Auditing Is The Accumulation And Evaluation Of Evidence Accounting Essay

Auditing Is The Accumulation And Evaluation Of Evidence Accounting Essay Auditing is the accumulation and evaluation of evidence about information to determine and report on the degree of correspondence between the information and established criteria. Auditing should be done by a competent, independent party and communicate the results to interested users. (Elder, and others, 2007, p .4) The purpose of audit is to enable the auditor to express an opinion whether the financial statements present a true and fair view in accordance with the identified financial reporting framework, and will enhance the credibility of financial statements. Audits can improve a companys efficiency and profitability by helping the management better understand their own work and financial system. The management, as well as the shareholders, suppliers and financers, is assured that the risks in the organization are well-studied, and effective systems are in place to handle them. Furthermore, audit can identify areas in an organizations financial structure that need improvement, and how to implement the proper changes and adjustments. It uncovers inaccuracies and discrepancies within an organizations records, which may be indications of weak financial organization or even internal fraud, and reduce the risks. (Auditing, n.d.) At present corporate scandals and fraud continues to be a pervasive problem and is very common across the world. However these scandals and fraud are so massive that every time many people especially employees of the company lose their jobs and investors are bearing from major losses in these incidents. Corporate fraud is any fraud committed against a business and is used to include many different wrongs that may occur within a business. Corporate fraud takes many forms, including insider trading, embezzlement, false billing schemes, securities fraud and forgery, Asset misappropriation fraud, Business opportunity fraud, Employment fraud, Short and long firm fraud, Employee fraud etc. Among the most dangerous forms of corporate fraud is known as long firm fraud, which occurs when a business is founded on legitimate grounds, and then lead by certain unethical individuals toward a purpose of defrauding over a long time. (Corporate fraud, n.d.) It is difficult to expect such scandals prior to the exposure as these involve complex methods for misusing funds, overstating revenues, understating expenses, etc. According to studies conducted in the United States, United Kingdom and Australia (2010) states that corporate fraud increased in the previous 12 months and economic conditions have w orsened. The studies also found that the dollar amount lost to fraud also had increased as well. The study conducted by Association of Certified Fraud Examiners (ACFE, 2009) estimated that fraud cost U.S organizations 7 percent of annual revenues, or $ 994 billion in losses based on 2008 gross domestic product. Although financial statement fraud less frequent, however it typically involved more than $ 2 million. Simultaneously, in the United Kingdom KPMGs Fraud Barometer found  £1.3 billion of fraud in 2009. National head of forensic Gary Gill (2010) states that 2009 Australia KPMGs Fraud Barometer found million in major fraud cases. And they highlighted that although the number of cases remain same compared to first to second half of 2009, the value or cost of the fraud doubled. This could weaken investors and shareholders confidence. In order to prevent or reduce from corporate fraud auditors do have a duty to detect errors and fraud hence auditors need to ensure that the financial statements are presented in true and fair view in accordance with the identified financial reporting framework. This research will assist the auditors to find out what they can be done or what would be their responsibility to reduce the massive corporate fraud which, is rapidly spreading around the world. Auditors obtain a reasonable assurance that financial statements are free of material misstatement caused by error or fraud. One of the challenges faced by the auditor in detecting fraud is the independence of the auditor. Some companies management will not allow the auditor to act with integrity and exercise objectivity and professional skepticism. The other challenge faced by the auditor is that the management does not provide sufficient information to detect the fraud. The auditors would not get enough supporting documents to prove that whether the transactions are correctly recorded. Maintaining public trust is also one of the challenges that auditors would face, hence the public has raised the question as to whether audit functions can be trusted, after financial statement fraud committed by big companies such as Enron, Tyco, and World.com. In order to prevent such problems from occurring, this research has been conducted in order to help auditors to enhance audit quality, preve nt and detect potential fraud before it is too late. Brief description of project objectives. (i.e. scope of proposal, constructs used, limitations and significance) Andrew Marshal (2009) said that when it comes to corporate fraud, nowhere is safe, hence there are fraud scandals occurring everywhere in the world. CPA Journal (2008) estimated that a typical organization loses 5 percent of its annual revenues to fraud, or about $ 4,500 per employee each year. Furthermore the CPA Journal (2008) states that most frauds involve a lack of adequate internal controls (opportunity), the need to maintain an expensive lifestyle or pressure to meet goals (incentive), and the perpetrators lack of awareness that their actions are wrong (self-rationalization) or simple lack of integrity. However, investors expect auditors to give them absolute assurance for detecting material misstatement due to fraud. The majority of investors want from an audit absolute assurance the financial statements are free of all types of material misstatement. The main objective or the aim of this research will be on how and what are the methods that auditors can contribute to minimiz e material misstatements from financial statements and reduce corporate fraud. The other objective of this research is to identify the difficulties in detecting corporate fraud and how to enhance audit quality. Besides that this research will evaluate the gaps in internal controls and how auditors can come out with a financial statement without any biasness. Scope of the Study The scope of this study will concentrate on auditors and their responsibilities towards the public. In addition it will focus on fraud awareness, fraud risk and the corporate governance in the organization. Furthermore this study will focus on the abilities and the knowledge which auditors should have to accomplish their obligations. Limitations of the Study The limitations of this research will be the difficulty in collecting primary data; hence this research will be conducted with face-to-face structured interviews and a survey questionnaire. The time limit for the interview may not be enough based on the availability of the participants, therefore interview has to be scheduled for the availability of participants. Significant of the Study This research will facilitate the auditors to maintain public trust and will enhance audit quality. By enhancing audit quality auditors can reduce the corporate fraud and make the companies trustworthy. When audit quality is enhanced auditors will be more responsible in detecting and preventing fraud. And auditors will perform their work more carefully as a result the companies will not experience financial trouble or difficulties. This research will gather information about how corporate frauds can be reduced and auditors as a key people who needs this information to be applied in the corporate world to reduce corporate fraud. Brief description of the models/theories/concepts that will be used in this proposal. (i.e. consumer behaviourism model, CSF and etc.) This project does not require any concepts, theories or models to be used in the process of doing it. E Academic research being carried out and other information, techniques being learnt. (i.e. literature what are the names of books you are going to read / data sets you are going to use) The books that this research will use are Audit and Assurance Books, Internal and External Auditing books. Magazines, online articles and journals which are related to corporate fraud and auditors will be used. Journals include such as Audit disaster futures: antidotes for the expectation gap by Fran M. Wolf, James A. Tackett and Gregory A. Claypool. And CPA journals such as Auditors responsibilities with respect to fraud: a possible shift. In addition this research will use the Audit firms reports such as KPMG. Other relevant sources include: http://www.reuters.com/article/idUSTRE5AJ03S20091120 http://www.reuters.com/article/idUSN1717856320100317 http://www.bobsguide.com/guide/news/2009/Nov/20/Corporate_accounting_fraud_increasing_around_the_world.html http://www.kpmg.com/AU/en/IssuesAndInsights/ArticlesPublications/Press-Releases/Pages/Press-release-fraud-doubles-16-Feb-10.aspx http://www.irs.gov/compliance/enforcement/article/0,,id=213768,00.html http://www.anonymousemployee.com/csssite/sidelinks/corporate_fraud.php http://www.procurement.travel/news.php?cid=corporate-fraud-rises-employees-layoffs-economy.Mar-10.31 http://www.emeraldinsight.com/journals.htm?articleid=868441show=html Brief description of the materials/methodologies needed by the proposal. (i.e. data collection methods, sampling, sample size and target group etc.) The data for this research is intended to be covered from Malaysia. Primary data will be collected from listed companies and Audit firms of Malaysia. Data will be collected with the help of questionnaires and interviews. Face-to-face structured interviews will be carried out. The interview will be kept short to respect the time constrains of the participants, hence would not be exceeding 30 minutes. Where clarification is required, further follow-up interviews can be carried out over telephone or e-mail where necessary. A total of 150 questionnaires would be prepared and distributed to the employees of listed companies of Malaysia and Certified Public Accountants (CPA) of Malaysia. The questionnaire would also be distributed to the shareholders for the listed companies of Malaysia and other employees who are working in financial institutions as well. The questionnaires would be more closed ended structured while there would be some open ended questions included as well. Most of the secondary data will be taken from online journals, Emerald, other websites and Athens database. G Brief description of the evaluation and analysis proposed for this project. (i.e. project deliverables and hypothesis, correlation test etc) Hypothesis of the Research This research would benefit the Audit firms, public and private limited companies and shareholders and stakeholders of the public companies. This research not only benefits to Audit firms, public and private companies, but for the public as a whole. The hypothesis testing would show the effect and the changes of audit quality. This hypothesis testing would be the relationship between internal auditors and the external auditors, being independent between the internal auditors would assist the external auditors to work independently and detect fraud and prevent them re-occurring. In addition this research would expect that there is a relationship between internal control and the audit quality and relationship between audit ethics and audit procedures that the external auditors carried out. Deliverables The end result of this research would provide empirical information to all auditors and the top management of the company. Especially this information would facilitate the directors to come out with reliable financial statement to its users. This research will provide theoretical information to all the directors and to the management of the company in order to prevent the financial statements from misstatement and fraud. This information helps the company from losing billions of dollars per year from corporate fraud and will safeguard the shareholders interest and the stake holders of the company. H. Illustration of how this project will benefit the future employability Relevance to Industry All the accounting and financial industries will benefit from this research. As an audit firm this research will help both enhancing the audit quality and will facilitate the new methods to detect fraud and will encourage new audit firms to perform the audit with due care. Auditing is one of the challenging works which assist the auditors to use their knowledge and experience and at the same time the new auditors will learn and gain experience in the audit field and would learn new methods on detecting frauds. This research would benefit all the corporations to minimize their corporate fraud cost. Significance to Modules This research is directly related to Audit and Assurance, Forensic Accounting. And also somehow this research is related to corporate governance as well.

Medical Data Analytics Using R

Medical Data Analytics Using R 1.) R for Recency => months since last donation, 2.) F for Frequency => total number of donation, 3.) M for Monetary => total amount of blood donated in c.c., 4.) T for Time => months since first donation and 5.) Binary variable => 1 -> donated blood, 0-> didnt donate blood. The main idea behind this dataset is the concept of relationship management CRM. Based on three metrics: Recency, Frequency and Monetary (RFM) which are 3 out of the 5 attributes of the dataset, we would be able to predict whether a customer is likely to donate blood again based to a marketing campaign. For example, customers who have donated or visited more currently (Recency), more frequently (Frequency) or made higher monetary values (Monetary) are more likely to respond to a marketing effort. Customers with less RFM score are less likely to react. It is also known in customer behavior, that the time of the first positive interaction (donation, purchase) is not significant. However, the Recency of the last donation is very important. In the traditional RFM implementation each customer is ranked based on his RFM value parameters against all the other customers and that develops a score for every customer. Customers with bigger scores are more likely to react in a positive way for example (visit again or donate). The model constructs the formula which could predict the following problem. Keep in repository only customers that are more likely to continue donating in the future and remove those who are less likely to donate, given a certain period of time. The previous statement also determines the problem which will be trained and tested in this project. Firstly, I created a .csv file and generated 748 unique random numbers in Excel in the domain [1,748] in the first column, which corresponds to the customers or users ID. Then I transferred the whole data from the .txt file (transfusion.data) to the .csv file in excel by using the delimited (,) option. Then I randomly split it in a train file and a test file. The train file contains the 530 instances and the test file has the 218 instances. Afterwards, I read both the training dataset and the test dataset. From the previous results, we can see that we have no missing or invalid values. Data ranges and units seem reasonable. Figure 1 above depicts boxplots of all the attributes and for both train and test datasets. By examining the figure, we notice that both datasets have similar distributions and there are some outliers (Monetary > 2,500) that are visible. The volume of blood variable has a high correlation with frequency. Because the volume of blood that is donated each time is fixed, the Monetary value is proportional to the Frequency (number of donations) each person gave. For example, if the amount of blood drawn in each person was 250 ml/bag (Taiwan Blood Services Foundation 2007) March then Monetary = 250*Frequency. This is also why in the predictive model we will not consider the Monetary attribute in the implementation. So, it is reasonable to expect that customers with higher frequency will have a lot higher Monetary value. This can be verified also visually by examining the Monetary outliers for the train set. We retrieve back 83 instances. In order, to understand better the statistical dispersion of the whole dataset (748 instances) we will look at the standard deviation (SD) between the Recency and the variable whether customer has donated blood (Binary variable) and the SD between the Frequency and the Binary variable.The distribution of scores around the mean is small, which means the data is concentrated. This can also be noticed from the plots. From this correlation matrix, we can verify what was stated above, that the frequency and the monetary values are proportional inputs, which can be noticed from their high correlation. Another observation is that the various Recency numbers are not factors of 3. This goes to opposition with what the description said about the data being collected every 3 months. Additionally, there is always a maximum number of times you can donate blood per certain period (e.g. 1 time per month), but the data shows that. 36 customers donated blood more than once and 6 customers had donated 3 or more times in the same month. The features that will be used to calculate the prediction of whether a customer is likely to donate again are 2, the Recency and the Frequency (RF). The Monetary feature will be dropped. The number of categories for R and F attributes will be 3. The highest RF score will be 33 equivalent to 6 when added together and the lowest will be 11 equivalent to 2 when added together. The threshold for the added score to determine whether a customer is more likely to donate blood again or not, will be set to 4 which is the median value. The users will be assigned to categories by sorting on RF attributes as well as their scores. The file with the donators will be sorted on Recency first (in ascending order) because we want to see which customers have donated blood more recently. Then it will be sorted on frequency (in descending order this time because we want to see which customers have donated more times) in each Recency category. Apart from sorting, we will need to apply some business rules that have occurred after multiple tests: For Recency (Business rule 1): If the Recency in months is less than 15 months, then these customers will be assigned to category 3. If the Recency in months is equal or greater than 15 months and less than 26 months, then these customers will be assigned to category 2. Otherwise, if the Recency in months is equal or greater than 26 months, then these customers will be assigned to category 1 And for Frequency (Business rule 2): If the Frequency is equal or greater than 25 times, then these customers will be assigned to category 3. If the Frequency is less than 25 times or greater than 15 months, then these customers will be assigned to category 2. If the Frequency is equal or less than 15 times, then these customers will be assigned to category 1 RESULTS The output of the program are two smaller files that have resulted from the train file and the other one from the test file, that have excluded several customers that should not be considered future targets and kept those that are likely to respond. Some statistics about the precision, recall and the balanced F-score of the train and test file have been calculated and printed. Furthermore, we compute the absolute difference between the results retrieved from the train and test file to get the offset error between these statistics. By doing this and verifying that the error numbers are negligible, we validate the consistency of the model implemented. Moreover, we depict two confusion matrices one for the test and one for the training by calculating the true positives, false negatives, false positives and true negatives. In our case, true positives correspond to the customers (who donated on March 2007) and were classified as future possible donators. False negatives correspond to the customers (who donated on March 2007) but were not classified as future possible targets for marketing campaigns. False positives correlate to customers (who did not donate on March 2007) and were incorrectly classified as possible future targets. Lastly, true negatives which are customers (who did not donate on March 2007) and were correctly classified as not plausible future donators and therefore removed from the data file. By classification we mean the application of the threshold (4) to separate those customers who are more likely and less likely to donate again in a certain future period. Lastly, we calculate 2 more single value metrics for both train and test files the Kappa Statistic (general statistic used for classification systems) and Matthews Correlation Coefficient or cost/reward measure. Both are normalized statistics for classification systems, its values never exceed 1, so the same statistic can be used even as the number of observations grows. The error for both measures are MCC error: 0.002577   and Kappa error:   0.002808, which is very small (negligible), similarly with all the previous measures. REFERENCES UCI Machine Learning Repository (2008) UCI machine learning repository: Blood transfusion service center data set. Available at: http://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center (Accessed: 30 January 2017). Fundation, T.B.S. (2015) Operation department. Available at: http://www.blood.org.tw/Internet/english/docDetail.aspx?uid=7741pid=7681docid=37144 (Accessed: 31 January 2017). The Appendix with the code starts below. However the whole code has been uploaded on my Git Hub profile and this is the link where it can be accessed. https://github.com/it21208/RassignmentDataAnalysis/blob/master/RassignmentDataAnalysis.R library(ggplot2) library(car)   # read training and testing datasets traindata à ¯Ã†â€™Ã… ¸Ãƒâ€šÃ‚   read.csv(C:/Users/Alexandros/Dropbox/MSc/2nd Semester/Data analysis/Assignment/transfusion.csv) testdata à ¯Ã†â€™Ã… ¸Ãƒâ€šÃ‚   read.csv(C:/Users/Alexandros/Dropbox/MSc/2nd Semester/Data analysis/Assignment/test.csv) # assigning the datasets to dataframes dftrain à ¯Ã†â€™Ã… ¸ data.frame(traindata) dftest à ¯Ã†â€™Ã… ¸ data.frame(testdata) sapply(dftrain, typeof) # give better names to columns names(dftrain)[1] à ¯Ã†â€™Ã… ¸ ID names(dftrain)[2] à ¯Ã†â€™Ã… ¸ recency names(dftrain)[3]à ¯Ã†â€™Ã… ¸frequency names(dftrain)[4]à ¯Ã†â€™Ã… ¸cc names(dftrain)[5]à ¯Ã†â€™Ã… ¸time names(dftrain)[6]à ¯Ã†â€™Ã… ¸donated # names(dftest)[1]à ¯Ã†â€™Ã… ¸ID names(dftest)[2]à ¯Ã†â€™Ã… ¸recency names(dftest)[3]à ¯Ã†â€™Ã… ¸frequency names(dftest)[4]à ¯Ã†â€™Ã… ¸cc names(dftest)[5]à ¯Ã†â€™Ã… ¸time names(dftest)[6]à ¯Ã†â€™Ã… ¸donated # drop time column from both files dftrain$time à ¯Ã†â€™Ã… ¸ NULL dftest$time à ¯Ã†â€™Ã… ¸ NULL #   sort (train) dataframe on Recency in ascending order sorted_dftrain à ¯Ã†â€™Ã… ¸ dftrain[ order( dftrain[,2] ), ] #   add column in (train) dataframe -   hold score (rank) of Recency for each customer sorted_dftrain[ , Rrank] à ¯Ã†â€™Ã… ¸ 0 #   convert train file from dataframe format to matrix matrix_train à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftrain, as.numeric)) #   sort (test) dataframe on Recency in ascending order sorted_dftest à ¯Ã†â€™Ã… ¸ dftest[ order( dftest[,2] ), ] #   add column in (test) dataframe -hold score (rank) of Recency for each customer sorted_dftest[ , Rrank] à ¯Ã†â€™Ã… ¸ 0 #   convert train file from dataframe format to matrix matrix_test à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftest, as.numeric)) # categorize matrix_train and add scores for Recency apply business rule for(i in 1:nrow(matrix_train)) { if (matrix_train [i,2]   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_train [i,6] à ¯Ã†â€™Ã… ¸ 3 } else if ((matrix_train [i,2] = 15)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_train [i,6] à ¯Ã†â€™Ã… ¸ 2 } else {   matrix_train [i,6] à ¯Ã†â€™Ã… ¸ 1   }   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   } # categorize matrix_test and add scores for Recency apply business rule for(i in 1:nrow(matrix_test)) { if (matrix_test [i,2]   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_test [i,6] à ¯Ã†â€™Ã… ¸ 3 } else if ((matrix_test [i,2] = 15)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_test [i,6] à ¯Ã†â€™Ã… ¸ 2 } else {   matrix_test [i,6] à ¯Ã†â€™Ã… ¸ 1 }   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   } # convert matrix_train back to dataframe sorted_dftrain à ¯Ã†â€™Ã… ¸ data.frame(matrix_train) # sort dataframe 1rst by Recency Rank (desc.) then by Frequency (desc.) sorted_dftrain_2à ¯Ã†â€™Ã… ¸ sorted_dftrain[order(-sorted_dftrain[,6], -sorted_dftrain[,3] ), ] # add column in train dataframe- hold Frequency score (rank) for each customer sorted_dftrain_2[ , Frank] à ¯Ã†â€™Ã… ¸ 0 # convert dataframe to matrix matrix_train à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftrain_2, as.numeric)) # convert matrix_test back to dataframe sorted_dftest à ¯Ã†â€™Ã… ¸ data.frame(matrix_test) # sort dataframe 1rst by Recency Rank (desc.) then by Frequency (desc.) sorted_dftest2 à ¯Ã†â€™Ã… ¸ sorted_dftest[ order( -sorted_dftest[,6], -sorted_dftest[,3] ), ] # add column in test dataframe- hold Frequency score (rank) for each customer sorted_dftest2[ , Frank] à ¯Ã†â€™Ã… ¸ 0 # convert dataframe to matrix matrix_test à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftest2, as.numeric)) #categorize matrix_train, add scores for Frequency for(i in 1:nrow(matrix_train)){    if (matrix_train[i,3] >= 25) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_train[i,7] à ¯Ã†â€™Ã… ¸ 3    } else if ((matrix_train[i,3] > 15) (matrix_train[i,3]   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_train[i,7] à ¯Ã†â€™Ã… ¸ 2    } else {   matrix_train[i,7] à ¯Ã†â€™Ã… ¸ 1   }   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   } #categorize matrix_test, add scores for Frequency for(i in 1:nrow(matrix_test)){    if (matrix_test[i,3] >= 25) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_test[i,7] à ¯Ã†â€™Ã… ¸ 3    } else if ((matrix_test[i,3] > 15) (matrix_test[i,3]   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   matrix_test[i,7] à ¯Ã†â€™Ã… ¸ 2    } else {  Ãƒâ€šÃ‚   matrix_test[i,7] à ¯Ã†â€™Ã… ¸ 1   } } #   convert matrix test back to dataframe sorted_dftrain à ¯Ã†â€™Ã… ¸ data.frame(matrix_train) # sort (train) dataframe 1rst on Recency rank (desc.) 2nd Frequency rank (desc.) sorted_dftrain_2 à ¯Ã†â€™Ã… ¸ sorted_dftrain[ order( -sorted_dftrain[,6], -sorted_dftrain[,7] ), ] # add another column for the Sum of Recency rank and Frequency rank sorted_dftrain_2[ , SumRankRAndF] à ¯Ã†â€™Ã… ¸ 0 # convert dataframe to matrix matrix_train à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftrain_2, as.numeric)) #   convert matrix test back to dataframe sorted_dftest à ¯Ã†â€™Ã… ¸ data.frame(matrix_test) # sort (train) dataframe 1rst on Recency rank (desc.) 2nd Frequency rank (desc.) sorted_dftest2 à ¯Ã†â€™Ã… ¸ sorted_dftest[ order( -sorted_dftest[,6],   -sorted_dftest[,7] ), ] # add another column for the Sum of Recency rank and Frequency rank sorted_dftest2[ , SumRankRAndF] à ¯Ã†â€™Ã… ¸ 0 # convert dataframe to matrix matrix_test à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftest2, as.numeric)) # sum Recency rank and Frequency rank for train file for(i in 1:nrow(matrix_train)) { matrix_train[i,8] à ¯Ã†â€™Ã… ¸ matrix_train[i,6] + matrix_train[i,7] } # sum Recency rank and Frequency rank for test file for(i in 1:nrow(matrix_test)) { matrix_test[i,8] à ¯Ã†â€™Ã… ¸ matrix_test[i,6] + matrix_test[i,7] } # convert matrix_train back to dataframe sorted_dftrain à ¯Ã†â€™Ã… ¸ data.frame(matrix_train) # sort train dataframe according to total rank in descending order sorted_dftrain_2 à ¯Ã†â€™Ã… ¸ sorted_dftrain[ order( -sorted_dftrain[,8] ), ] # convert sorted train dataframe matrix_train à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftrain_2, as.numeric)) # convert matrix_test back to dataframe sorted_dftest à ¯Ã†â€™Ã… ¸ data.frame(matrix_test) # sort test dataframe according to total rank in descending order sorted_dftest2 à ¯Ã†â€™Ã… ¸ sorted_dftest[ order( -sorted_dftest[,8] ), ] # convert sorted test dataframe to matrix matrix_test à ¯Ã†â€™Ã… ¸ as.matrix(sapply(sorted_dftest2, as.numeric)) # apply business rule check count customers whose score >= 4 and that Have Donated, train file # check count for all customers that have donated in the train dataset count_train_predicted_donations à ¯Ã†â€™Ã… ¸ 0 counter_train à ¯Ã†â€™Ã… ¸ 0 number_donation_instances_whole_train à ¯Ã†â€™Ã… ¸ 0 false_positives_train_counter à ¯Ã†â€™Ã… ¸ 0 for(i in 1:nrow(matrix_train)) {    if ((matrix_train[i,8] >= 4) (matrix_train[i,5] == 1)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   count_train_predicted_donations = count_train_predicted_donations + 1   } if ((matrix_train[i,8] >= 4) (matrix_train[i,5] == 0)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   false_positives_train_counter = false_positives_train_counter + 1}    if (matrix_train[i,8] >= 4) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   counter_train à ¯Ã†â€™Ã… ¸ counter_train + 1   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   }    if (matrix_train[i,5] == 1) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   number_donation_instances_whole_train à ¯Ã†â€™Ã… ¸ number_donation_instances_whole_train + 1    } } # apply business rule check count customers whose score >= 4 and that Have Donated, test file # check count for all customers that have donated in the test dataset count_test_predicted_donations à ¯Ã†â€™Ã… ¸ 0 counter_test à ¯Ã†â€™Ã… ¸ 0 number_donation_instances_whole_test à ¯Ã†â€™Ã… ¸ 0 false_positives_test_counter à ¯Ã†â€™Ã… ¸ 0 for(i in 1:nrow(matrix_test)) {    if ((matrix_test[i,8] >= 4) (matrix_test[i,5] == 1)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   count_test_predicted_donations = count_test_predicted_donations + 1   } if ((matrix_test[i,8] >= 4) (matrix_test[i,5] == 0)) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   false_positives_test_counter = false_positives_test_counter + 1}    if (matrix_test[i,8] >= 4) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   counter_test à ¯Ã†â€™Ã… ¸ counter_test + 1   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   }    if (matrix_test[i,5] == 1) {   Ãƒâ€šÃ‚  Ãƒâ€šÃ‚  Ãƒâ€šÃ‚   number_donation_instances_whole_test à ¯Ã†â€™Ã… ¸ number_donation_instances_whole_test + 1   Ãƒâ€šÃ‚   } } # convert matrix_train to dataframe dftrain à ¯Ã†â€™Ã… ¸ data.frame(matrix_train) # remove the group of customers who are less likely to donate again in the future from train file dftrain_final à ¯Ã†â€™Ã… ¸ dftrain[c(1:counter_train),1:8] # convert matrix_train to dataframe dftest à ¯Ã†â€™Ã… ¸ data.frame(matrix_test) # remove the group of customers who are less likely to donate again in the future from test file dftest_final à ¯Ã†â€™Ã… ¸ dftest[c(1:counter_test),1:8] # save final train dataframe as a CSV in the specified directory reduced target future customers write.csv(dftrain_final, file = C:\Users\Alexandros\Dropbox\MSc\2nd Semester\Data analysis\Assignment\train_output.csv, row.names = FALSE) #save final test dataframe as a CSV in the specified directory reduced target future customers write.csv(dftest_final, file = C:\Users\Alexandros\Dropbox\MSc\2nd Semester\Data analysis\Assignment\test_output.csv, row.names = FALSE) #train precision=number of relevant instances retrieved / number of retrieved instances collect.530 precision_train à ¯Ã†â€™Ã… ¸Ãƒâ€šÃ‚   count_train_predicted_donations / counter_train # train recall = number of relevant instances retrieved / number of relevant instances in collect.530 recall_train à ¯Ã†â€™Ã… ¸ count_train_predicted_donations / number_donation_instances_whole_train # measure combines PrecisionRecall is harmonic mean of PrecisionRecall balanced F-score for # train file f_balanced_score_train à ¯Ã†â€™Ã… ¸ 2*(precision_train*recall_train)/(precision_train+recall_train) # test precision precision_test à ¯Ã†â€™Ã… ¸ count_test_predicted_donations / counter_test # test recall recall_test à ¯Ã†â€™Ã… ¸ count_test_predicted_donations / number_donation_instances_whole_test # the balanced F-score for test file f_balanced_score_test à ¯Ã†â€™Ã… ¸ 2*(precision_test*recall_test)/(precision_test+recall_test) # error in precision error_precision à ¯Ã†â€™Ã… ¸ abs(precision_train-precision_test) # error in recall error_recall à ¯Ã†â€™Ã… ¸ abs(recall_train-recall_test) # error in f-balanced scores error_f_balanced_scores à ¯Ã†â€™Ã… ¸ abs(f_balanced_score_train-f_balanced_score_test) # Print Statistics for verification and validation cat(Precision with training dataset: , precision_train) cat(Recall with training dataset: , recall_train) cat(Precision with testing dataset: , precision_test) cat(Recall with testing dataset: , recall_test) cat(The F-balanced scores with training dataset: , f_balanced_score_train) cat(The F-balanced scores with testing dataset:   , f_balanced_score_test) cat(Error in precision: , error_precision) cat(Error in recall: , error_recall) cat(Error in F-balanced scores: , error_f_balanced_scores) # confusion matrix (true positives, false positives, false negatives, true negatives) # calculate true positives for train which is the variable count_train_predicted_donations # calculate false positives for train which is the variable false_positives_train_counter # calculate false negatives for train false_negatives_for_train à ¯Ã†â€™Ã… ¸ number_donation_instances_whole_train count_train_predicted_donations # calculate true negatives for train true_negatives_for_train à ¯Ã†â€™Ã… ¸ (nrow(matrix_train) number_donation_instances_whole_train) false_positives_train_counter collect_trainà ¯Ã†â€™Ã… ¸c(false_positives_train_counter, true_negatives_for_train, count_train_predicted_donations, false_negatives_for_train) # calculate true positives for test which is the variable count_test_predicted_donations # calculate false positives for test which is the variable false_positives_test_counter # calculate false negatives for test false_negatives_for_test à ¯Ã†â€™Ã… ¸ number_donation_instances_whole_test count_test_predicted_donations # calculate true negatives for test true_negatives_for_testà ¯Ã†â€™Ã… ¸(nrow(matrix_test)-number_donation_instances_whole_test)- false_positives_test_counter collect_test à ¯Ã†â€™Ã… ¸ c(false_positives_test_counter, true_negatives_for_test, count_test_predicted_donations, false_negatives_for_test) TrueCondition à ¯Ã†â€™Ã… ¸ factor(c(0, 0, 1, 1)) PredictedCondition à ¯Ã†â€™Ã… ¸ factor(c(1, 0, 1, 0)) # print confusion matrix for train df_conf_mat_train à ¯Ã†â€™Ã… ¸ data.frame(TrueCondition,PredictedCondition,collect_train) ggplot(data = df_conf_mat_train, mapping = aes(x = PredictedCondition, y = TrueCondition)) +    geom_tile(aes(fill = collect_train), colour = white) +    geom_text(aes(label = sprintf(%1.0f, collect_train)), vjust = 1) +    scale_fill_gradient(low = blue, high = red) +    theme_bw() + theme(legend.position = none) #   print confusion matrix for test df_conf_mat_test à ¯Ã†â€™Ã… ¸ data.frame(TrueCondition,PredictedCondition,collect_test) ggplot(data =   df_conf_mat_test, mapping = aes(x = PredictedCondition, y = TrueCondition)) +    geom_tile(aes(fill = collect_test), colour = white) +    geom_text(aes(label = sprintf(%1.0f, collect_test)), vjust = 1) +    scale_fill_gradient(low = blue, high = red) +    theme_bw() + theme(legend.position = none) # MCC = (TP * TN FP * FN)/sqrt((TP+FP) (TP+FN) (FP+TN) (TN+FN)) for train values mcc_train à ¯Ã†â€™Ã… ¸ ((count_train_predicted_donations * true_negatives_for_train) (false_positives_train_counter * false_negatives_for_train))/sqrt((count_train_predicted_donations+false_positives_train_counter)*(count_train_predicted_donations+false_negatives_for_train)*(false_positives_train_counter+true_negatives_for_train)*(true_negatives_for_train+false_negatives_for_train)) # print MCC for train cat(Matthews Correlation Coefficient for train: ,mcc_train) # MCC = (TP * TN FP * FN)/sqrt((TP+FP) (TP+FN) (FP+TN) (TN+FN)) for test values mcc_test à ¯Ã†â€™Ã… ¸ ((count_test_predicted_donations * true_negatives_for_test) (false_positives_test_counter * false_negatives_for_test))/sqrt((count_test_predicted_donations+false_positives_test_counter)*(count_test_predicted_donations+false_negatives_for_test)*(false_positives_test_counter+true_negatives_for_test)*(true_negatives_for_test+false_negatives_for_test)) # print MCC for test cat(Matthews Correlation Coefficient for test: ,mcc_test) # print MCC err between train and err cat(Matthews Correlation Coefficient error: ,abs(mcc_train-mcc_test)) # Total = TP + TN + FP + FN for train total_train à ¯Ã†â€™Ã… ¸ count_train_predicted_donations + true_negatives_for_train + false_positives_train_counter + false_negatives_for_train # Total = TP + TN + FP + FN for test   total_test à ¯Ã†â€™Ã… ¸ count_test_predicted_donations + true_negatives_for_test + false_positives_test_counter + false_negatives_for_test # totalAccuracy = (TP + TN) / Total for train values totalAccuracyTrain à ¯Ã†â€™Ã… ¸ (count_train_predicted_donations + true_negatives_for_train)/ total_train # totalAccuracy = (TP + TN) / Total for test values totalAccuracyTest à ¯Ã†â€™Ã… ¸ (count_test_predicted_donations + true_negatives_for_test)/ total_test # randomAccuracy = ((TN+FP)*(TN+FN)+(FN+TP)*(FP+TP)) / (Total*Total)   for train values randomAccuracyTrainà ¯Ã†â€™Ã… ¸((true_negatives_for_train+false_positives_train_counter)*(true_negatives_for_train+false_negatives_for_train)+(false_negatives_for_train+count_train_predicted_donations)*(false_positives_train_counter+count_train_predicted_donations))/(total_train*total_train) # randomAccuracy = ((TN+FP)*(TN+FN)+(FN+TP)*(FP+TP)) / (Total*Total)   for test values randomAccuracyTestà ¯Ã†â€™Ã… ¸((true_negatives_for_test+false_positives_test_counter)*(true_negatives_for_test+false_negatives_for_test)+(false_negatives_for_test+count_test_predicted_donations)*(false_positives_test_counter+count_test_predicted_donations))/(total_test*total_test) # kappa = (totalAccuracy randomAccuracy) / (1 randomAccuracy) for train kappa_train à ¯Ã†â€™Ã… ¸ (totalAccuracyTrain-randomAccuracyTrain)/(1-randomAccuracyTrain) # kappa = (totalAccuracy randomAccuracy) / (1 randomAccuracy) for test kappa_test à ¯Ã†â€™Ã… ¸ (totalAccuracyTest-randomAccuracyTest)/(1-randomAccuracyTest) # print kappa error cat(Kappa error: ,abs(kappa_train-kappa_test))

Langston Hughess poem Theme for English B :: English Literature

Langston Hughes's poem Theme for English B is a complicated piece, which is able to stimulate the mind of any person that gives it a chance. Langston Hughes's poem Theme for English B is a complicated piece, which is able to stimulate the mind of any person that gives it a chance. This poem touches on the ever present topics of racism, commonality and differences, with insights on how people are so frequent to prejudge others without even knowing their name. Racism is still present today, even most who would not consider themselves raciest have used some sort of raciest remark or gesture. Racism does not only affect a person with more or less melanin, but also those of a different creed or religion. Many people use everyday slang which contains some sort of derogatory meaning. With the ongoing effort of individuals as well as organizations most hope that our home, our planet, will progress into the all accepting society that is has the potential to become. This poem has made me contemplate all that our country is, and the right to a fair and equal life that we all deserve. With the giant steps we have taken over the past century, I know that we are on the right track. People must realize that we are all the same, except for the melanin concentration in our skin. In fact it has been recently proven through DNA science that a person of African decent in America is more closely related to a Caucasian in America then another African-American. I found this to be amazing and proof of just how close we all our. I am as well as everyone that I know, are guilty of making prejudgments on people without even knowing a thing about them. Whether it is the clothes one wears or the parlance they use, all of use have thought a certain way about a person just from that first impression. This is not a just an American characteristic, in fact in England one could be thought of as smarter and even better looking if

The Punishment Suits The Crime For Dante :: Dante Alighieri Inferno

The Punishment Suits the Crime   Ã‚  Ã‚  Ã‚  Ã‚  In the Inferno, Dante takes us on a journey through Hell. Dante describes the sins and the punishment in great detail. He puts the severity of the sins in a particular order, where the further one goes down, the more severe the sin. The order that Dante puts the sins in are: incontinence, violence, fraud, and betrayal. This paper will discuss two groups of sins, incontinence and fraud, and how severe the punishment for each sin is determined. In particular, it will compare the sin of gluttony in the third circle and divining in the fourth pouch of the eight circle.   Ã‚  Ã‚  Ã‚  Ã‚  The first group of sins are the incontinent sins. These are located in the second through fifth rings. These sins are primarily concerned with sins of the body. These sins also show a lack of restraint.   Ã‚  Ã‚  Ã‚  Ã‚  The sin of gluttony is found among the incontinent sins. Gluttony, by definition, is excess, such as food and drink, for example. God has given us all that we need on Earth, but that doesn't mean that we are supposed to have excessive gratification. When Dante and Virgil enter the third Circle where the gluttons are found, Dante acknowledges that it is "a realm of cold and heavy rain-a dark, accursed torrent eternally poured with changeless measure and nature" (Inferno, p. 45). The harsh and endless rain may be connected to the sin of gluttony. Since these sinners experienced excess on Earth, then they too are punished with an excess of rain in Hell. Dante also notices that "the soil they drench gives off a putrid odor" (Inferno, p. 45). The punishment of wallowing filth may also be connected to the sin of gluttony. Since they indulged in filth on Earth, then they shall wallow in filth for all eternity in Hell. The gluttons are also tortured by the three-headed dog Cerberus, the mythological guardian of Hell.   Ã‚  Ã‚  Ã‚  Ã‚  Dante meets a sinner named Ciacco while in this third circle. He says to Dante, "Your city, so full of envy that the sack spills over.

Al Capone Essay -- essays research papers

Al Capone Al Capone is America's best-known gangster and the single greatest symbol of the collapse of law and order in the United States during the 1920s Prohibition era. Capone had a leading role in the illegal activities that lent Chicago its reputation as a lawless city. Al Capone's mug shot, 1931. Capone was born on January 17, 1899, in Brooklyn, New York. Baptized "Alphonsus Capone," he grew up in a rough neighborhood and was a member of two "kid gangs," the Brooklyn Rippers and the Forty Thieves Juniors. Although he was bright, Capone quit school in the sixth grade at age fourteen. Between scams he was a clerk in a candy store, a pinboy in a bowling alley, and a cutter in a bookbindery. He became part of the notorious Five Points gang in Manhattan and worked in gangster Frankie Yale's Brooklyn dive, the Harvard Inn, as a bouncer and bartender. While working at the Inn, Capone received his infamous facial scars and the resulting nickname "Scar face" when he insulted a patron and was attacked by her brother. In 1918, Capone met an Irish girl named Mary "Mae" Coughlin at a dance. On December 4, 1918, Mae gave birth to their son, Albert "Sonny" Francis. Capone and Mae married that year on December 30. Al Capone Capone's first arrest was on a disorderly conduct charge while he was working for Yale. He also murdered two men while in New York, early testimony to his willingness to kill. In accordance with gangland etiquette, no one admitted to hearing or seeing a thing so Capone was never tried for the murders. After Capone hospitalized a rival gang member, Yale sent him to Chicago to wait until things cooled off. Capone arrived in Chicago in 1919 and moved his family into a house at 7244 South Prairie Avenue. The unpretentious Capone home at 7244 South Prairie Avenue, far from Chicago's Loop and Capone's business headquarters. Capone went to work for Yale's old mentor, John Torrid. Torrid saw Capone's potential, his combination of physical strength and intelligence, and encouraged his portаigаi. Soon Capone was helping Torrid manage his bootlegging business. By mid-1922 Capone ranked as Trio’s number two men and eventually became a full partner in the saloons, gambling houses, and brothels. Al Capone When Torrid was shot by rival gang members and consequently decided to leave... ... spent the rest of his felony sentence in the hospital. On January 6, 1939, his prison term expired and he was transferred to Terminal Island, a Federal Correctional Institution in California, to serve his one-year misdemeanor sentence. He was finally released on November 16, 1939, but still had to pay fines and court costs of $37,617.51. Capone at Comisky Park in 1931, before his conviction. After his release, Capone spent a short time in the hospital. He returned to his home in Palm Island where the rest of his life was relaxed and quiet. His mind and body continued to deteriorate so that he could no longer run the outfit. On January 21, 1947, he had an apoplectic stoke that was probably unrelated to his syphilis. He regained consciousness and began to improve until pneumonia set in on January 24. He died the next day from cardiac arrest. Capone was first buried in Mount Olivet Cemetery in Chicago's far South Side between the graves of his father, Gabriele, and brother, Frank, but in March of 1950 the remains of all three were moved to Mount Carmel Cemetery on the far West Side. Capone’s hitman Louis Bartollo (left) and close friend Phillip D’Agessi. Al Capone Essay -- essays research papers Al Capone Al Capone is America's best-known gangster and the single greatest symbol of the collapse of law and order in the United States during the 1920s Prohibition era. Capone had a leading role in the illegal activities that lent Chicago its reputation as a lawless city. Al Capone's mug shot, 1931. Capone was born on January 17, 1899, in Brooklyn, New York. Baptized "Alphonsus Capone," he grew up in a rough neighborhood and was a member of two "kid gangs," the Brooklyn Rippers and the Forty Thieves Juniors. Although he was bright, Capone quit school in the sixth grade at age fourteen. Between scams he was a clerk in a candy store, a pinboy in a bowling alley, and a cutter in a bookbindery. He became part of the notorious Five Points gang in Manhattan and worked in gangster Frankie Yale's Brooklyn dive, the Harvard Inn, as a bouncer and bartender. While working at the Inn, Capone received his infamous facial scars and the resulting nickname "Scar face" when he insulted a patron and was attacked by her brother. In 1918, Capone met an Irish girl named Mary "Mae" Coughlin at a dance. On December 4, 1918, Mae gave birth to their son, Albert "Sonny" Francis. Capone and Mae married that year on December 30. Al Capone Capone's first arrest was on a disorderly conduct charge while he was working for Yale. He also murdered two men while in New York, early testimony to his willingness to kill. In accordance with gangland etiquette, no one admitted to hearing or seeing a thing so Capone was never tried for the murders. After Capone hospitalized a rival gang member, Yale sent him to Chicago to wait until things cooled off. Capone arrived in Chicago in 1919 and moved his family into a house at 7244 South Prairie Avenue. The unpretentious Capone home at 7244 South Prairie Avenue, far from Chicago's Loop and Capone's business headquarters. Capone went to work for Yale's old mentor, John Torrid. Torrid saw Capone's potential, his combination of physical strength and intelligence, and encouraged his portаigаi. Soon Capone was helping Torrid manage his bootlegging business. By mid-1922 Capone ranked as Trio’s number two men and eventually became a full partner in the saloons, gambling houses, and brothels. Al Capone When Torrid was shot by rival gang members and consequently decided to leave... ... spent the rest of his felony sentence in the hospital. On January 6, 1939, his prison term expired and he was transferred to Terminal Island, a Federal Correctional Institution in California, to serve his one-year misdemeanor sentence. He was finally released on November 16, 1939, but still had to pay fines and court costs of $37,617.51. Capone at Comisky Park in 1931, before his conviction. After his release, Capone spent a short time in the hospital. He returned to his home in Palm Island where the rest of his life was relaxed and quiet. His mind and body continued to deteriorate so that he could no longer run the outfit. On January 21, 1947, he had an apoplectic stoke that was probably unrelated to his syphilis. He regained consciousness and began to improve until pneumonia set in on January 24. He died the next day from cardiac arrest. Capone was first buried in Mount Olivet Cemetery in Chicago's far South Side between the graves of his father, Gabriele, and brother, Frank, but in March of 1950 the remains of all three were moved to Mount Carmel Cemetery on the far West Side. Capone’s hitman Louis Bartollo (left) and close friend Phillip D’Agessi.

End the Wolf Hunt – Save the Wolves

Molly Kinney Composition 1 Mary Burmaster November 11 2012 Saving the Grey Wolves Wolves and humans have been coexisting for hundreds of years. Before Europeans conquered our vast country, wolves held a very esteemed place in Native American culture, as they were vital to forest ecosystems, and were often believed to be spiritual beings in many tribes (kidsplanet 1). As much as they were honored in tribal cultures, others feared them.Children’s fables often described them as â€Å"the big bad wolf† in stories such as Little Red Riding hood and The Three Little Pigs (kidsplanet 1). Settlers saw wolves in this way because they were a sort of competition, dwindling stock and wild game numbers (kidsplanet 1). Even into the 20th century, the belief that wolves were still a threat to human safety continued despite documentation to the contrary, and by the 1970s, the lower forty eight states had wolf populations less than three percent of their historical range, about 500 to 1 ,000 wolves (kidsplanet 1).In a book written by Bruce Hampton called The Great American Wolf, he states, â€Å"In the span of three hundred years nationwide, but only seventy years in the West, hunters in the United States had managed to kill off the wild prey of gray wolves; settlers, farmers, and ranchers had occupied most of the wolves' former habitat; wolfers had poisoned them; bounty hunters had dynamited their dens and pursued them with dogs, traps, and more poison; and finally, the government had stepped in and, primarily at the livestock industry's behest, quite literally finished them off.    Fortunately, around this time in the 70’s, American’s were starting to become much more aware of their impact on the environment and the wildlife. The Endangered Species Act was created in 1973, and the Grey Wolf was put on the list in 1974. After almost 35 years of restoration efforts and conservation work, the Grey Wolf has finally been taken off the endangered speci es list in Minnesota, with about 1,700 hundred wolves in the state (kidsplanet 1). Less than a year later, the Minnesota Department of Natural Resources (MN DNR) passed a law allowing a certain number of wolves to be hunted starting November 3, 2012 (kidsplanet 1).In the month and half the season has been open, about 150 Grey wolves have been killed (dnr. state. mn). Grey wolves are a vital part of our ecosystems and perhaps eventually grey wolves will once again thrive well enough that hunting them will not result in more conflict, but it is too soon to start the hunt again. Hunters should not be allowed to hunt grey wolves in Minnesota, because they have not had enough time to replenish their population and wolves are not a threat to human safety at all.Normally when an animal is taken off the endangered species list, it is given a five-year grace period to try and regain its spot back in the ecosystem before declaring a hunting season is even a thought in the minds of DNR decisio n makers (Horon 1). Since it took close to 40 years for the Grey wolf to be taken off the list, it seems logical to give the animal an even longer period to recover, to ensure that the animal does not get put on the list ever again.Though one hunt most likely will not kill off all the wolves, if hunting continues every year, there could be serious damage once again to the wolf population, as said in an article from a Wisconsin news website, madison. com. â€Å"One hunt won't put wolves†¦ back on the list but research hints at possible longer-term harm to the wolf population and even an increase in wolves killing livestock, researchers say† (Seely 1). However, the Minnesota DNR ruled that less than one year was a sufficient amount of time for the wolves to repopulate, and opened a wolf-hunting season on November 3rd, 2012.Before settlers came to North America, more than 250,000 wolves roamed the uncharted territory that is now the United States (Cosmos magazine). With eve ry year of citizen growth in the New World, Wolf population decreased. As the U. S. grew and became more populated, settlers practically made careers out of wolf hunting. In the 19th century, the pelts were in such high demand that almost everyone sought to kill as many wolves as possible (kidsplanet 1). People moving west bought hundreds of acres of land to raise their stock on, and killed every wolf that came near.Research from1974 showed that there were only about 500 Grey wolves living in the entire United States (kidsplanet 1). In efforts to re-grow the wolf population, conservationists took wolves into protection. Being protected by the Endangered Species Act has helped the Grey Wolf a lot. In the  Great Lakes, wolves have grown in population and expanded their range from Minnesota to Northern Michigan and Wisconsin (Meador 1). Although there have been huge gains in favor of the wolves, population recovery is far from over.Only 5,000 to 6,000 wolves occupy a mere five percen t of the animals’ historical range throughout Minnesota and the rest of the United States (Meador 1). Replenishing wolf populations through out the states would protect the future of wolves and allow them to play their important role in the forest environment in greater fulfillment of their former range. Yet another reason why wolves should not be hunted is because they pose no actual threat to humans or livestock. Wolves are able to kill animals much larger than humans and should be treated with respect.Contrary to the belief that wolves are vicious and aggressive towards humans, there have only been two reported deaths by wolf attack since 1900, one of which is heavily disputed (OregonWild 1). Moreover, wolves are opportunists, and sometimes eat livestock. However, they have a relatively small impact on the livestock industry as a whole (OregonWild 1). Unstable meat prices, disease, fuel and land prices, weather, dogs, and even human thieves pose larger threats to the marke t. (OregonWild 1). It is simply not true when people in favor of the wolf hunt say that wolves are detrimental to the industry.The United States livestock industry has been in a slow decline, preceding wolf recovery by many decades; However, a study done in an area of Oregon with a high wolf population showed that from 2009 to 2011, while the wolf population grew from 500 to 1400, revenue in the livestock industry jumped almost fifty percent to almost $27 million in a county with barely 7,000 citizens (OregonWild). Although wolves were not the cause of the huge increase, it is clear that their impact in the industry is small (OregonWild).Like shark attacks, when wolves wreak havoc, it can make for upsetting photos and grim stories, and so the risk of wolves to livestock is many times magnified (OregonWild 1). Research done in areas of high wolf populations has actually shown that having wolves around may actually decrease livestock loss by keeping smaller predators like coyotes in c heck (OregonWild 1). Of course, there are many people who believe that a Wolf hunt is completely acceptable under current circumstances. Many supporters believe that if professionals are not actively watching wolf population, it will increase much too rapidly (Robb 1).In an article from petersenhunting. com, Bob Robb, a hunting column writer, says, â€Å"This is especially true in areas where there are lots of animals for them to eat – like the Yellowstone ecosystem. Because wolf numbers exceeded targeted reintroduction population goals in the Yellowstone ecosystem more rapidly than expected, the animal was removed from the Endangered Species List and a sport hunting season on wolves was instituted in 2009† (Robb 1). Research does show that wolf packs not observed by researchers do reproduce more (petersenhunting 1).Minnesotans should not be allowed to hunt wolves because they have only been off the endangered species list for a year so their populations are not at the greatest numbers, and statistics from states where wolf hunting is illegal show that they may actually help the livestock industry and are not a threat at all. Very recently, researchers at Yellowstone National Park were saddened when the Alpha Female, called 832F by scientists and â€Å"Rockstar† by visitors, was found dead outside park boundaries on December sixth.Seven other wolves were found dead with her, all killed by hunters (EarthIslandJournal 1). After environmentalist’s work getting the Grey Wolf on the list finally paid off, they had hopes for the wolves to once again thrive someday in their natural habitat. This will never happen if we start diminishing wolf populations, right when they are at the height of restoration progress. Works Cited â€Å"DNR- What Happened behind Closed Doors? †Ã‚  Howling for Wolves Minnesota. N. p. , 13July 2012. Web. 13 Nov. 2012. ;http://www. owlingforwolves. org/news/dnr-what- happened-behind-closed-doors;. â€Å"COSM OS Magazine. †Ã‚  Grey Wolf Withdrawn from Endangered List. N. p. , 05 May 2010. Web. 13 Nov. 2012. . Greder, Andy. â€Å"Minnesota Wolf Hunt: About 150 Wolves Killed Statewide’’. â€Å"TwinCities. com. N. p. , 18 Nov. 2012. Web. 10 Dec. 2012. Horon, Sonia. â€Å"The Grey:? A Bad Fairy Tale About Wolves. †Ã‚  Globalanimal. com. Global Animal Website, 27 Jan. 2012. Web. 10 Dec. 012. Meador, Ron. â€Å"Save the Grey Wolf. †Ã‚  Causes. Minnpost, n. d. Web. 19 Sept. 2012. ;http://www. causes. com/causes/75833-save-the-grey-wolf;. Motsinger, John. â€Å"Wolf Weekly Wrap-up | Defenders of Wildlife Blog. †Ã‚  Wolf Weekly Wrap-up | Defenders of Wildlife Blog. N. p. , 7 Dec. 2012. Web. 10 Dec. 2012. â€Å"Oregon Wild. †Ã‚  Wolves-Misunderstood. N. p. , n. d. Web. 10 Dec. 2012. ;http://www. oregonwild. org/fish_wildlife/bringing_wolves_back/wolves- misunderstood; Robb, Bob. â€Å"Petersen's Hunting. †Ã‚  Petersens Hunting. N. p. 2 Nov. 2012. Web. 10 Dec. 2012. Seely, Matt. â€Å"Questions abound before Wisconsin’s Wolf Hunt†Ã‚  Madison. com. 14 Oct. 2012. Web. 10 Dec. 2012 William, Matt. â€Å"Yellowstone Popular Alpha Female Wolf Shot Dead by Hunters Outside Park. 10 Dec. 2012. Web. 10 Dec. 2012. â€Å"Wolf Management. †Ã‚  : Minnesota DNR. Minnesota DNR, Web. 13 Nov. 2012. ;http://www. dnr. state. mn. us/mammals/wolves/mgmt. html;.

Explain how the following link to your practice Essay

Bullying In my work place there is an anti bullying policy in place also all the children and staffs are educated on bullying and how it can affect people differently. The school also takes bullying very seriously and any accusations are dealt with straight away. Cyber bullying In my work place we have an cyber bullying policy also we educate the children and the staff on signs to watch out for and how to prevent it from happening also what to do if it does happen to you. The school also has posters in the ict room about bullying and where you can get help from. The school also send out leaflets so the parents can read them and educate themselves. Rewards At my work they are different rewards for different year groups and tasks. Some off the reward systems are ; star chart, this is mainly used on a 1-1 basis to help the child concentrate on the tasks set for them. Cloud and rainbow this is a whole class reward as it is not just focused on one child, if a child is behaving well they can go on the rainbow but if the child is not completing a task they will go on a cloud. There is also house points when a child is doing well they will get a point for their team and at the end of the week the points are added up throughout school and the team with the most points are moved along in the race. The team at the end of the year who wins the race will get a reward each. Sanctions There are different sanctions depending on the severity of the situation. One of the main sanctions is that if you are told more than once about something your name will go on the bored and if you still continue to misbehaving you will get a tick against your name, three ticks and you will spend playtime and dinner time in class doing extra work. Impact on personal factors This all depends on the situation for example; A child could be going through bereavement and they may act out e.g anger,  lashing out, withdrawn. If the school knows about an incident occurring they will take the child to one side and explain what has happened and how they might feel but that they can always talk to them if needed. Managing pupil behaviour They are different methods when dealing with pupil behaviour depending on the situation. If the child has additional needs they may be behaving differently due to confusion or anger. Two of the methods that are used most in my school are setting up a 1-1 support and also have a reward chart. Inclusion In my work place we try and treat everyone with respect and try and involve everyone no matter what. One of the ways we do this is buy changing the lesson plan so it can fit to the needs of the individual also if there is any other additional equipment needed such as a chair cushion it will be provided. Training Training is provided to all members off staff. If there is any additional training that is needed it will be provided such as first aid and food hygiene.