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A Comparative Analysis Of The Results Finance Essay - Free Essay Example

Sample details Pages: 21 Words: 6278 Downloads: 4 Date added: 2017/06/26 Category Finance Essay Type Analytical essay Did you like this example? Abstract This study is based on the original M3-Competition. (The M3 competition was a competition designed to examine the forecasting capabilities of several forecasting organisations). The project, which uses the M3 data, replicates the results obtained by the original researchers and confirms the calculations of their study in terms of a SMAPE (Symmetric Mean Percentage Error) analysis. Don’t waste time! Our writers will create an original "A Comparative Analysis Of The Results Finance Essay" essay for you Create order The data was also analysed using an alternative error analysis methodology (ROC Rate of Change) and conclusions drawn on the comparative analysis of the results. In conclusion this study has shown that the findings drawn in the original M3 study did differ from those obtained using the ROC methodology, although there was some general agreement in the context of complexity or otherwise of the forecasting methodologies employed. For example, the ROC methodology showed that one of the top performing methods was the Theta this in agreement with the SMAPE analysis which ranked it as the best overall performing method. Given also that the Theta method is considered as a simple forecasting approach this tends to confirm the conclusions drawn from the original study. As previously mentioned, this study also showed that there were differences in the overall rankings, using the two different methods of comparison, between the 24 different methods used in the original study. This study also showed that there are differences between the published results of the original study and those replicated in this study. Declaration I hereby declare: That except where reference has clearly been made to work by others, all the work presented in this report is my own work; that it has not previously been submitted for assessment; and that I have not knowingly allowed any of it to be copied by another student I understand that deceiving or attempting to deceive examiners by passing off the work of another as my own is plagiarism. I also understand that plagiarising the work of other of another or knowingly allowing another student to plagiarise from my work is against the University regulations and hat doing so will result in loss of marks and possible disciplinary proceeding against me. SignedÃÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡  ¬Ãƒâ€šÃ‚ ¦.. Data ÃÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦ÃƒÆ' ¢Ãƒ ¢Ã¢â‚¬Å¡Ã‚ ¬Ãƒâ€šÃ‚ ¦. Table of figures Table 1 Number of negative data in each forecasting method Table 2 SMAPE across the 18 forecasting horizons Table 3 SMAPE between 18 forecasting horizons boundaries Table 4 Comparative ranking of SMAPE between published results in the M3-Competition and those calculated in this study. Table 5 Table 5 ROC Error on Single across the 18 forecasting horizon Table 6 Ranking of ROC Results Table 7 ROC Result per observation Table 8 Comparative ranking between SMAPE and ROC Table A1 Comparative ranking between ROC and SMAPE Table A2 ROC Result Table A3 ROC of Single across 18 forecasting horizons Table A4 ROC of Winter across 18 forecasting horizons Graph 1 Comparative ranking of SMAPE between published results in the M3-Competition and those calculated in this study Graph 2 Matching difference between published results in the M3-Competition and those calculated in this study Graph 3 Z/O Z/A chart, by John (2004) Graph 4 Comparative ranking be tween SMAPE and ROC Graph A1 Matching difference of Rank between ROC and SMAPE Graph A2 Single ROC on the 11th forecasting horizon Graph A3 Winter ROC on the 9th forecasting horizon Section Content Page 1.0 Introduction 1 2.0 Study of Forecasting Competition 3 2.1 Previous Study 3 2.2 M-Competition 4 2.3 M2-Competition 6 2.4 M3-Competition 7 3.0 Source data 9 3.1 Data format 9 3.2 Actual data 10 3.3 Forecasted data 13 3.4 Data source error 18 4.0 SMAPE Concept and Calculation 19 4.1 Definition 19 4.2 Calculation 20 4.3 Results 21 4.4 Matching M3-Competition data 23 5.0 ROC (Rate of change) Concept and Calculation 26 5.1 Definition 26 5.2 Results 29 5.3 Ranking ROC Result 30 6.0 Comparative analysis between MAPE and ROC 32 7.0 Discussion and Conclusion 36 Reference 39 Appendix Comparative different between SMAPE and ROC Result i ROC Result iii Single Result iv Winter Result vi Summary of the result from the 24 forecasting methods viii File list The lists of all the files include were listed on File list.txt, which more information and format of the files would be explained. 1.0 Introduction Prediction has become very important in many organisations since decision-making process rely mostly on prediction of future event. From the important of these forecasts, many forecasting methods have been applied and used. Furthermore, measurement errors had been implied to forecasting methods to determine their performance. In this study, M3-Competition is to be re-analysed and also investigate with ROC (Rate of Change) methodology. M3-Competition was published in 2000, from the researchers at INSTEAD, Paris. Aims The project had explored and been investigated the conclusions and subsequent commentaries from the original M3-Competition and then undertake an analysis, based on the Rate of Change methodology, on the original data sets and draw comparison of the results. Objectives The study would be involved in the tasks as followed Study the work of all M-Competitions and also related previous work Replicate the result of SMAPE (Symmetric Mean Average Percentage Error) introduced in M3-Competition. To undertake the ROC (Rate of Change Methodology) on the M3 data and produce consequent error Compare the variance measurement errors between SMAPE and ROC Conclude on error measurement goodness 2.0 Study of Forecasting Competition 2.1 Previous Studies Early studies on forecasting accuracy, in the context of this report, were started in 1969. At that time, the studies were only based on limited number of methods. In 1979, Makridakis and Hibon expanded the range and scope of such studies. The study compared 111 time series drawn from real-life situations such as business, industry and macro data. Theils U-Coefficient and MAPE (Mean Average Percentage Error) were used as the measures of accuracy. The major conclusion from these studies was that simple methods such as the smoothing method out performed the more sophisticated ones, as reported in M3-Competition by Makridakis et al. (2000, p. 452). However these conclusions conflicted with the accepted views at that time. 2.2 M-Competition Despite the critics, Makridakis continued his argument by introducing M-Competition (1982). This time the number of series was increased by 1001 and also the number of methods increased to 15. In addition different trials of the same method were also tested. Minor changes were made to the general structure of the competition such as the type of series, which changed to macro, micro, industry, demographic. The observations were arranged as 18 for monthly, 8 for quarterly and 6 for yearly. Also additional measurement errors were added, these were Mean Square Errors, Average Rankings and Median of Absolute Percentage errors. From the results, the four conclusions drawn by Makridakis et al. (1982, 2000, p. 452) were: 1. It was not true that statistically sophisticated or more complex methods, out performed simpler methods. 2. The relative ranking of the various methods varied according to the method of accuracy measurement used. 3. The forecast accuracy, when various indivi dual methods are combined, outperforms the individual methods which were the constituent parts of the combined method and the combined methods on the whole did very well in comparison to other methods. 4. The accuracy of each of the various methods depends upon the length of the forecasting horizon involved. At the conclusion of the study, the results were made available to other researchers for the purposes of verification and replication. This showed that:- 1. The calculations contained in the study were verified and found to be correct. 2. The results were also confirmed when other researchers, using the same data sets, employed different methods of measuring the developed results. 3. Other researchers, using different data series, also reinforced, in their results, the validity of such studies. Throughout this period it was still too soon to state that statistically sophisticated methods did not do better than simple methods when there was considerable randomne ss in the data. It was also shown that simple and sophisticated methods could be equally effective when applied to series which exhibited seasonal patterns. 2.3 M2-Competition In 1993, a further attempt was made to measure and develop the accuracy of various forecasting methods in the M-2 Competition (1993). This was constructed on a real time basis with a further five forecasting organisations (the data was provided by four companies and included six economic series). In this more recent study other forecasting methods, such as NaÃÆ'ƒÂ ¯ve 2, single smoothing and Dampen were included. The accuracy measure employed was based on MAPE (mean absolute percentage error). The four companies provided the experts with the actual data of past and present situations (information on the nature and prevailing business conditions was also provided to the experts). Then the participating experts had to provide forecasts for the next 15 months. After a year, the forecasting data was checked against the actual data from the companies. However the conclusions from this study were identical to those drawn from the M-Competition, in that the more sophisticated m ethods did not create more accurate forecasts than the simpler ones. The study also agreed that the conclusions drawn from previous studies were confirmed. 2.4 M3-Competition The M3-Competition (2000), involved more methods, researchers and more time series. The number of time series was extended to 3003. To reduce the demands on data storage it was decided that a minimal number of observations for each type of data would be used: 14 observations for yearly series 16 observations for quarterly series 48 observations for monthly series 60 observations for other Given the source data, the participating experts were asked to develop further forecasts as follows: 6 for yearly 8 for quarterly 18 for monthly 8 for others The given time series data did not include any data containing negative values. Thus it was expected that the submitted forecasted data should also not include negative data. Despite this requirement, it was decided that any negative values received in the forecasted data, would be set to zero. Also seven methods were also added to the submitted data received from those who used neural networks, expert systems an d decomposition to produce their forecasts. Five accuracy measures were used to analyze the data as follows: Symmetric MAPE Average Ranking Median symmetric APE Percentage Better Median RAE From the analysis of the M3- Competition, the conclusions were identical to the previous M-Competitions. However it was recognised that Theata, a new method used in the M3 competition, had out performed all other methods, and performed consistently well across both forecasting horizons and accuracy measures, suggested by Makridakis et al. (2000, p.459) 3.0 Source Data 3.1 Data format The original data came from the M3- Competition which has been provided by INSTEAD. The data was broken down into two parts, which were actual data and forecast data. The actual data was taken from the website of international institute of forecasters (M3-Competition data). This data was given as an xls. file or in an Excel spreadsheet format. Meanwhile the data was broken down into 5 parts, which were titles as Competition, M3 Year, M3 Quart, M3 Month and M3 Other. However the forecast data was provided by Michele Hibon, who was one of the authors of the M3-Competition: results, conclusions and implication. The datas format was in a DAT. file, this meant that the data was needed to be converted into xls. format in order for the forecast data to be compatible with the actual data. 3.2 Actual data As mention previously actual data was provide as an xls. file. This meant that the data could be used into calculation straight away. However the forecast data only provide the last 6 data in yearly, 8 data in quarterly, 18 data in monthly and 8 data in other. Therefore the last of each type of data would only be used. In order to copy the unsynchronised last data repeatedly, Macro tool was utilised. The code (Macro code) was used to move all the data to right hand side of the spread sheet. This meant that the following last data in each category could be easily copy. Macro code For rearrange the actual data of the M3-Competition Yearly data Sub () aa = Range(A1:BA646) For i = 1 To 646 t = 53 For h = 53 To 1 Step -1 rr = aa(i, h) aa(i, h) = Empty If rr Empty Then aa(i, t) = rr t = t 1 End If Next h Next i Range(A1:BA646) = aa End Sub Quarterly data Sub () aa = Range(A1:BZ757) For i = 1 To 757 t = 78 For h = 7 8 To 1 Step -1 rr = aa(i, h) aa(i, h) = Empty If rr Empty Then aa(i, t) = rr t = t 1 End If Next h Next i Range(A1:BZ757) = aa End Sub Monthly data Sub () aa = Range(A1:ET1429) For i = 1 To 1429 t = 150 For h = 150 To 1 Step -1 rr = aa(i, h) aa(i, h) = Empty If rr Empty Then aa(i, t) = rr t = t 1 End If Next h Next i Range(A1:ET1429) = aa End Sub Other data Sub () aa = Range(A1:DF175) For i = 1 To 175 t = 110 For h = 110 To 1 Step -1 rr = aa(i, h) aa(i, h) = Empty If rr Empty Then aa(i, t) = rr t = t 1 End If Next h Next i Range(A1:DF175) = aa End Sub 3.3 Forecasted data Forecasted data consisted of 24 forecasters which were all provided in DAT. file. The data was then converted into xls. file by opening the file through Excel. Also margins were added to separate each of the value to according cells. However the imported data still has data which overlay each other and did not match the format of the actual data. Therefore Macro was used to rearrange data to working format. The data was first rearrange to remove the overlay on each observations by demonstrate an example on macro. Then this was done repeated to set condition on macro. Meanwhile the cells which used to have the overlay values were still present. Therefore another macro was used made to remove all the empty cells. Meanwhile with AAM1 and AAM2 data, condition on macro needed to be changed as only 2184 observations were provided. At last in order for the data to be compatible with the actual data, heading for each observation was then remove by another written macro. Macro code: R earrange the overlay values Sub () For i = 2804 To 8514 Range(A i + 1 :H i + 1).Select Selection.Cut Range(I i).Select ActiveSheet.Paste Range(A i + 2 :H i + 2).Select Selection.Cut ActiveWindow.ScrollColumn = 2 ActiveWindow.ScrollColumn = 3 ActiveWindow.ScrollColumn = 4 ActiveWindow.ScrollColumn = 5 ActiveWindow.ScrollColumn = 6 Range(Q i).Select ActiveSheet.Paste ActiveWindow.ScrollColumn = 5 ActiveWindow.ScrollColumn = 4 ActiveWindow.ScrollColumn = 3 ActiveWindow.ScrollColumn = 2 ActiveWindow.ScrollColumn = 1 i = i + 3 Next i End Sub Remove all the empty cells Sub () j = 5659 For i = 2805 To j Rows(2819:2820).Select Rows(i : i + 1).Select Selection.Delete Shift:=xlUp i = i + 1 j = j 4 Next i End Sub Remove all the heading Sub () j = 3003 For i = 1 To j Rows(2819:2820).Select Rows(i : i).Select Selection.Delete Shift:=xlUp Next i End Sub Macro code (AAM1 and AAM2): Rearrange the overlay value Sub () For i = 1514 To 7224 Range(A i + 1 :H i + 1).Select Selection.Cut Range(I i).Select ActiveSheet.Paste Range(A i + 2 :H i + 2).Select Selection.Cut ActiveWindow.ScrollColumn = 2 ActiveWindow.ScrollColumn = 3 ActiveWindow.ScrollColumn = 4 ActiveWindow.ScrollColumn = 5 ActiveWindow.ScrollColumn = 6 Range(Q i).Select ActiveSheet.Paste ActiveWindow.ScrollColumn = 5 ActiveWindow.ScrollColumn = 4 ActiveWindow.ScrollColumn = 3 ActiveWindow.ScrollColumn = 2 ActiveWindow.ScrollColumn = 1 i = i + 3 Next i End Sub Delete the empty cells Sub () j = 7224 For i = 1515 To j Rows(2819:2820).Select Rows(i : i + 1).Select Selection.Delete Shift:=xlUp i = i + 1 j = j 4 Next i End Sub 3.4 Data source error By dealing with a large data set, errors could have been occurred through out data transfer from the original and tested data. In the process of prepare forecasted data, five forecasts had been found to obtain negative forecasted results. The forecasts are as followed; Robust-Trend Automat ANN Theata ARARMA SmartFcs Also some forecasts had more negative values than other. Robust-Trend was found to have the most number of negative values present in the data, with 151 negative values. The second was Automat ANN, which as the list followed. This meant that the least would be SmartFcs with one negative values presented. Therefore negative values in the five forecast were then replace by positive sign. This was the case as of the reason that the result, which obtained was nearer to result published in M3-Compettiton (2000). Method Number of data Robust-Trend 151 Automat ANN 47 Theata 19 ARARMA 4 SmartFcs 1 Table 1 Number of neg ative data in each forecasting method 4.0 SMAPE (Symmetric Mean Average Percentage Error) Concept and Calculation 4.1 Definition Symmetrical Mean Average Percentage Error (SMAPE) or Adjusted Mean Average Percentage Error, Armstrong (1985) A could be defined as: SMAPE = (1.1) Note: X- Actual value, F Forecasted value, which the sum of the total divided by number of observations Despite the similarity to MAPE (Mean Average Percentage Error), SMAPE had an advantage to Mean Average Percentage Error as this would eliminate the favour for low estimates and also there were no limits to high side, mentioned by Armstrongs Long-Range Forecasting book (1985). Meanwhile the limit of SMAPE was between 0%, which meant for perfect and 200% for infinitely bad forecast. This meant that SMAPE was to be less sensitive than MAPE to measurement errors in actual data, stated by Armstrong (1985, p. 348). However SMAPE was not totally symmetric as over-forecasts and under-forecasts were not treated equally. 4.2 Calculation In each forecast, SMAPE had been done individually according to forecasting horizon. At first, Average Percentage Mean (APE) was calculated according to each observation for 3003 observations and 2184 observations (AAM1 and AAM2) as followed: The calculation of Error, which could be defined as Error = Actual Forecast Take the Absolute Error, | Error | Calculate the sum of Actual and Forecast Divided the sum of Actual and Forecast by 2 Calculate the APE by taken the value in step 2 divided by value in step 4. In order to produced, SMAPE, the sum of value of APE in each observation were divided by the number of observations which it had been considered. Then, the SMAPE was calculated according to boundary forecasting horizon, such as 1 to 4, 1 to 6, 1 to 8, 1 to 12, 1 to 15, and 1 to 18. 4.3 Results From the SMAPE, the forecasts were ranked from best with the least error to worst with the highest. These were ranked according to the result from boundary forecasting horizon 1 to 18. This was the case as the selected error would combine all the errors in 18 forecasting horizons. This was shown in Table 2 and 3: Example result from Theata method Forecasting horizon SMAPE N 1 0.084017 3003 2 0.095669 3003 3 0.113103 3003 4 0.125112 3003 5 0.131298 3003 6 0.139994 3003 7 0.122699 2358 8 0.119834 2358 9 0.131595 1428 10 0.133898 1428 11 0.1347 1428 12 0.132214 1428 13 0.154032 1428 14 0.151862 1428 15 0.162854 1428 16 0.177043 1428 17 0.168029 1428 18 0.182731 1428 Table 2 SMAPE across the 18 forecasting horizons Forecasting Horizon 1 to 4 1 to 6 1 to 8 1 to 12 1 to 15 1 to 18 Total Per centage Error 1254.958 2069.647 2641.54 3401.817 4071.19 4824.892 N 12012 18018 22734 28446 32730 37014 SMAPE 0.104475 0.114866 0.116193 0.119589 0.124387 0.130353 Table 3 SMAPE between 18 forecasting horizons boundaries 4.4 Matching M3-Competition data In order to replicate the result from the M3-Competition, the same SMAPE, which was mention previously, was used as the measurement error on the M3 data. The obtained SMAPE result was then compare to the result published in M3-Competition, which was shown in the table 4. Rank SMAPE SMAPE(M3) 1 Theata Theata 2 Forecast X Forecast Pro 3 Forecast Pro Forecast X 4 Comb S-H-D Comb S-H-D 5 Dampen Dampen 6 RBF RBF 7 B-J automatic Theata-sm 8 Automat ANN B-J automatic 9 SmartFcs PP-autocast 10 PP-autocast Automat ANN 11 Flores-Pearce2 SmartFcs 12 Single Flores-Pearce2 13 Theata-sm Single 14 Autobox2 Autobox2 15 AAM1 Holt 16 Flores-Pearce1 AAM2 17 ARARMA Winter 18 AAM2 Flores-Pearce1 19 Holt ARARMA 20 Winter AAM1 21 Autobox1 Autobox1 22 NaÃÆ'ƒÂ ¯ve2 Autobox3 23 Autobox3 NaÃÆ'ƒÂ ¯ve2 24 Robust-Trend Robust-Trend Table 4 Comparative ranking of SMAPE between published results in the M3-Competition and those calculated in this study From the comparison, the ranking of the forecasting methods were not the same as it was expected. From the result, some methods seem to out perform better than it was expected. For example, AAM1 had moved up by 5 ranks. Also NaÃÆ'ƒÂ ¯ve2 had out performed Autobox3. Meanwhile, some methods did not perform as well, for example Theata-sm was decreased by 6 ranks. Graph 1 Comparative ranking of SMAPE between published results in the M3-Competition and those calculated in this study Graph 1 Comparative Ranking between M3-Competition and calculated result Graph 2 Matching difference between published results in the M3-Competition and those calculated in this study As mention earlier, some errors had been found in the raw data. These were the negative data in the 5 forecasting me thods. However, as all the negative values was replaced with positive values in these forecasting methods. There were two forecasting methods which produced the corresponded ranking to the original M3 SMAPE analysis. These methods were Robust-trend and Theata. But the rest which were AutomatANN, SmatFcs, and ARARMA, had mis-matched the original SMAPE by 2 ranks. Despite, the argument above, it was clear that there were other forecasting methods which had perfectly good data, produced mis-match result. As the size of the data, it was possible that errors could have occurred in various stages in the calculation, even though this had been treated with caution. For example, rounding errors could occur when the 3003 observations were used to calculate the total SMAPE in each forecasting horizon. This meant that by considered the more number of observations, the likelihood of the errors in rounding would be more noticeable. Also it was to be mention that the forecasted data was not directly obtained from the M3-Competion, as the data was not published in the paper of M3-Competition (2000). Therefore it was fair to that the forecasted data could not have been the identical one which was used in the M3-Competition. However, this data was provided by Michele Hibon. Therefore, despite the difference in result, both results produced the same conclusions and also the data was obtained from a reliable source. This was also proven by the small deviation in the plot of both results, as Root Mean Square value was 0.9012. 5.0 ROC (Rate of Change) Concept and Calculation 5.1 Definition The Rate of change method is based on the Centred Forecast-Observation diagram for change developed by Theil (1958) and subsequently reported by Gilchrist (1976) and extended by John (2004, p.1000). In 1968, the diagram of actual and predicted changes was a graphical picture of turning point error, mentioned by Theil (1958, p. 29). This was represented on the horizontal axis as actual change, vertical axis as predicted change (observation change), and with a line of perfect forecast which was 45 ° to the origin. The diagram is divided into four quadrants, the second and fourth quadrants represent turning point errors. These are determined by the sign of the preceding actual change in the same variable. Meanwhile, the other two quadrants were divided by the line of perfect forecast into equal areas of overestimation and underestimation of changes. Centred Forecast-observation diagram, Gilchrist (1976 p. 223) was used to explain more about the characteristics of forecasting. The diagram is split into six quadrants. This was also mentioned by John (2004, p. 1001). John (2004) uses the diagram as a chart with the forecast series on the y-axis and actual series on the x-axis. For each actual series a pair would be determined as: Z/Ai = Ai + 1 Ai (2.1) Then for each forecast series the pair would be: Z/Oi = Ôi + 1 Ôi (2.2) Note: A pair of actual values, Ô pair of forecast values Then each of the individual pairs of Z/Ai and Z/Oi could be determined and plotted. As mentioned previously the chart was divided into six quadrants, the quadrants start in a clockwise direction from the forecast pair axis in positive. The quadrants are as follows: Sector 1 Overestimate of positive change Sector 2 Underestimate of positive change Sector 3 Forecast decrease when an increase in the actual occurs Sector 4 Overestimate of negative change Sector 5 Underestimate of positive change Sector 6 Forecast increase when a dec rease in the actual occurs Graph 3 Z/O Z/A chart, by John (2004) In each quadrant the number of errors could then be determined. However, the magnitude of the errors in each quadrant was not as equal as each other. The error was then divided into two distinct types, normal error and quadrant error, as recognised by John (2004). Normal error was given to the pair which had the same direction (sign of change), and measured as: Normal error = (|Z/Ai| | Z/Oi|)  ² (2.3) When the direction (sign of change) of Z/Ai and Z/Oi was different, for example Z/Ai was positive and Z/Oi was negative. This was known as the quadrant error, and measured as: Quadrant error = (3|Z/Ai| + | Z/Oi|)  ² (2.4) This was devised by John (2004), since it could be argued that even if a forecast failed to recognize the correct magnitude of change, it would be expected that it should at least recognise the direction of change. 5.2 Result In each forecasting horizon, all the Z/Ai and Z/Oi were calculated and allocated to correct position on the chart. Then the magnitude of errors in each quadrant was calculated. Then total normal error, total quadrant error, and total error for individual forecasting horizon were calculated, as shown in Single method in table 5. Therefore by adding all the total errors from each forecasting horizons, the total magnitude of error could be determined for each forecast methods. Results from the Single method Forecasting horizon Normal Error Quadrant Error Total Error 1 1,669,235,393 4,762,819,858 6,432,055,251 2 3,090,915,225 7,819,879,985 10,910,795,211 3 3,809,937,394 10,672,690,256 14,482,627,650 4 4,684,287,634 18,397,987,871 23,082,275,504 5 4,736,994,108 16,554,461,103 21,291,455,210 6 4,360,306,528 20,049,475,146 24,409,781,674 7 2,373,819,526 10,918,011,198 13,291,830,724 8 2,532,396,960 11,776,071,077 14,308,468,037 9 1,792,817,169 4,172,721,813 5,965,538,981 10 1,801,230,076 4,202,413,276 6,003,643,352 11 1,378,997,041 3,858,393,264 5,237,390,306 12 2,121,625,068 4,529,366,741 6,650,991,810 13 1,386,801,507 5,628,793,890 7,015,595,396 14 1,798,343,821 7,168,856,951 8,967,200,772 15 2,150,461,735 8,319,292,944 10,469,754,679 16 2,980,716,589 4,849,696,617 7,830,413,206 17 2,754,027,689 6,003,654,290 8,757,681,979 18 2,359,952,669 6,596,002,482 8,955,955,151 Table 5 ROC Error on Single across the 18 forecasting horizon 5.3 Rank ROC Result In order to rank the forecasting methods, any bias, caused by the different number of observations in each method, could be eliminated by implied Error per observation. The results from the 24 forecasting methods were as followed: Methods Error per observation Rank Single 67,953,198 1 Theata 72,562,195 2 Comb S-H-D 72,944,577 3 Forecast X 76,111,849 4 Flores-Pearce2 76,589,909 5 SmartFcs 79,120,086 6 Theata-sm 81,199,075 7 Dampen 83,862,347 8 Forecast Pro 83,931,783 9 AAM1 86,621,738 10 NaÃÆ'ƒÂ ¯ve2 87,290,813 11 AAM2 88,513,888 12 B-J automatic 89,875,446 13 RBF 89,950,117 14 Automat ANN 92,188,194 15 PP-autocast 94,661,129 16 Holt 94,735,528 17 Autobox3 102,203,567 18 Flores-Pearce1 115,985,051 19 Autobox1 120,046,101 20 Robust-Trend 122,657,044 21 Autobox2 134,148,029 22 ARARMA 237,620,699 23 Winter 6,602,739,469 24 Table 6 Ranking of ROC Results This was also applied to the other calculated errors such as normal errors and, quadrant errors. In addition, the numbers of observed data points, which were over-estimates, under-estimates, quadrants and correct, have been normalised and listed. The list of the forecasting methods performance is shown in table 7. Method Correct Over-estimates Under-estimates Quadrants Normal Errors Quadrant Errors Total Errors Best NaÃÆ'ƒÂ ¯ve2 Single Robust-Trend Theata Single Comb S-H-D Single AAM1 Automat ANN Autobox3 Dampen Automat ANN Theata Theata Single NaÃÆ'ƒÂ ¯ve2 Autobox1 Comb S-H-D NaÃÆ'ƒÂ ¯ve2 Single Comb S-H-D Forecast X B-J automatic Holt Forecast Pro Forecast X Flores-Pearce2 Forecast X Flores-Pearce1 Theata-sm ARARMA Single Theata-sm Forecast Pro Flores-Pearce2 SmartFcs Theata Winter Forecast X AAM1 Dampen SmartFcs AAM2 Autobox2 RBF PP-autocast AAM2 Forecast X Theata-sm B-J automatic Forecast X SmartFcs B-J automatic SmartFcs SmartFcs Dampen Flores-Pearce2 Dampen Flores-Pearce2 Flores-Pearce1 Flores-Pearce2 B-J automatic Forecast Pro Forecast Pro Flores-Pearce2 Flores-Pearce1 RBF Theata Theata-sm AAM1 ARARMA PP-autocast Autobox2 Winter Comb S-H-D Holt NaÃÆ'ƒÂ ¯ve2 Theata Flores-Pearce1 Forecast Pro ARARMA RBF RBF AAM2 Autobox2 Comb S-H-D PP-autocast NaÃÆ'ƒÂ ¯ve2 Dampen Autobox2 B-J automatic Comb S-H-D Forecast Pro Comb S-H-D Holt Forecast Pro PP-autocast RBF Autobox1 SmartFcs Forecast X Theata-sm PP-autocast AAM1 Automat ANN Winter RBF Theata-sm SmartF cs Autobox3 AAM2 PP-autocast Autobox3 Autobox1 NaÃÆ'ƒÂ ¯ve2 Flores-Pearce2 B-J automatic NaÃÆ'ƒÂ ¯ve2 Holt Automat ANN Winter Automat ANN Autobox2 Holt Autobox1 Autobox3 Theata-sm ARARMA Dampen Autobox3 Flores-Pearce1 Autobox3 Flores-Pearce1 Dampen Holt B-J automatic Automat ANN Robust-Trend Automat ANN Autobox1 PP-autocast Autobox3 Theata Robust-Trend Autobox1 ARARMA Robust-Trend RBF Robust-Trend Single Autobox1 Autobox2 Robust-Trend Autobox2 Holt AAM1 AAM2 AAM1 ARARMA Flores-Pearce1 ARARMA Worst Robust-Trend AAM2 AAM1 AAM2 Winter Winter Winter Table 7 ROC Result per observation 6.0 Comparative analysis between SMAPE and ROC From the analysis, it could be argued that conclusions form the M-Competition could still be valid, since it had been proved that sophisticated or complex methods did not out perform the simpler ones. This became clear as Single out performed all of the selected methods. Single was based on single exponential smoothing, considered to be a simple method. Also the explicit methods, such as Robust-Trend and Winter came last in the competition. Also it was proven that different accuracy measures would produce a different relative ranking of the various methods. By taking a comparative analysis of the two measurement error methods, some forecasting methods did perform better in ROC. An example of a significant change was between Single and NaÃÆ'ƒÂ ¯ve2. These methods improved by 11 ranks when compared with SMAPE. In addition, combined methods did still out perform individual methods. As it showed that Winter, which was explicit trend model did worst in all of the method s. Furthermore, the worst combined method was Flores-Pearce1, which was 19 in ROC and 16 in SMAPE. For all the agreement mentioned above, ROC and SMAPE were still different methods of error measurement and did produce different results. In ROC, errors could be divided into normal and quadrant. This gives the researchers more information on how each forecasting methods did and also indicates where and how improvements could be justified. Also from this extended information of the measurement error, performance on each forecasting horizon in each forecasting methods could be compared against normal and quadrant errors. As mention in the definition of ROC, normal error could be divided into two types, over-estimates and under-estimates. This information would be critical on the improvement of forecasting methods. In this study, the number of error points in each type was calculated. An example of this could be seen in Single (Table A4) and Winter (Table A5) [ROC results] in the a ppendix. From this sample, the table also showed that correct data point were also obtained form the ROC. Despite the advantage of this information, quadrant errors were still the most dominant in magnitude of the calculated error in each forecasting method. As in NaÃÆ'ƒÂ ¯ve 2, which had the most correct data points with 90, still came 11 in the overall rank. Also this could be supported by Winter which had more correct data points and was still overall the worst performance. If the over-estimates and under-estimates are considered and separated into positive change (Sector 1, S1 and Sector 2, S2) and negative change (Sector 4, S4 and Sector5, S5) then further comments could then be made on each individual observation in each forecasting method. Thus it could be argued that despite the fact that SMAPE and ROC produce the same conclusion on the overall performance of the type of forecasting methods, one major difference could be identified. In that it was true to say that ROC could be used as to better understand the errors caused in each forecasting methods. Such analysis is not possible with other methods of error measurement such as SMAPE. The ranking of each of the forecasting methods are listed in table 8: Rank SMAPE ROC 1 Theata Single 2 Forecast Pro Theata 3 Force X Comb S-H-D 4 Comb S-H-D Force X 5 Dampen Flores-Pearce2 6 RBF SmartFcs 7 B-J automatic Theata-sm 8 Automat ANN Dampen 9 SmartFcs Forecast Pro 10 PP-autocast AAM1 11 Flores-Pearce2 NaÃÆ'ƒÂ ¯ve2 12 Single AAM2 13 Theata-sm B-J automatic 14 Autobox2 RBF 15 AAM1 Automat ANN 16 Flores-Pearce1 PP-autocast 17 ARARMA Holt 18 AAM2 Autobox3 19 Holt Flores-Pearce1 20 Winter Autobox1 21 Autobox1 Robust-Trend 22 NaÃÆ'ƒÂ ¯ve2 Autobox2 23 Autobox3 AR ARMA 24 Robust-Trend Winter Table 8 Comparative ranking between SMAPE and ROC Graph 4 Comparative ranking between SMAPE and ROC 7.0 Discussion and Conclusion This study has replicated the results from the M3-Competition, despite some of the mis-matching of the ranking of methods. Also, the Rate of Change (ROC) concept has been introduced as another method of error measurement. From the results of ROC analysis, many characteristics of the forecasting methods have been better understood. In the analysis, AAM1 and AAM2 had seemed to perform better in most of the categories, but when the number of observations was taken into account their true ranking was obtained. After normalising the results to per observation in table 7, Single and Theata did perform as well as expected. Single had the least number of under-estimated errors and Theata had the least number of quadrant errors. However, Robust-trend had the least over-estimates. This was not expected since overall it was one of the worst performing forecasts in both SMAPE and ROC. But, it could be argued that it had presented the most number of under-estimated errors and therefore this mea nt that the total normal errors would be much higher than the other methods. Also from the ROC results per observation, it was evident that AAM1 and AAM2 were the worst methods in over-estimates, under-estimates and quadrants. In ROC analysis, the number of correct forecast could also be accumulated. This showed that NaÃÆ'ƒÂ ¯ve2 had 90 correct values, whilst the others had only 20 correct values. Also the Winter had obtain 1 correct value, thus it was regarded as the worst performer in ROC. However from the results, it was true to say that quadrant values would still be the most dominant on the performance of the forecast. This meant that the higher the quadrant, the less likely for the forecasting method to perform well. For example, NaÃÆ'ƒÂ ¯ve2 had more correct values than Single. But Single did have lesser quadrant values. Therefore Single would out perform Naive2, [see the results recorded by rank, table 8]. Also ROC analysis showed that the resu ltant information could be used to improve the accuracy of forecast. [this was mentioned in the comparative of SMAPE and ROC]. In ROC, the trend of the plot in each forecasting horizon could explain the performance of each method. A good example would be by taking ROC plots of the forecasting horizons from the best and worst methods respectively, [Single and Winter]. The Single plot was taken from the 11th forecasting horizon, and the 9th forecasting horizon was taken from Winter data. By comparing the two plots, the differences in the distribution of errors can be clearly seen. In Single, the data points are mainly distributed in the sectors 1, 2, 4 and 5. This means that the errors which were obtained would under-estimates and over-estimates [normal errors]. Whilst, in Winter a large numbers of data points fell in sector 6, which is quadrant error. This means that a greater error was created from the Winter method than from the Single method. In addition, the Winter plot showed that there were more data points in sectors 1 and 4 than in sectors 2 and 5. This meant that the method tended to over-estimate the forecasts. Also it was still to be noted that there were some minority of under-estimates. Also the same observation could be used on Single plot. There were more data points in sector 1 than sector 2. This meant that the method tended to over-estimate in a positive direction. However, this was different in negative direction since there were more data points in sector 5 than in sector 6. Therefore, the method tended to under-estimate in the negative direction. From these observations, the analysis on each method could be sent back to each forecasting method as advice. Then this could be used to improve the methods for better forecasts in the future. Therefore, it was clear that ROC has a much greater advantage in analysing forecasts than other measurement methods. However, more work is needed to produce an ROC analysis than is required for a SMAPE analysis. Also this means that more care is needed since the calculations increase as the number of forecasting horizons in each forecasting method increases. Conclusion This study has replicated the results of the M3 competition using SMAPE as the means of error measurement and undertaken a further analysis on the same data using the ROC method developed by John (2004). The study has proven that the conclusions of the M3-Compeition are still valid. However, it could be argued that re-analysis of SMAPE was not totally reliable due to some mis-matching in the ranking performance of the original SMAPE in M3-Competition and calculated SMAPE from this study. Also there were data errors found in the source data. In addition as mentioned earlier, rounding error could have occurred in the calculations, [as suggested by Chatfield C. (1988, p. 28) when he said that There are obvious dangers in averaging accuracy across many time series]. Meanwhile, this could be argued that the calculated SMAPE produced the same conclusions as previous SMAPE results in M3-Competition. In addition the same conclusion was also recognised by ROC. This meant that all the res ults tested produced one critical conclusion which was that Simple method did outperform many of the more sophisticated methods, which included individual and combined methods. Also in general, the combined methods did perform better than the individual ones. From this analysis, the use of the Simple method could be now more appreciated since it gave an equal or greater result of accuracy when compared to the more complex forecasting methods. Also this would broaden the uses of the forecasting method, which despite the fact that accuracy was just one indicator of performance beside cost, ease of use, and ease of interpretation. Also this was mentioned by Chatfield (1998, p. 21), that simple methods were considered likely to be easily understood and implemented by managers and other works that used these forecasted results. Reference: Armstrong J.S. 1985 Testing Outputs, Long-Range Forecasting from crystal ball to computer (2nd Edition) Wiley-Interscience Publication, 348 Chatfiled C. 1988, What is the best method of forecasting?, University of Bath, Journal of Applied Statistics, Vol. 15, No. 1, Gilchrist W. 1976, Statistical forecasting, Wiley-Interscience Publication, 222-225 John E. G., 2004Comparative assessment of forecasts, International Journal of Production Research, Vol. 42, NO. 5, 997-1008 Makridakis S. et al. 1979 Accuracy of Forecasting: An Empirical Investigation, Spyros Makridakis and Michele Hibon, Journal of the Royal Statistical Society. Series A (General), Vol. 142, No. 2, 1979, 97-145 Makridakis S. et al. 1982, The Accuracy of Extrapolation (Time series) Methods: Result of a Forecasting Competition, Journal of Forecasting, 1, 111-153 Makridakis S. et al. 1993, The M2-Competition: A real-time judgmentally based forecasting study, International Journal of Forecasting, Vol. 9, 5-22 Makridakis S. et al. 2000, The M3-Competition: results, conclusions and implications, International Journal of Forecasting, Vol. 16, 451-476 Theil H. 1958, Economic forecasts and policy, North-Holland Publishing Company, Amsterdam, 29-30 Website: M3-Competition data, Available at: https://forecasters.org/data/m3comp/M3C.xls Marco code, Available at: https://club.excelhome.net/thread-346575-1-1.html [Accessed: 8 January 2010]. Equations: 1.1 Symmetric Mean Absolute Error. From: Appendix A, The M3-Competition: results, conclusions and implications, Spyros Makridakis, Michele Hibon, International Journal of Forecasting, 2000, Vol. 16, 461 2.1 Actual series pair, 2.2 Forecast series pair, 2.3 Normal Error, and 2.4 Quadrant Error. From: Rate of change method (ROC), Comparative assessment of forecasts, E. G. John, International Journal of Production Research, 2004, Vol. 42, NO. 5, 1000-1001

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Development Of Coronary Heart Disease Health And Social Care Essay Free Essays

In this essay I will debate about the relationship between high blood pressure and type 2 mellitus diabetes ( T2DM ) . Hypertension has a taking factor to play in cardiovascular diseases ; high blood pressure and diabetes affect the vascular tree. Hypertension is a long lasting status which makes the blood force per unit area rise above the normal norm, 90 % of high blood pressure is indispensable they can be classed as two different types primary and secondary, when you get high blood force per unit area because of other factors such as the kidneys or tumours this is known as secondary high blood pressure. We will write a custom essay sample on Development Of Coronary Heart Disease Health And Social Care Essay or any similar topic only for you Order Now Type 1 Diabetes is when your organic structure fails to bring forth insulin and requires insulin to be injected. Type 2 diabetes is when the organic structure is n’t utilizing insulin in a correct and most efficient manner. Atherosclerosis vascular disease is besides known as coronary artery disease ; this is the thickener of the arterias and builds up of fat stuffs like cholesterin. Connery bosom disease is when there is a obstruction in your Connery arterias because of fatty acids and stops the blood being pumped around the organic structure. The major factor to shots and bosom onslaughts is due because of relentless high blood pressure. From the NHS web site I can find that Connery bosom disease is the biggest slayer in the UK, at least 300,000 people dice of Connery bosom disease every twelvemonth impacting 1 in every 4 work forces and 1 in every 6 adult females. From the national UK nose count they say that the most common cause for the past 90 old ages has been Connery bosom disease, see table 1.1 ( from the tabular array you can see that in merely England and Wales disk shape disease is the most common. As age increases the opportunity of acquiring Connery bosom disease increases every bit good this is because of your immune system non working every bit good and because of the unhealthy life style being lived with non adequate exercising. Table 1.1 Connery bosom disease occurs when the Connery arteries subdivision of from the chief aorta this is merely above the aortal valve, when fatty acids build up in the blood vas walls and shorten the transition manner of the blood to flux through. From the NHS web site I can find that Connery bosom disease is the UK biggest slayer, around 300,000 people have a bosom onslaught each twelvemonth there is about 1 in 6 adult females deceasing from the disease and around 1 in 4 work forces. Connery bosom disease when there are big sums of cholesterin in your organic structure this can besides take to atherosclerosis. In the US these figures are a batch higher the hazard of holding |CHD over 40 is 49 % for work forces and 32 % for adult females there has been a lessening in the figure of deceases from CHD for people aged 65 and under. But for people over the age of 65 there is a lessening in the figure of people deceasing from CHD but it is well more that people aged 65 and under. This is due to the life manner people have changed over the old ages. CHD decease rate as a per centum of 1980 rate among work forces and adult females aged 55-64 old ages in England and Wales ‘s clip span of 1980-1995 Type 2 diabetes can be genetically inherited non all is bad, some of them are good heritage they help forestall type 2 diabetes. A major factors for diabetes is obesity, from my statistics I can see that the national Centre for the wellness shows us that there is around 60 million corpulent people, this is because there is a higher hazard of insulin opposition because it interferes with the organic structure to utilize the insulin. The figure of kids with type 2 mellitus diabetes has besides increased. 90 % of people that have been diagnosed with type diabetes were overweight. There is excessively much fat in the organic structure and there is non adequate fiber and simple saccharides these all aid to the diagnosing of diabetes. Eating good and right can change by reversal these reactions and they can forestall type 2 diabetes. Peoples who have household diagnosed with type 2 diabetes have a greater hazard in developing it themselves. African Americans have a familial temperament tow ards type 2 diabetes. Age besides has a large part. FIG 1.2 Diabetes is when there is an addition in glucose and it rises above the normal degree, type 2 diabetes can besides be inherited genetically non wholly inherited diabetes is bad because some aids to forestall diabetes. From the national wellness statistics I can see that there is around 60 million corpulent people, fleshiness has a immense contributing factor to diabetes, the larger a individual is the higher the opportunity of the insulin being wasted and insulin opposition, type 2 diabetes increases the opportunities of a cardio vascular bosom disease this is due to your bosom working harder to get the better of the little sums of glucose being turned into glucose. If there is high degree of glucose or even if there is non plenty in your organic structure for a long period of clip this can damage the blood vass and can take to bosom onslaughts, shots and hapless circulation. Atherlersiosis is a status that hardens the arterias ; the arterias are blood vessles which carry rich O blood around your organic structure. The arterias harden due to the physique up of plaque this causes them to contract over clip and because of the arterias being narrowed there is less blood being pumped around the organic structure and to your bosom. The Plaque inside the Connery arterias is made up of fat, Ca and cholesterin, this status is known as coronary artery disease. This status causes the arterias to lose they elasticity ; this restricts the blood flow and causes high blood pressure which is addition of the blood force per unit area. This makes the arterial force per unit area to increase and causes the bosom musculus to work twice every bit difficult to pump the sum of blood a normal bosom would pump, these lending factors could take up to hold shots or even bosom onslaughts. Atherelerosis development is foremost when the fatty run is followed by the formation of plaqu e and so characterised of the increasing sum of macrophage and froth cells. Looking at FIG 1.3 you can see that A is a healthy arteria that would pump blood around the organic structure without a job, but from B you can see that there is a build up of plaque and this arteria would hold problem pumping blood about. Many people that have this status are incognizant but merely happen out after they have had a shot or a bosom onslaught, the chief intervention that is used for atherlerosis is to alter your life style, there are medical interventions and medical specialties that you can take to assist populate a healthier life style. Type 11 mellitus diabetes ; high blood pressure is linked together and all lead up to impacting the bosom. Age has a major function to play as this is an uncountable factor by acquiring one of these status e.g. high blood pressure this may take to atherlerosocis. All 3 conditions play a major function in impacting the bosom and barricading the blood vass. First if type 11 mellitus diabetes is developed and non looked after and controlled the sum of glucose will lift, as the glucose rises this puts force per unit area on the kidneys to increase their working rate to interrupt down the glucose and halt it from developing into cholesterin. When there is a build up of glucose and the is excessively much strain on the kidneys they begin to neglect and can non maintain up and this leads to cholesterol being deposited into the blood vass, as above in FIG 1.3 as cholesterin is deposited into the blood vass this leads to the physique up of plaque and eventually leads to atherolerosis. This ca uses the blood vass to halt working decently as it has restricted the blood flow, this changes the manner the blood is flowed through the arterias and leads to high blood pressure. Atherlerosis is the major cause of Hypertension. Hypertension has a prima function to play to Connery bosom disease this is the blood vass are already strained by the physique up of plaque and high blood pressure increases the strain which causes the blood vass to rupture and rend. The force per unit area builds up in the blood vass and the blood is filled in the cryings which cause coagulums and this leads to Connery bosom disease. The best manner in commanding conary bosom disease and handling it is to keep a healthy life manner. By commanding emphasis degrees will assist forestall high blood pressure, seeking to avoid emphasis and nerve-racking state of affairss and maintaining a positive head. Exerting on a regular basis helps the variety meats to work decently the NHS say at least 4-5 per hebdomad for exercising is required. To drop cholesterin degrees eating a balanced diet with at least 5 parts of fruit and vegetable and cutting back on fatso nutrients and saccharides. Eating meats in moderate parts will besides assist. Cuting back on tea, java and intoxicant can assist cut down the physique up of plaque and atherlerosis. Stevens, RJ et Al 2005 says A glass of ruddy wine daily is good for the bosom. Drug intervention for conary bosom disease is different for each person as the physicians prescribe you medication on your life style and side effects. Low doses of acetylsalicylic acid could be given as this helps to forestall blood curdling and reduces the hazard of bosom onslaughts. ACE inhibitors could be taken these aid to handle high blood force per unit area besides known as high blood pressure, this causes the blood vass to loosen up and widen and assist to take down the blood force per unit area. The drugs nitrates may besides be used this helps to forestall thorax strivings and besides widens the vass. To assist command cholesterin degrees a statin drug is used these types of drugs are merely prescribed after the non-drug interventions described as above have all been extinguished this drug helps to forestall shots and bosom onslaughts. The chief types of drugs that are used to assist handle conary bosom disease are lipid-lowering medicines and ACE inhibitors. hypertext transfer protocol: //www.nhs.uk/conditions/Coronary-heart-disease/Pages/Introduction.aspx hypertext transfer protocol: //www.healthcentral.com/heart-disease/drugs.html How to cite Development Of Coronary Heart Disease Health And Social Care Essay, Essay examples

Death Penalty Research Paper free essay sample

The Death Penalty Research Paper English Composition ENG101 03 December 2011 Abstract The death penalty is a subject of much debate amongst the American people. Some people support capital punishment while others do not. Examination of sources and analyses of important history regarding the death penalty will hopefully add to the understanding of why it is so important in our day and age to have such a penalty to deter and deal with the most violent of offenders in our modern day society. A major influence on my position is my uncle being murdered when I was younger. The points I use to support my argument for being Pro Death Penalty are the history of the death penalty, the death penalty as a deterrent, cost comparison between the sentences of life and death, and is victim’s justice served. Knowledge of this personal influence, and the points mentioned above adds to the understanding of being Pro Death. The Death Penalty The position I take on the issue of the death penalty is one of deep conviction due to personal experience with a murdered family member. Many people might say that this would cause me to have a biased view. This experience if anything gives me a better understanding of the importance of such a punishment, and I truly believe that you can not accurately speak on such a topic unless you yourself has been affected by the outcome of such a heinous crime. Therefore the death penalty should not be banned as a form of punishment and should be implemented in a much more aggressive manner. Besides the murder of my uncle influencing my decision, there are a few other reasons to support my position on the death penalty, and those are, looking at the history of the death penalty, the death penalty as a deterrent, cost comparison (Life vs. Death), and victim’s justice served (retribution). The history of the death penalty in America was derived from the British. The first recorded execution in America was that of Captain George Kendall for being a spy for Spain in 1608. All throughout colonial times the death penalty was evolving from Duke’s Laws of 1665 which meant you could be sentenced to death for stealing grapes, or striking ones own parents, to Thomas Jefferson proposing a bill to only enforce death sentences for the crimes of murder and treason. In 1794 Dr. Benjamin Rush with the support of Benjamin Franklin led Pennsylvania to become the first state to consider degrees of murder. That same year Pennsylvania repealed the death penalty for all offenses except first-degree murder. As we came into the nineteenth and twentieth century some states began abolishing the death penalty while some states held onto capital punishment recognizing the importance of ridding society of worthless human beings. Surprisingly as time goes on from then till present day people continue to become a weaker and weaker society losing the backbone that our forefathers once had. Unfortunately our justice system shows more sympathy for criminals than it does victims. People would rather coddle a violent criminal instead of giving them the sentence they deserve, death. The fear of death is by far a person’s greatest fear, it does not matter who you are, or what you do for a living, no person wants to die. There is no better deterrent to violent crime than capital punishment. (Clemson University Professor Shepherd) found that each execution results, on average, in five fewer murders. The problem we face with deterrence is the speed in which the death penalty is carried out, to instill the fear that you will be dealt with swiftly for your violent crimes. Without a shadow of a doubt the death penalty is a deterrent and saves a lot of innocent lives. Actual testimony from convicted individuals states this fact. One example is of an Iowa prisoner, who escaped from a transportation van, with several other prisoners. While this escape took place he made sure that no harm or assault came to the guards because they were in Texas and he feared being sentenced to death if one of those guards were killed. He was already a twice-convicted murderer in Iowa where there is no death penalty for murder. So although he was already a convicted murderer the threat of the death penalty deterred him from killing or allowing any of the other prisoners to kill any of the guards. Another example is of a woman who robbed a store with an unloaded gun. When asked by police why she didn’t have a loaded gun she replied, if things got out of hand she did not want to lose her cool and start shooting people, and possibly kill someone for fear of being sentenced to death. The topic that is at the forefront of every death penalty argument these days is the cost-comparison of â€Å"life in prison vs. death†. There are a couple different ways to dissect this issue. It is true that a capital case is more expensive than a life sentence case. This is because of the long drawn out nature of these cases. The appeals that are associated with them cost the court even more time and money. Finally the burden on the taxpayer for having to foot the bill for their housing in prison while they wait for years on death row. California is home to the largest death row population in the U. S. , it costs an extra $90,000 per inmate to imprison someone sentenced to death, an additional expense that totals more than $63. 3 million annually for 667 inmates on death row. Today this seems to be the major argument for the population that does not support capital punishment, that it just costs too much money to sentence somebody to death. That is because the inmate generally will sit on death row for 20+ years bleeding the taxpayers dry. The easy solution to this problem is to speed up the process in which a guilty verdict, with the sentence of death is imposed and carried out. With the advancements in policing, evidence gathering, and evidence processing, when a sentence of death is imposed it should be done so without a shadow of doubt. Once that has been done, within a week the individual should be executed, saving the state and the people a lot of money. This is the only way to cut costs and to make the death penalty a more effective deterrent. The ones that pay the most and lose the most in a capital case is the victim’s family. They not only have to deal with the loss of a loved one, they have to go through the long drawn out process of dealing with a criminal trial and every painful memory that is brought up along the way. The entire time all the family is seeking is retribution for the loss of their loved one. People often confuse retribution with revenge. Vengeance signifies inflicting harm on the offender out of anger because of what he or she has done. Retribution is the rationally supported theory that the criminal deserves a punishment fitting the gravity of his or her crime. As a victim these two closely mirror each other with the scale leaning more towards vengeance. In a perfect world I believe it should be up to the murdered victim’s family to decide whether or not the death penalty be carried out, because they are the only ones that should have a true voice in the matter, not some reporter so far detached and removed from the actual occurrence speaking about something that they have no emotional attachment to. The only attachment or opinion they have is of something they will not fully understand until it happens to them. Then I bet they would be singing a different tune. In most cases when an inmate is executed this brings some form of closure to the family of the victim in knowing that justice was served â€Å"Eye for an Eye†. The death penalty as a form of punishment, the ultimate punishment will always be up for discussion and debate as long as it still exists in our society. Whether you are pro death penalty or against it altogether, I hope you will never have to be brought so close to this topic as my family and I have been.