Moudawana reforms in Morocco Research Paper Example | Topics and Well Written Essays - 3000 words

Moudawana reforms in Morocco - Research Paper Example Women play a major role in the family dynamics in Morocco as they help form the structure of the society. Initially, Moroccan women had fewer privileges to enjoy in the family. They were treated as inferiors and the men around them made all family decisions. While women were restricted from divorcing their husbands, men were allowed to break up with their wives without their consent. Women could not marry without the approval of their guardians, and when married, they were to obey their husbands. On the other hand, men could marry as many wives as they wanted without any approval from their wives. In other words, women were treated as objects. When the Moudawana reform movement was adopted in 1958, these unjust laws continued to prevail as women had no control over their marriage life. Some even argued that getting married was the end of a woman’s life because if she was unfortunate and faced hardships, she did not have an easy way out. All this started to change, when the Wom en's Action Union was formed and decided to sweep out these injustices against women. This union catalyzed the reform to play its full part in the fight. This was the start of the Moudawana Reforms in Morocco. Prior to the formation of this union, the reform movement governed the family laws but gave few privileges to women. The governed areas by the law included child custody, inheritance, divorce and marriage. Men enjoyed many privileges and saw traditional laws as an opportunity to suppress women. The same laws made life unbearable for women and bound the reforms. This paper will focus on the contributions made by women and especially the Islam activists in Moudawana reforms. The efforts made by different organizations led by women activists would also be highlighted. Moudawanna as a national Issue The Moudawana law suppressed women as it gave them limited opportunities to enjoy their rights. The main goal of the activists as argued earlier was to ensure that women were treated w ith fairness in the society. This was a fight against authoritarianism as defined by Childress. It is defined as the type of ruling used by rulers to oppress their women. In this case, women were the oppressed group. Regarding women as the main element making up the family and eventually the society, they had to have privileges in life. Men could make any decision in their marriage without the consent of their wives. On the side of women, even the least decisions, for example deciding who to get married to and when, needed a guardian’s intervention. Women were getting married at the tender age of 15 while instead they should have been in schools studying. This shows that women were deprived of their human rights (Bran 276). As argued by Bayat, resource mobilization theory, collective behavior approach, and crowd theory were necessary. No single woman could push for the reforms on her own efforts. This called for an ‘imagined solidarity’ in which women had to come together and create set actions that had to be followed (890). Imagined solidarity was a situation in which different people or groups visualized to have similar interests even though they fought using different strategies but headed to the same goal. Similarly, Childress argues that social, community-based, coalitional, and organizational movements played a major role in the entire reforms. This was because with the political parties in place, activists saw community-based movement as the best (1). The argument to support this was that people were heard the most when they are together. The main goal of the activists in the reform was to persuade the government to treat women more equally, just as they treated men. Their main goal could not be reached by the activists’ words only, and they needed support from as many Moroccans as possible so that it would be easier to prove to the government that the Moroccans indeed demanded for change. Engaging many Moroccans into the idea of reforms was done through the 1 million-signature petition

Carbon, Phosphorus and Nitrogen Cycles Essay Example for Free

Carbon, Phosphorus and Nitrogen Cycles Essay The carbon cycle starts with the reservoir of the carbon dioxide in the air, the carbon atoms move from carbon dioxide through photosynthesis into atoms of organic molecules that form the plants body. These carbon atoms are then further metabolized and are eaten and turned into tissue that all organisms in the ecosystem use. Half of the atoms are respired by the plants and animals and half are deposited back into the soil in the form of dead animal and plant matter, which are eaten by decomposers and transformed back into carbon dioxide. Humans impact this cycle because we are removing so much of the photosynthetic efforts of the plants in order to support our enterprises, we are â€Å"diverting 40% of the photosynthetic productivity of land plants to support human enterprises,† (pg 67). Two examples of our harmful tendencies are burning fossil fuels which has increased atmospheric carbon dioxide â€Å"35% over preindustrial levels,† (pg. 67) and logging. These both are being used naturally by the ecosystem and the lack of these resources causes stress and strain to keep the balance. At the rate it is going carbon to complete its cycle from the atmosphere through one or more living organism and back to the atmosphere happens about every 6 years. The phosphorus cycle includes the cycle of all the biologically important nutrients found in the natural minerals. These elements include iron, calcium, potassium found in the rock and soil minerals in the lithosphere. Over time a rock breaks down and releases phosphate (PO43-) and other ions which replenish phosphorus that is lost due to runoffs and leaching. The phosphate is absorbed by plants and turned into compounds that are moved through the food chain. Humans impact this cycle because we are using the phosphorus to make fertilizers, animal feeds, detergents or other products and mining these locations. Our water systems are being damaged because â€Å"human applications have tripled the amount of phosphorus making it to the oceans,† (pg 68). This is a problem because it causes over fertilization or eutrophication of the aquatic ecosystem. The waterborne phosphorus cannot be returned to the soils this causes too much bacteria or algae in the water and kills of the fish and other water mammals. The nitrogen cycle is similar to the carbon and phosphorus cycles; because it has a gas phase like carbon and can also be a limiting factor such as phosphorus. The main form of nitrogen is in the air â€Å"which is about 78% nitrogen gas (N2),† (pg 68). The plants change the nitrogen into organic compounds which are necessary like proteins and nucleic acids. Humans impact this cycle because many of our crops are legumes or nonleguminous. Legumes like peas, beans provide the bacteria a place to live and a source of food and receive nitrogen in exchange, where it enters the food web. Nonleguminous crops such as corn, wheat, potatoes and cotton have to be heavily fertilized with nitrogen’s from industrial fixations. The over fertilization of nitrogen into the soils are destroying lakes, ponds and forests. However our actions are more than doubling the rate which nitrogen is moved from the atmosphere to the land, â€Å"nitric acid has destroyed thousands of lakes and ponds and caused extensive damage to forests,† (pg 70). Humans have a great impact on all three cycles. If it continues the way that we are using fossil fuels, and destroying the land as we are currently are. We are depleting our resources at a faster rate than we can sustain naturally which is causing harmful living conditions which we may not necessarily feel the repercussions of immediately.

Network Opimisation Problems And Forecasting

Network Opimisation Problems And Forecasting The Makonsel Company, a fully integrated company that both produces and sells goods at its retail outlets. After production, the goods are stored in companys two warehouses until needed by the retail outlets. Trucks are used to transport the goods from the two plants to the warehouses, and then from the warehouses to the three retail outlets. Using units of full truckloads, the following table shows each plants monthly output, its shipping cost per truckload sent to each warehouse, and the maximum amount that it can ship per month to each warehouse. Unit Shipping Cost For each retail outlet (RO), the next table shows its monthly demand, its shipping cost per truckload from each warehouse, and the maximum amount that can be shipped per month from each warehouse. Unit Shipping Cost The Managements objective is to determine the shipping plan (number of truckloads shipped per month from each plant to each warehouse and from each warehouse to each retail outlet) that will minimise the total shipping cost. In order to achieve the objective, the following issues will be discussed : The distribution network of Makonsel Company, algebraic formulation for the network model, spreadsheet formulation for this problem by using the solver of excel and interpretation and recommendation of the result. The distribution network A network model for the Makonsel Company problem as a minimum-cost flow problem According to the data from the table above we put it into a distribution network. The supply nodes in this network are P1 (plant1) and P2 (plant2), the transshipment nodes are W1 (warehouse1) and W2 (warehouse2) and the demand nodes in this network are RO1, RO2 and RO3. And the shipping cost and the shipping capacity differ considerably among these shipping lanes. The cost per unit shipped and the maximum amount that it can ship per month (given in square brackets of the arc) through each lane is shown above corresponding arrow in the above Figure. Algebraic Formulation Solution: Decision variables Makonsel must determine how much to ship per month from each plant to each warehouse and from each warehouse to each retail outlet. Let Xij = Number of truckloads to ship from i to j (i = P1, P2; j = W1, W2). Let Xjk =Number of truckloads to ship from j to k (j =W1, W2 k=RO1, RO2, RO3). Then Makonsels problem may be formulated as Objective: Subject to: The first five constraints ensure that each retail outlet is meet their monthly demand, and the 2 Sources constraints are ensure that each plants monthly output and the last 10 ensure the maximum amount that can be shipped per month. Spreadsheet Formulation After we finished the algebraic formulation, we can transform them to spreadsheet, and using the solver of Excel to work out the distribution problem. The spreadsheet formulations are all showed in the graph below. A spreadsheet model for the Makonsel Company minimum-cost flow problem, where the changing cells (C4:C13) show the optimal solution obtained by the Solver and the target cell (E15) gives the resulting total cost of the flow through the network. Interpretation and Recommendation The optimal solution for the Makonsel Company problem, where the shipping amounts are shown in parentheses over the arrows By using excel we can calculate the minimum total shipping cost of Makonsel Company is  ¿Ã‚ ¡488.125. In order to make the minimum total monthly shipping cost of  ¿Ã‚ ¡488.125, the Makonsel Company should first transport 125 truckloads per month from plant 1to warehouse 1 and 75 units to warehouse 2. And ship 175 truckloads per month from plant 2 to warehouse 1, ship 125 truckloads per month to warehouse2. After that the retail outlet1, retail outlet 2 and retail outlet 3 should get 100 truckloads, 50 truckloads and 100 truckloads from warehouse 1 respectively. And should separately transport 50 truckloads, 150 truckloads and 50 truckloads from warehouse 2 to retail outlet1, retail outlet 2 and retail outlet 3. As we have known the shipping cost per truckload from each plant and each warehouse from the table. Thus the Minimum Cost= 425*125+560*75+510*125+600*175+470*100+505*50 +490*100+390*50+410*150+440*50 =488,125 Conclusion Determined the shipping plan which can minimise the total shipping cost is the management objective of Makonsel Company. By building the distribution network , formulating the constraints and calculating the result through using the solver of excel , Makonsel Company successfully solve the distribution network problem and construct the shipping plan with the minimum total shipping cost of  ¿Ã‚ ¡488.125. Forecasting Introduction The time-series below relates to the Sales of a company (00s) for the last five years. The objective is to use the information contained in the time-series data above to construct a forecast of the next four quarters sales. In order to achieve the objective, the following issues will be discussed: Analysis this time-series, Detrend a Time-Series and construct the Seasonal Indices by MINITAB, Forecasting the next four quarters sales and use measures to identify the forecast accuracy, Reservations about the appropriateness of the forecasting procedure used. Time-series Analysis Main characteristics of this time-series The first step in any forecasting exercise is to plot a graph of the time-series. We transfer the data from the table to Minitab and use the time series plot-simple of Minitab to make the graph, since the time-series was recorded in quarter, so we choose the quarter of calendar in time scale. The plot of this time-series looks like: Form the graph above We can roughly find out that there is a decreasing trend over time, a clear quarterly seasonal effect and it is a table time series, the pattern is regular with little random noise. With the purpose of confirming the characteristics of the time-series, we use the cantered moving averages (CMA). Since the CMA is the average and smoothed data of the actual figures, which is much easier for us to determine the characteristics of the time-series, we use this plot instead. As the time-series was recorded in quarters and with quarterly seasonal effect so the length of moving average is 4, and chose the moving averages, plot the graph smoothed vs. actual. Negative Trend Structure, an decreasing trend over time It is a negative trend structure. Look at the smoothed line of this time-series, as at the beginning of the time-series the sales of this company is about 485 ,however , it keeps decreasing and from about 485 down to around 478 to roughly 471 and finally it decrease to around 405. A clear seasonal structure , additive seasonal structure It can be seen from the graph above that there is a clear quarterly seasonal structure, for each quarter 1 the actual observed value is about 13 units below the trend value. For quarters 2, 3 4 estimating from the graph the actual observed values are 30 above, 22 above and 23 below the estimated trend values. Seasonal Structures: Q1 Q2 Q3 Q4 -13 30 22 -23 These are estimates of the seasonal indices; and also in this case, for a given variable the quarter 1 is 13 units below trend, quarter 2, 3 are 30 units and 22 units above trend, quarter 4 is 23 units below trend. And it can be seen from the graph above, seasonal deviation is constant about the trend so this seasonal structure is additive. A table time series, the pattern is regular with little random noise The graph of Moving average plot for sales above shows us that the pattern is regular with little random noise, it decreasing stability of the seasonal pattern, and also from the smoothed line we can find that the series reduce stability, from about 485 down to around 478 to roughly 471, ect. No more than 10units lower. Model the time-series QUADRATIC TREND MODELS There are two trend models ,one is linear trend model (Trend = a + b*t ) and the other is Quadratic trend model (Trend = a + b*t + ct2 ), and as we have been calculated the Cantered Moving Average (CMA)above, which is the average and smoothed line of the actual sales, so by using the CMA, we can use value of these two models to compare with the value of CMA, and then choose the model which the value is much closer to the CMA as our forecasting model. There are three commonly used measures of forecast accuracy: Mean Square Deviation (MSD), Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE). And the smaller the data is, the more accurate of the forecast. And it can be seen from the graph above that the Quadratic Trend Model, MAPE=0.43297, MAD=1.91172, MSD = 5.44313, and to the Linear Trend Model, MAPE=0.51281, MAD=2.21232, MSD = 7.74838. The data of the Quadratic Trend Model are all smaller than the Linear Trend Model, which means that the value of quadratic trend model is much closer than value of the CMA; the quadratic trend model is much more accurate than the linear trend model, so choose the quadratic trend model to forecast. Detrend a Time-Series and construct SI of MINITAB Detrend a Time-Series Detrend a Time-Series, which means Sales-Trend (DIV), the gap between the actual sales and the forecast sales. After we have decided to take the quadratic model to forecast, we can record the data as the trend data, and the plot the graph above to compare with the sales and trend. And use the actual sales data minus the forecast one we can Detrend a Time-Series. As the graph shows us above the DIV1=472-500.367=-28.3673, DIV2=516-493.333=22.6674, DIV3=507-486.459=20.5414, DIV4=462-479.745=-17.7454, etc. By using the Minitab, we can use the calculator to figure the result. Construct the Seasonal Indices by MINITAB As Seasonal Indices is the quarter average of DIV, after we have calculated the DIV, we can use MINITAB to construct the seasonal indices. And in the MINITAB, we use the decomposition to figure out the SI. As we have described before that the sales trend of this company is additive and seasonal and the data were recorded in quarter, so the seasonal indices is four quarters as a unit, the seasonal length is 4 and the model type is additive. And Seasonal Indices is the average of each quarter of DIV, so the seasonal indices can be calculated as below: Quarter1=SI1= (DIV1+DIV5+DIV9+DIV13+DIV17)/5= -24.0937 Quarter2=SI2= (DIV2+DIV6+DIV10+DIV14+DIV18)/5=20.4062 Quarter3=SI3= (DIV3+DIV7+DIV11+DIV15+DIV19)/5=17.2812 Quarter4=SI4= (DIV4+DIV8+DIV12+DIV16+DIV20)/5=-13.5937 Since seasonal indices is the average of each quarter of DIV so SI is quarterly cycle, the value of SI5 will equal to the value of SI1, SI6=SI2, etc. And also it can be seen from graph above that the SI is quarterly cycle. Forecasting and measures of forecast accuracy Future Forecast As the Future Forecast equal Future Trend plus Future Seasonal Indices, so first we should use the CMA to calculate the future trend of the next four quarters. Since the CMA is the average and the smoothed data of the actual data, using the data of CMA can let forecast more accuracy. And the time-series is seasonal structure of quarter, so the number of forecast is 4. And we use trend analysis to calculate the future trend. After we figure out the future trend, copy the first four Seasonal Indices (SI is quarterly cycle) which we have calculated before (-24.0937, 20.4062, 17.2812, -13.5937), as Future Seasonal Indices. And then use the FTrend and FSI to figure out the Future Forecast value (FFC=FTrend+FSI). After figure out the FFC, copy them after the FC to plot a forecast. The plot of time series of sales and forecast looks like: So the next four quarters; Q1, Q2, Q3 Q4 of 2009 are: Q1=366.116, Q2=406.796, Q3=400.011, Q4=365.637 Measures of forecast accuracy After we calculate the forecasts for the next four quarters, we need to know whether the forecast is accurate or not, so we use the three commonly used measures of forecast accuracy: MSD, MPE and MAD to check the forecasts. i. Mean Square Deviation: MSD = S (Xt Ft)2/n ii. Mean Absolute Deviation: MAD = S |Xt Ft| /n iii. Mean Percentage Error: MPE = S |(Xt Ft)/Xt| /n Since all of measures above need the value of Xt Ft (error), so we should calculate the error first. Error = Sales-FC, in the Minitab we use calculator to figure it out. After calculated the error, we can figure out the value of accuracy. MSD = S (Xt Ft)2/n MAD = S |Xt Ft| /n MPE = S |(Xt Ft)/Xt| /n And for this forecast the MSD=29.3526, MAD=4.69560, MPE=1.08963. As we all know for each forecast indicator, the lower value, the higher prediction accuracy. And usually we use the MPE to confirm the accuracy. Lets look at the MPE, the value of MPE is equal to 1.08963%, though the value of MPE is slightly higher than 1%, it close to 1%, the forecast is still accuracy. Reservation In this forecasting procedure we faced two choices, one is determined the seasonal structure of the time-series, determining whether the seasonal structure is additive or multiplicative. And the other one is to confirm the trend model, choosing the linear model or the quadratic model. The choice we make will affect the accuracy of forecasting. Additive or Multiplicative In this forecasting, we analysis the time-series as additive seasonal structure by using method below ¼Ã… ¡ Seasonal Structures: Q1 Q2 Q3 Q4 -13 30 22 -23 In this case ¼Ã…’for a given variable the quarter 1 is 13 units below trend, quarter 2 is 30 units above trend, etc. This is an Additive Seasonal Index. Alternatively we could have expressed the index as follows: Q1 Q2 Q3 Q4 -13% 30% 22% -23% Here the quarter 1 data is 13% below the trend value, or more conventionally 87% of trend, similarly for the other quarters. It is conventional to express this Seasonal Index as: Q1 Q2 Q3 Q4 87% 130% 122% 77% This is called a Multiplicative Seasonal Index and if the seasonal deviation is proportional to the trend then the seasonal structure is multiplicative. In this case we preferred the additive seasonal structure as the time-series constant about the trend, but in fact it could proportional to the trend and become the multiplicative seasonal structure in the future, so we should make appropriate adjustments base on the future data. Linear or Quadratic model In this case, we modeled the time-series as quadratic model due to the data the company provided closer to the quadratic model now, however, with the future data the model may be transformed into the linear model. Conclusion The objective of the company is to use time-series data to construct a forecast of the next four quarters sales. So as to do the forecast first we analysed the time-series to determine main characteristics of this time-series and modeled it, then found out the difference between the sales and trend to construct the seasonal indices, after that did the forecasting and to identify whether the forecast accurate or not by using the MAD, MSD and MPE. And the next four quarters sales of this company are Q1=366.116, Q2=406.796, Q3=400.011, Q4=365.637. However, during the forecasting procedure we should also consider about the choice we have made whether to choose additive or multiplicative, the linear model or the quadratic model will affect the accuracy of forecasting.

Dysfunctional Medical Insurance Essay -- Argumentative

Dysfunctional Medical Insurance A mom is waiting frantically in the local emergency room while her little girl continues to cry with pain. An hour goes by without any news as to when she will finally be treated. At last, their number gets called and they go over to the registration desk. The lady behind the desk opens their file and shakes her head in disbelief as she turns to look at yet another mother with remorse. She continues to tell the mother that the hospital has refused to provide medical attention to her little girl due to insurance problems. This scenario is sadly very common in the lower class families with little or no insurance coverage, in fact â€Å"20% of the U.S. population lacks medical coverage† (Richman). While the initial idea of medical insurance seems more beneficial than not, the current health insurance situation has caused many negative repercussions for both the patients and the physicians. Some of these disadvantages include: denial of health care, compromised medical att ention, astronomical billings, privacy issues, discriminatory plans, and even possible risks of fraud. The original idea of medical insurance should have been a noble way to help Americans afford medical bills in a case of an emergency or just routine physicals and check-ups. A lot of Americans coming from different financial situations could not afford the emergency or even the customary treatments and would therefore go without medical attention. This obviously had dire consequences on the patients’ health, thus forcing the need of an alternative option. â€Å"Fortunately, a handful of physicians associated with Sacramento's Sutter General Hospital saw beyond the despair. Seeing a need for an alternative health care financing vehicle in the early 1930s, the doctors created the first open enrollment hospital insurance plan in the United States† (Sutter Health). As originally planned, this new medical insurance proposed benefits to both the doctors and the patients; patients could afford to be treated, while doctors could see more patients-even of the lower income clas s. Somehow—between then and now, the plan has been twisted and turned around so much that it has lost some of its greatest benefits and reputation. Because of the numerous loop holes in the current insurance plan, I believe that right now the drawbacks and disadvantages are overshadowin... ...been hit with a half of billion dollars in claims. As the extensity of this crime allows, there are many other issues to be dealt with besides just the insurance aspect. However, if insurance wasn’t implemented into our society or even if loop holes didn’t exist in this case, then this crime could not have been possible. Since insurance was introduced to our society, there have been many problems. Much like the little girl who was denied medical attention, many people suffer from these disadvantages of medical insurance. I believe that the medical insurance idea is argumentative, because there are a lot of repercussions that people may or may not have thought about. Besides the recent crimes being committed against them, the insurance companies are benefiting immensely, but are we? The prices of medical treatments are rising, the doctor’s attention to actual patients as opposed to who has insurance is diminishing, and less people are benefiting from health insurance. I do not think that medical insurance should have been proposed in the first place. I do admit that it should have been a benefit to Americans, but I have yet to see everyone benefit as initially planned.

Analysis of Food Inc. Essay

Studies have shown that many people all over the world are unaware of where their food comes from. When an individual goes to consume a food product, he or she could be completely oblivious to the methods of manufacture, processing, packaging or transportation gone into the production of the food item. It is often said that ‘ignorance is bliss’ – perhaps this rings true in the case of food, its origins and its consumption as well. In such a scenario, eating well could seem like an unlikely prospect. The definition of ‘eating well’ in modern times seems to have gone from eating healthily, to eating ethically. The manner in which food is produced and consumed has changed more rapidly in the past fifty years than it has in the previous ten thousand years (Pollan and Schlosser, 2008). With this swift transformation, various ethical issues came to the fore. Food production is now done large scale in factories, rather than in farms. Mass production of various types of food, from crops and vegetables to seafood and meat, is very much the norm. The fact that food is mass produced nowadays is already something that a lot of people do not know about. The reason behind this is that food producing firms do not want the consumers – their customers – to know too much about the food manufacturing industry (Pollan and Schlosser, 2008), in the fear that customer loyalty could be lost upon their finding out various truths. To retain their customer base, according to documentary film ‘Food, Inc.’, narrated by Michael Pollan and Eric Schlosser, the image associated with food in th e United States of America is that of an American farmer. Various motifs plastered all over food packaging and advertisements for food products, such as green pastures for grazing cattle, picket fences, the typical farmhouse, vast meadows and, most importantly, the farmer, lead consumers to believe that their food still comes from farms, or at least a pastoral version of small time cottage industries. With these motifs constantly pervading the sensibilities of the average American consumer, it is little wonder that the consumer continues to ‘eat unethically’ – they are simply in the dark. Because what these motifs represent could not be further from the reality. The apparent crop central to all mass food production, as shown on ‘Food Inc’ (Pollan and Schlosser, 2008) and alluded to in Pollan’s book, ‘In The Omnivore’s Dilemma’ (2006), is corn. Corn is used in a vast assortment of ways in the food manufacturing industry. Bes ides, of course, being a food crop for direct consumption by humans, it is used to make a range of additives in processed food too, such as high fructose corn syrup, ascorbic acid, xanthan gum, et cetera. Corn is also a significant constituent of animal fodder, and is fed to almost all kinds of livestock. These include animals that are not meant, by evolution, to eat corn, such as cattle and fish (Pollan and Schlosser, 2008). The massive demand for corn is only counterbalanced by the massive supply of corn in the United States. This is due to the American government subsidising the cost of production of corn, encouraging corn farmers to produce more than the amount is truly required. Because of such heavy subsidies, corn becomes extremely cheap, produced at merely a fraction of its cost of production, and results in an enormous scale of production of corn. This manner of overproduction and consumption of corn alone raises a few ethical issues. First of all, the feeding of corn to cows and fish – not the natural food of such animals – causes immense problems to these animals, which could bring about serious repercussions to humankind as well. Take for instance, the feeding of corn to cows. Because corn is produced extremely cheaply, meat manufacturers are inclined to use corn as their choice of feed for their livestock, in order to cut down on the selling price of meat. Studies have shown that feeding corn to cows has brought about the emergence of a new, acid resistant strain of E.coli bacteria (Pollan and Schlosser, 2008). This, coupled with the terrible rearing conditions of the cows, causes the new strain of E.coli to get into the meat meant to be eventually sold. This strain of bacteria has proven to be dangerous, having claimed the lives of many people. Knowing this, the expected public reaction would be an outcry against the food manufacturing industry, demanding answers and greater, better checks of food producing companies. However, even such reactions may not yield any permanent solutions. According to ‘Food Inc.’, food regulatory bodies are being led by people from the very firms they are meant to regulate. This has appeared to cause certain food monitoring measures to become relaxed, such as a sharp decline in number of chec ks conducted by the Food and Drug Administration (FDA) of the United States, from 50 000 in 1972, to 9164 in 2006. One woman’s constant lobbying for better checks and regulation after her son, Kevin, passed away due to contaminated food brought about a ‘Kevin’s Law’, which, six years into her efforts, still had not been passed (Pollan and Schlosser, 2008). There is little surprise that the food produced nowadays is getting more and more dangerous for consumption. Given these circumstances, ‘eating well’ has become even more unlikely – the general public’s efforts to control the quality of their food gets constantly thwarted by powerful corporate and political institutions. Still, all does not seem to be lost. Some farmers are recognising the need to ‘de-industrialise’ the production of foodstuff. Michael Pollan’s ‘All Flesh is Grass’ talks about a ‘grass farmer’, Joel Salatin, who is a non industrial producer of food, and whose methods of production revolve around grass. (2006). ‘Big Organic’, another article by the same author, describes how food products in the whole foods aisle are properly farmed, as opposed to mass manufactured, therefore being processed or refined as little as possible. There are two downsides to be noted in both instances. For one, Joel Salatin produces foodstuff only for the local population, and expressly refuses to supply meat and other animal by-products from his relatively healthier farm animals all over the country. As a result, his ideas of rearing animals, as opposed to manufacturing them, by feeding them what they are meant to instead of cheaply obtained corn, are restricted to the borders of Swoope, Virginia (Pollan, 2008). On the other hand, to supply such products to various parts of the country, or the world, would fly in the face of the idea of sustainable food production practices. This presents quite a paradox. Another downside would be the added expense of consuming whole foods in the place of processed and mass produced food. One of the core reasons for choosing to malnourish animals by blanket feeding them corn, despite the negative implications, was the resultant driving down of cost of production of meat. This is how the average American consumer is able to put away two hundred pounds of meat every year (Pollan and Schlosser, 2008), otherwise such large quantities of meat may not be as easily produced. People nowadays have the option of buying meat and animal by-products derived from ‘freerange animals’ – referring to animals that are left to roam freely to feed, instead of restricting their movement in enclosures – for slightly more money. In economic terms, consumers seek products that minimises costs while maximising benefit. In this case, consumers are ostensibly unmoved by the prospect of consuming meat and other products from ethically raised animals, favouring instead, the cheaper, corn fed, mass produced alternatives. With this mentality to begin with, ethical eating habits would be difficult to foster. Completely giving up consumption of animal products and by-products entirely (i.e. becoming vegan) has becoming a rising phenomenon all over the world. It seems, to many vegan converts, to be the move that could galvanise the promotion of sustainable agriculture and animal welfare into action. However, according to an article on The Conversation, ‘Ordering the vegetarian meal? There’s more animal blood on your hands’, turning vegan, or even simply vegetarian, could be more detrimental than helpful (The Conversation, 2011). To provide the extra plants required to feed the changing diets of Australians alone would mean clearing native flora and fauna off arable land ‘the size of Victoria plus Tasmania’ (The Conversation, 2011) – already killing off a vast amount of animals and native plants to make way for plant based food. The above scenarios only serve to confuse the consumer even further. Most consumers do not have any way around purchasing food off the supermarket shelves that are, more often than not, tainted by ethical quandaries such as animal welfare issues etc. They also don’t exactly have the option of changing their diets to spare the lives of animals, as the result could be more damaging that the current situation. As such, an ostensible impasse seems to present itself regarding this issue. In my opinion, ‘eating well’ – ethically, and with as little animal blood on consumers’ hands – will never truly be viable in modern society. Bibliography: Pollan, M. (2006), ‘All Flesh is Grass’, In The Omnivore’s Dilemma, Penguin Press: New York, pp. 123-133 Pollan, M., Schlosser, E., 2008, ‘Food Inc.’, Available at: [Accessed 19th May, 2013] Pollan, M. (2006) ‘Big Organic’, In The Omnivore’s Dilemma, Penguin Press: New York, pp. 134-184. The Conversation, 2011, ‘Ordering the vegetarian meal? There’s more animal blood on your hands’ [online] Available at: [Accessed 19th May, 2013]

Estimate a Population Parameter

Estimation is a procedure by which a numerical value or values are assigned to a population parameter based on the information collected from a sample. The assignment of value(s) to a population parameter based on a value of the corresponding sample statistic is called estimation. In inferential statistics, _ is called the true population mean and p is called the true population proportion. There are many other population parameters, such as the median, mode, variance, and standard deviation.The following are a few examples of estimation: an auto company may want to estimate the mean fuel consumption for a particular model of a car; a manager may want to estimate the average time taken by new employees to learn a job; the U. S. Census Bureau may want to find the mean housing expenditure per month incurred by households; and the AWAH (Association of Wives of Alcoholic Husbands) may want to find the proportion (or percentage) of all husbands who are alcoholic.The examples about estimat ing the mean fuel consumption, estimating the average time taken to learn a job by new employees, and estimating the mean housing expenditure per month incurred by households are illustrations of estimating the true population mean. The example about estimating the proportion (or percentage) of all husbands who are alcoholic is an illustration of estimating the true population proportion, p.This article explains how to assign values to population parameters based on the values of sample statistics. For example, to estimate the mean time taken to learn a certain job by new employees, the manager will take a sample of new employees and record the time taken by each of these employees to learn the job. Using this information, he or she will calculate the sample mean, then, based on the value of he or she will assign certain values to _.As another example, to estimate the mean housing expenditure per month incurred by all households in the United States, the Census Bureau will take a sa mple of certain households, collect the information on the housing expenditure that each of these households incurs per month, and compute the value of the sample mean, Based on this value of the bureau will then assign values to the population mean, _. The sample statistic used to estimate a population parameter is called an estimator.The estimation procedure involves the following steps. 1. Select a sample. 2. Collect the required information from the members of the sample. 3. Calculate the value of the sample statistic. 4. Assign value(s) to the corresponding population parameter. Remember, the procedures to be mentioned above assume that the sample taken is a simple random sample. If the sample is not a simple random sample, then the procedures to be used to estimate a population mean or proportion become more complex. Estimate a Population Parameter Estimation is a procedure by which a numerical value or values are assigned to a population parameter based on the information collected from a sample. The assignment of value(s) to a population parameter based on a value of the corresponding sample statistic is called estimation. In inferential statistics, _ is called the true population mean and p is called the true population proportion. There are many other population parameters, such as the median, mode, variance, and standard deviation.The following are a few examples of estimation: an auto company may want to estimate the mean fuel consumption for a particular model of a car; a manager may want to estimate the average time taken by new employees to learn a job; the U. S. Census Bureau may want to find the mean housing expenditure per month incurred by households; and the AWAH (Association of Wives of Alcoholic Husbands) may want to find the proportion (or percentage) of all husbands who are alcoholic.The examples about estimat ing the mean fuel consumption, estimating the average time taken to learn a job by new employees, and estimating the mean housing expenditure per month incurred by households are illustrations of estimating the true population mean. The example about estimating the proportion (or percentage) of all husbands who are alcoholic is an illustration of estimating the true population proportion, p.This article explains how to assign values to population parameters based on the values of sample statistics. For example, to estimate the mean time taken to learn a certain job by new employees, the manager will take a sample of new employees and record the time taken by each of these employees to learn the job. Using this information, he or she will calculate the sample mean, then, based on the value of he or she will assign certain values to _.As another example, to estimate the mean housing expenditure per month incurred by all households in the United States, the Census Bureau will take a sa mple of certain households, collect the information on the housing expenditure that each of these households incurs per month, and compute the value of the sample mean, Based on this value of the bureau will then assign values to the population mean, _. The sample statistic used to estimate a population parameter is called an estimator.The estimation procedure involves the following steps. 1. Select a sample. 2. Collect the required information from the members of the sample. 3. Calculate the value of the sample statistic. 4. Assign value(s) to the corresponding population parameter. Remember, the procedures to be mentioned above assume that the sample taken is a simple random sample. If the sample is not a simple random sample, then the procedures to be used to estimate a population mean or proportion become more complex.