic that a child will develop comparing to an adult’s brain. For decades, different psychologist such as Jean Piaget, Jerome Bruner, and Lev Vygotsky have researched and created different kind of theories that helps the adults to understand how a child’s mind and brain process new information especially in†¦show more content†¦Hidden curriculum is a type of lessons that are not included in the formal curriculum and are unintentionally taught by teachers. While the formal curriculum contains the topics for each of the subjects and learning activities, hidden curriculum contains lessons that students pick up by themselves. For example, from the class, a student can learn how to write English and how to calculate Mathematics but they can also learn values such as honesty and how to interact with their peers and how to respect elders such as teachers. These are all included in the hidden curriculum because it is not written in the formal curriculum to teach thus the students automatically learns from the school surroundings. Marsh et al (2014) states that in Piaget’s theory students â€Å"†¦movement through these stages depends on the quality of their environment.† The right environment is important because students can trust and understand what is right and wrong and school are and should be one of great example of being the right environment for a student. The connection between the Piaget’s theory and the hidden curriculum is that a developing child understand and e volve to their surrounding more quickly than adults and hidden curriculum that is taught in the right environment can mold these knowledges to lead the child on the right path and make strong citizen which is one of the main aims of education in a society. From Piaget’s theory states that learning needs to be engaging both

Iran s Support For International Terrorism - 3050 Words

Introduction The 1979 Revolution in Iran ended a truly symbiotic relationship between the United States and the Shah, thus creating the beginning of a longwinded road of economic sanctions imposed upon Iran. â€Å"As a result of Iran s support for international terrorism and its aggressive actions against nonbelligerent shipping in the Persian Gulf, President Ronald Reagan, on October 29, 1987, issued Executive Order 12613 imposing a new import embargo on Iranian-origin goods and services† , and for when rogue Iranians took American citizens hostage at the embassy, releasing them months later, the relationship began to become strained. In following the history of the economic sanctions, I’ll begin with an outline of the United States’ most†¦show more content†¦citizens from entering into jobs requiring the financing or supervision â€Å"of the development of petroleum resources located in Iran or in an area over which Iran claims jurisdiction† 8) the May 1995 ban on exportation of goods to Iran from the US of anything types of goods, technology, or even trade services such as financing by United States banks 9) the August 1996 signing of the Iran and Libya Sanctions and finally 10) the last executive order of August 1997 in which we confirmed that all trade and investment activities with Iran by banks, companies, or any persons are formally prohibited. Research Design This paper will be defining and critiquing the problem at hand through use of a case study and analysis of the literature at hand to help prove the theory that sanctions have been ineffective throughout our historical use of them. It will identify the problem as being one of nuclear deterrence to solve for Iran’s threatening stance on nuclear power. It will then go on to argue that the United States and all involved allies should employ soft power peace talks to negotiate trade and settle debate on Iran’s past of nuclear

Essay about Cultures Affecting Our Daily Lives - 1086 Words

Cultural differences effect many aspects of our daily lives. With the many different cultures embedded in the United States, it is important that everyone has an understanding and respect for the melting pot that is encountered daily in our communities, workplaces and schools. Culture is not an exotic notion studied by a select group of anthropologists in the South Seas. It is a mold in which we are all cast, and it controls our daily lives in many unsuspected ways... Culture hides much more than it reveals, and strangely enough what it hides, it hides most effectively from its own participants. (Hall) The United States populated from the beginning with people from different cultures. There were Irish, German, Italian and†¦show more content†¦Today with all the multicultural shopping opportunities people of all backgrounds patron other culture groups businesses. Multicultural businesses affect our lives by giving us opportunities to experience different market places and learn a different traditions or ways that make our lives more productive. The workplace has become one of the biggest multicultural settings. Almost half of the work force in the United States is made up of minorities. There are many advantages to having this percentage of people from different cultures throughout our workplace. One is the increase in creativeness. Not only in products that are being created but in the developing of an approach to solving problems. Other cultures can offer insightful alternatives (EthoConnect). Another way in which the workplace is affected by this is in productivity and attitudes. When people of all cultures pull together to reach a common goal there are less limited boundaries and there are more global understanding that are used to help reach the worlds market place; creating a larger market for products. 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Demand and Supply of Milk and Factors

Question: Discuss about the Demand and Supply of Milk and Factors. Answer: Introduction: Demand and Supply are the primary factors that decide the price of a product in a market. The price mechanism analysis is an important economic tool that is used to decide the price of a product using the demand and supply factors taking place in the market. On the other hand, demand and supply of a commodity is influenced by several other factors that prevail in the market (Smithers and Augustin, 2012). Hence, to observe the pattern of change in the price, it is important to understand the changes in the factors that influence the demand and supply of the product. Hence, the paper has been developed to analyse the factors that influence the demand and supply of Milk in the Australian market and explain the current issues of price going on in the Milk industry of the country (Sajadieh and Akbari Jokar, 2009). Lockhart, Donaghy and Gow (2016) found that a structural change in the global diary industry has been evident during the 2015 that acted as a perfect storm to shatter the price of milk in the country. The coincidence of technology, events and policy changes in significant dairy producing countries including Australia has resulted in a long run reset of the diary economics all around the world. The cooperatives Fonterra and Murray Goulburn have reduced the prices that are offered to the dairy farmers that resulted in a backlash from the milk producers in the country (Marini, 2007). Hence, the primary objective of the study is to observe the incidents that resulted in the changes in demand and supply of milk products in the Australian market. Along with that, the study focuses on explaining how these changes in the demand and supply impacted the price of the necessary commodity. Furthermore, the paper explains the demand side factors and supply side factors that influence the aggregate demand and supply of milk in the Australian market. Due to the structural change in the global dairy industry, the demand and supply scenario of milk have been reset. In the study article, the current status of the Australian dairy sector has been described showing the supply and demand side features. Understandably, long-term reforms and policy of the dairy market at the global stage has contributed to the dramatic changes of milk price (Yang et al., 2013). At the existing market scenario, a decline in prices of milk has been one of the major issues for the dairy farmers operate in the Australian dairy sector. Dramatical changes occurred in the global dairy economics can be termed as the primary reason for a price reduction. Moreover, there are other contributory factors as well, to say the least. Australia, one of the major dairy producing countries of the global economy has seen higher milk prices (Chin, 2015). Currently, the short-term oversupply of milk has been blamed as the most convincing reason for price dropdown. Admittedly, reduction of demand for milk has fuelled the situation of oversupply in the Australian market. As a result of the scenario, the biggest cooperatives of Australia i.e. Murray Goulburn and Fonterra have offered reduced milk prices to the dairy farmers (Lockhart, Donaghy and Gow, 2016). In the underlying section, the oversupply scenario and its impact on demand and price of milk have been stated. An excessive quantity of milk has generated a massive gap between aggregated supply and aggregated demand in the Australian dairy sector. As the short-term demand for milk has reduced in compared to the supply flow, the surplus has become the evident result. Due to sluggish demand for milk from China and Russia, the global dairy sector has faced significant challenges. Major dairy producing countries such as Australia has ignored the reduction in demand at the global stage to produce a massive amount of milk (Eilon, 2013). As a result of the scenario, Australia has to face issues such as oversupply of milk. As shown in the given figure, at the initial stage of the supply phase, the demand for milk was at Qs at price P1. As the demand is reduced due to global changes in the dairy industry to Qd, the price has revised to P. Meanwhile, during the period of the time, supply has been increased at the same rate resulted in oversupply as marked in the graph. In the contemporary market scenario, the global surplus of milk has contributed to the short-term decline in demand and price of milk in Australian dairy sector. Meanwhile, the downturn in the US dairy sector may restore the demand and supply of global dairy industry (Thom, 2011). Currently, the supply situation of the United States of America has significantly contributed to the low price of milk. Such international factors have added extra pressure to the domestic Australian milk producing farmers (Hodgson, 2013). Currently, profitability margins of domestic farmers have hit badly, to say the least, due to the surplus scenario of dairy products. Decisively, the dairy market policy of Australia may have been questioned in this context. During the global surplus, production of milk must be reduced to control the scenario at the earlier stages. As a result of a poor form of policy measures, the value of marginal production has majorly hit. Significant aggregated demand and supply term s must be developed by the third largest milk producer in the global market to improve the existing scenario of the surplus of milk (Smithers and Augustin, 2012). Now, in order to understand the primary reason for the changes in price of milk in the Australian market, the demand side factors and supply side factors must be analysed. The demand and supply side factors that influence the quantity demanded and supplied of dairy products in the Australian market are discussed herein below: Demand Side Factors The demand side factors are those aspects that influence the quantity demanded of milk in the Australian market. The demand side factors are presented herein below: Number of buyers: Considering the global dairy market, the demand of dairy products for export from Australia has decreased due to the emergence of new producers in the Asian market. Along with that, the high level of investment by the Chinese investors in Australia has also reduced the demand of Australian producers in the global market (Lockhart, Donaghy and Gow, 2016). Tastes and Preferences: The taste and preferences of customers have changed. The consumption of dairy products has reduced due to health consciousness and contents of fat in the milk products (Whitehead, 2010). On the other hand, the price of milk rises if it is processed to reduce the level of fat. Hence, it reduces the demand of dairy products in the Australian market. Price of related goods: The price of related products such as butter, cheese, and processed milk has risen due to innovative technology that further reduces the demand for dairy products (Whitehead, 2010). Hence, it can be seen from the above diagram that the demand of milk reduces that results in the leftward shift of the demand curve from D1 to D2 that further results in the fall of milk price from P1 to P2 in the Australian market (Parker, 2006). Supply side Factors The supply side factors are those aspects that influence the quantity supplied of milk in the Australian market. The supply side factors are presented herein below: Number of sellers: The number of milk producers has increase in Australia that results in the rise of production of dairy products in the country. The new investment made by the Chinese and Asian investors has directly increased the supply of milk in Australia (Lockhart, Donaghy and Gow, 2016). Technology: The implementation of innovative technology in the dairy industry has resulted in increase in the production of milk products (Lockhart, Donaghy and Gow, 2016). On the other hand, the new technology also increases the production cost. Expectations of producers: The expectation of the milk producers have failed due to immense fall in the demand for milk products in the global market (Xavier Malcata, 2009). It has resulted in oversupply of milk in the country that has further resulted in the fall of price of the product. It can be seen from the above diagram that the rise in the quantity supplied of milk has resulted in a rightward shift of the supply curve from S1 to S2. It has further resulted in the fall of the price of milk products in the market from P1 to P2 (Parker, 2006). Hence, it can be seen that supply, demand, and pricing have been thoroughly interlinked with each other. Meanwhile, the article has described the impact of global changing trends of the dairy industry. In Australia, a short-term surplus of milk production has contributed the demand and pricing. Admittedly, the decline in demand for milk has reduced the price of milk at the same level. The analysis of the contributing factors of declining milk prices has identified the role of policies directed towards Australian dairy sector. Through the discussion of the events, it is quite clear that the poor set of policy measures has massively contributed towards the surplus scenario of milk. In order to improve the challenging scenario, Australian government must provide a subsidy to the major milk producing firms to improve the condition of farmers. Moreover, the global consequences have led to a surplus of milk. On economics perspective, a surplus of milk has contributed to disequilibrium of s upply and demand scenario. Hence, the price of milk has reduced to a significant level. Primarily, as the universal law of supply and demand has been changed during the change of global dairy industry, larger production leads to the short-time dropdown in price. Meanwhile, significant measures and change in the global scenario may develop the condition. References Cappiello, G. (2015). Price Policies, Over-Supply and Demand Bubbles.Symphonya. Emerging Issues in Management, (2). Chin, G. (2015). Differences in strategy across the globe.Science, 348(6241), pp.1328-1328. Eilon, S. (2013). Three price elasticities of demand.Omega, 11(5), pp.479-490. Hodgson, R. (2013). Trends and Needs in the Dairy Industry.Journal of Dairy Science, 56(5), pp.614-620. Lockhart, J., Donaghy, D. and Gow, H. (2016).Milk price cuts reflect the reality of sweeping changes in global dairy market. [online] The Conversation. Available at: https://theconversation.com/milk-price-cuts-reflect-the-reality-of-sweeping-changes-in-global-dairy-market-59251 [Accessed Nov. 2016]. Marini, G. (2007). Price Variability, Supply-Side Policies and Monetary Rules.Economica, 54(213), p.109. Parker, P. (2006).The 2007-2012 world outlook for dairy and soy products. 1st ed. San Diego, CA: ICON Group. Sajadieh, M. and Akbari Jokar, M. (2009). Optimizing shipment, ordering and pricing policies in a two-stage supply chain with price-sensitive demand.Transportation Research Part E: Logistics and Transportation Review, 45(4), pp.564-571. Smithers, G. and Augustin, M. (2012).Advances in Dairy Ingredients. 1st ed. Hoboken: Wiley. Thom, E. (2011). Trends in Dairy Products Merchandising and Their Impact on the Dairy Industry.Journal of Dairy Science, 44(2), pp.365-368. Whitehead, J. (2010). Demand-Side Factors and Environmental Equity Analysis.Society Natural Resources, 13(1), pp.75-81. Xavier Malcata, F. (2009). Critical Issues Affecting the Future of Dairy Industry: Individual Contributions in the Scope of a Global Approach.Journal of Dairy Science, 82(8), pp.1595-1611. Yang, C., Ouyang, L., Yen, H. and Lee, K. (2013). Joint pricing and ordering policies for deteriorating item with retail price-dependent demand in response to announced supply price increase.Journal of Industrial and Management Optimization, 9(2), pp.437-454.

Vladek and the Holocaust Essay Example

Vladek and the Holocaust Essay Maus is a comic book written by Art Spiegelman chronicling his father’s life through the Holocaust. Vladek is an aggravating character he’s stingy, contemptuous, and illiberal. Vladek’s irritating personality is an effect of his experiences during the Holocaust. His selfishness can be blamed upon his experiences where he had to take things to survive, even if it meant taking from others. His distrust is a result of him being betrayed on multiple accounts, and his fixation with money can be linked to how money helped him through the holocaust. The underlying traits of Vladeks personality would have always been there but the holocaust has affected him in a way to bring the aggravating characteristics to surface. Vladek’s selfish character is a side affect of his traumatic experiences where   Ã¢â‚¬Ëœevery man is for himself’. Every soul in the holocaust had to learn to put themselves before others for survival, this severely affected Vladeks character.  Vladek’s egoistic behaviour showed when he climbed onto someone’s shoulders to hang a hammock for himself to lie on, so he wouldnt be killed, or trampled when others were shoved onto the train to the concentration camp. This event in the book really mad clear the importance of putting ones self before others for survival. Vladek’s selfishness when he wanted to give Art a new coat, saying â€Å"It’s a shame my son would wear such an old shabby coat.† making clear it was shameful for his own reputation. Instead of buying Art a new coat out of love, he gave him a coat out of keeping a high image for himself.  He later says, â€Å"I have for you a warmer one. I got at Alexander’s a ne w jacket, and I can give to you my old one; it’s still like new!† This shows that Vladek did not care for arts image but only for his own, buying himself a new coat and letting art have a hand me down. We will write a custom essay sample on Vladek and the Holocaust specifically for you for only $16.38 $13.9/page Order now We will write a custom essay sample on Vladek and the Holocaust specifically for you FOR ONLY $16.38 $13.9/page Hire Writer We will write a custom essay sample on Vladek and the Holocaust specifically for you FOR ONLY $16.38 $13.9/page Hire Writer Vladek is also very cynical and distrustful at times, during the holocaust he was betrayed by m