The Polio Vaccine free essay sample

The Polio epidemic happened, each of the 48 states at the time had rampant cases of Polio. The un-curable disease had taken over America. Poliomyelitis is an infectious viral disease that attacks the nerve cells and sometimes the central nervous system; it is caused by the destruction of nerve cells in the spinal cord. Polio often causes muscle wasting, paralysis, and even death. 1 In surveys of what Americans feared most, Polio came in second to the Atomic Bomb. Children were the main target of Polio and until Dr. Jonas Salk’s Polio Vaccine that became available in 1952, there was no cure for the disease Polio was often called infantile paralysis because the majority of the infected were elementary school children. â€Å"It must have been profoundly difficult in that first quarter-century of polio. How helpless parents must have felt to know that there was this killer that could come each summer, and that nothing they could do could safeguard their children. We will write a custom essay sample on The Polio Vaccine or any similar topic specifically for you Do Not WasteYour Time HIRE WRITER Only 13.90 / page Every sniffle, every cold, every muscle cramp, every temper tantrum that a child exhibited in the long, hot days of summer and early autumn were potential symptoms of polio. How long could a family show good spirits in front of a child confined to an iron lung, or later, during the two or more years a child might spend in rehabilitative therapy. † This quote is really significant in the fear that polio had over people. Every parent had no way to defend their kids from the disease. Sending children to school was extremely difficult because many schools were shut down from massive outbreaks of polio. Not only schools but also every other public place; restaurants, grocery store, movie theaters, etc. No one would leave the comfort of their own home, and even then they weren’t safe from Polio, they were just safer there than anywhere else. 2 A very notable case of Polio was Franklin D. Roosevelt. Even though it was children who were especially vulnerable, Adults could catch the disease as well. In 1921, FDR had contracted Polio and he became paralyzed from the waist down for the rest of his life. Later on in 1937, Roosevelt advocated for The National Foundation for Infantile Paralysis. Roosevelt’s foundation established The March of Dimes that helped to fund money to find a cure and resist the disease. March of Dimes was really successful in raising money to help fight against the disease. 3 Polio quickly moved from city to city taking no pity on any of its victims. The disease got stronger among the nation. It became surreal-like as demonstrated by this quote by Richard Aldrich M. D. , â€Å"The first summer when I was home in Minnesota was that gosh-awful polio epidemic they had there. We admitted 464 proven cases of polio just at the University Hospital, which is unbelievable. And this was a very severe paralytic form. Maybe two or three hours after a lot of these kids would come in with a stiff neck or a fever, theyd be dead. It was unbelievable. It was just loads of people that came in, sometimes with only a fever but usually a headache and a little stiffness in the neck. And just absolutely terrified. At the height of the epidemic, the people in Minneapolis were so frightened that there was nobody in the restaurants. There was practically no traffic, the stores were empty. It just was considered a feat of bravado almost to go out and mingle in public. A lot of people just took up and moved away, went to another city. †4 What really frustrated people is that there really wasn’t anything they could do. As previously stated, Hospitals were pass there limit of people that could be emitted into the hospital. They couldn’t just turn them away, so they took in everyone who came through the door. This was problematic because the Doctor-Patient ratio was very unbalanced. Not everyone was able to get attention right away and within a couple hours, the disease completely overtook them and they were dead. Every year more and more cases of paralytic polio were occurring. In 1933 there were only 5000 cases and ten years later in 1943, the number of cases was more than double with 12,000 cases. In 1950 there was 33,000 cases. Polio seemed to be moving north to east. Polio shifted from the eastern side of North America and began making its way across the entire United States. Then in 1952 the Polio Epidemic occurred with 59,000 proven cases of paralytic polio. 5 By then, polio epidemics were second only to the atomic bomb in surveys of what Americans feared most. Bomb and virus alike were terrible agents of destruction that might arrive at any moment to devastate a family, a community, or an entire nation. The disease seemed like an omnipresent threat, and its cure became a national responsibility. Epidemics struck other countries, but never as heavily as here. America was the center of polio, and the place where people knew they must work first, and fastest, to end it. They gave their time and money to help the growing swell of victims and to find a way to stem the rising tide of injury. When the call came, they even volunteered their children, millions of them, to test a new vaccine. The fear that had once driven Americans apart was now the force that pulled them together. This quote defines the 1952 Polio Epidemic and the strong fear that Americans had. The worst hit areas to date were Texas, Iowa, Ohio, Nebraska, and the worst were in New York, Pennsylvania, and Massachusets. 7 The Most significant figure during this time was Dr. Jonas Salk. Jonas Salk was born October 28, 1914 in New York City. He was the eldest son of Russian immigrant parents. His parents, like many immigrants of the time, were uneducated, but determined that their sons should have formal educations and achieve American success. Salk attended Townsend Harris High School, one of the finest public high schools in New York. He became the first member of his family to go to college. As a medical student and later a researcher at the University of Michigan, Salk studied viruses, such as influenza, and ways to vaccinate against them. In 1947, Salk became head of the Virus Research Lab at the University of Pittsburgh. He began investigating the polio virus. 8 On July 2, 1952, Salk tried a refined vaccine on children whod already had polio and recovered. After the vaccination, their antibodies increased. He then tried it on volunteers who had not had polio, including himself, his wife, and their children. The volunteers all produced antibodies, and none received polio. One of the newest and most hopeful weapons in the fight against polio is a blood fraction called gamma globulin. It is a rich storehouse of the disease fighting antibodies. Also, injections of this would reduce measles in children. There has been evidence that gamma globulin could also destroy polio before it reaches the nervous system, disrupting its paralyzing effects. 6 In 1953, Salk reported his findings in The Journal of the American Medical Association. A nationwide testing of the vaccine was launched in April 1954 with the mass inoculation of school children. Before the polio vaccine, 13,000 to 20,000 people were paralyzed by polio. About 1,000 people died from it each year in the United States alone. The results of Salk’s vaccine were a 60 -70 percent prevention of Polio. The public was ecstatic with Salk’s marvelous creation. It was the gift that the American Public waited for years and years to get. Because of the Vaccine, Many universities awarded Salk honorary degrees, he was given a Congressional Gold Medal, and France made him a member of the Legion of Honor. â€Å"Fifty years ago, on April 12, 1955, the world heard one of the most eagerly anticipated announcements in medical history: Dr. Jonas Salks polio vaccine worked. The vaccine turned a disease that once horrified America into a memory. † There was one incident that took place with Salk’s vaccine that put American’s hope in the dumpsters. About 200 cases of the disease were caused by Salk’s vaccine and 11 people died. All testing was halted because the American people didn’t feel safe anymore. Investigators found that the disease-causing vaccine all came from one poorly made batch at one drug-company. This resulted in higher production standards that were adopted and vaccinations resumed. By August of 1955 four million vaccinations were given and the impact was dramatic. In 1955, there were 28,985 cases of polio. In 1956, there were 14,647 cases. In 1957, there were 5,894 cases. The number of cases kept declining with the number of vaccinations given, and by 1959, 90 other countries used Salks vaccine. Although the last known case of polio in the Western hemisphere was reported in 1991 and there has not been a case in Western Asia since 1997, polio is still rampant in South Asia, West Africa, and Central Africa. Approximately 5,000 cases are diagnosed each year. Although that represents a 90% decrease in just the last ten years.

The Hindenburg Disaster

The Hindenburg Disaster The Hindenburg marked the beginning and the end of transatlantic airships. This 804-foot dirigible filled with over 7 million cubic feet of hydrogen was a crowning achievement of its age. Never before or since has a larger aircraft taken flight. However, the explosion of the Hindenburg changed the landscape for lighter-than-air crafts forever. The Hindenburg is Engulfed in Flames   On May 6, 1937, the Hindenburg carrying 61 crew and 36 passengers arrived hours behind schedule at Lakehurst Naval Air Station in New Jersey. Inclement weather forced this delay. Buffeted by winds and rain, the craft hovered in the area by most accounts for about an hour. The presence of lightning storms were recorded. The landing of the Hindenburg with these types of conditions was against regulations. However, by the time the Hindenburg began its landing, the weather was clearing up. The Hindenburg seems to have been traveling at a fairly fast speed for its landing and for some reason, the Captain attempted a high landing, being winched to the ground from a height of about 200 feet. Soon after the mooring lines were set, some eyewitnesses reported a blue glow on top of the Hindenburg followed by a flame towards the tail section of the craft. The flame was almost simultaneously succeeded by an explosion that quickly engulfed the craft causing it to crash into the ground killing 36 p eople. Spectators watched in horror as passengers and crew were burned alive or jumped to their deaths. As Herb Morrison announced for the radio, Its burst into flames.... Get out of the way, please, oh my, this is terrible...Oh, the humanity and all the passengers. The day after this horrible tragedy occurred, the papers started speculating about the cause of the disaster. Up until this incident, the German Zeppelins had been safe and highly successful. Many theories were talked about and investigated: sabotage, mechanical failure, hydrogen explosions, lightning or even the possibility that it was shot from the sky. On the next page, discover the major theories of what happened on this fateful day in May.   The Commerce Department and the Navy led the investigations into the Hindenburg disaster. However, the Federal Bureau of Investigation also looked into the matter even though it technically had no jurisdiction. President FDR had asked all governmental agencies to cooperate in the investigation. The FBI files released about the incident through the Freedom of Information Act are available online. You must download Adobe Acrobat to read the files. Theories of Sabotage The theories of sabotage began to surface immediately. People believed that maybe the Hindenburg had been sabotaged to harm Hitlers Nazi regime. The sabotage theories centered on a bomb of some sort being placed aboard the Hindenburg and later detonated or some other sort of sabotage performed by someone on board. Commander Rosendahl of the Department of Commerce believed that sabotage was the culprit. (See p. 98 of Part I of the FBI documents.) According to a Memorandum to the Director of the FBI dated May 11, 1937, when Captain Anton Wittemann, the third in command of the Hindenburg, was questioned after the tragedy he said that Captain Max Pruss, Captain Ernst Lehmann and he had been warned of a possible incident. He was told by the FBI Special Agents not to speak of the warning to anyone. (See p. 80 of Part I of the FBI documents.) There is no indication that his claims were ever looked into, and no other evidence arose to support the idea of sabotage. Possible Mechanical Failure Some people pointed to a possible mechanical failure. Many of the ground crew later interviewed in the investigation indicated that the Hindenburg was coming in too fast. They believed that the airship was thrown into a full reverse to slow the craft. (See p. 43 of Part I of the FBI documents.) The speculation arose that this may have caused a mechanical failure which sparked a fire causing the hydrogen to explode. This theory is supported by the fire at the tail section of the craft but not much else. The Zeppelins had a great track record, and there is little other evidence to support this speculation. Was It Shot From the Sky?   The next theory, and probably the most outlandish, involves the dirigible being shot from the sky. The investigation focused on reports of a pair of tracks found near the back of the airfield in a restricted area. However, there were numerous people on hand to watch the amazing event of the Hindenburg landing so these footprints could have been made by anyone. In fact, the Navy had caught a couple of boys who had sneaked into the airfield from that direction. There were also reports of farmers shooting at other dirigibles because they passed over their farms. Some people even claimed that joy seekers shot down the Hindenburg. (See p. 80 of Part I of the FBI documents.) Most people dismissed these accusations as nonsense, and the formal investigation never substantiated the theory that the Hindenburg was shot from the sky. Hydrogen and the Hindenburg Explosion The theory that gained the most popularity and became the most widely accepted involved the hydrogen on the Hindenburg. Hydrogen is a highly flammable gas, and most people believed that something caused the hydrogen to spark, thus causing the explosion and fire. At the beginning of the investigation, the idea arose that the drop lines carried static electricity back up to the airship which caused the explosion. However, the chief of the ground crew denied this claim by the fact that the mooring lines were not conductors of static electricity. (See p. 39 of Part I of the FBI documents.) More credible was the idea that the blue arc seen at the tail of the airship just before it burst into flames was lightning and caused the detonation of the hydrogen. This theory was substantiated by the presence of the lightning storms reported in the area. The hydrogen explosion theory became accepted as the reason for the explosion and led to the end of commercial lighter-than-air flight and the stalling of hydrogen as a reliable fuel. Many people pointed to the flammability of the hydrogen and questioned why helium was not used in the craft. It is interesting to note that a similar event happened to a helium dirigible the year before. So what really caused the end of the Hindenburg? Addison Bain, a retired NASA engineer and hydrogen expert, believes he has the correct answer. He states that while hydrogen might have contributed to the fire, it was not the culprit. To prove this, he points to several pieces of evidence: The Hindenburg did not explode but burned in numerous directions.The airship remained afloat for several seconds after the fire began. Some people report it did not crash for 32 seconds.Fabric pieces fell to the ground on fire.The fire was not characteristic of a hydrogen fire. In fact, hydrogen makes no visible flames.There were no reported leaks; the hydrogen was laced with garlic to give off an odor for easy detection. After years of exhaustive traveling and research, Bain uncovered what he believes is the answer to the Hindenburg mystery. His research shows that the Hindenburgs skin was covered with the extremely flammable cellulose nitrate or cellulose acetate, added to help with rigidity and aerodynamics. The skin was also coated with flecks of aluminum, a component of rocket fuel, to reflect sunlight and keep the hydrogen from heating and expanding. It had the further benefit of combating wear and tear from the elements. Bain claims these substances, although necessary at the time of construction, directly led to the disaster of the Hindenburg. The substances caught fire from an electric spark that caused the skin to burn. At this point, the hydrogen became the fuel to the already existing fire. Therefore, the real culprit was the skin of the dirigible. The ironic point to this story is that the German Zeppelin makers knew this back in 1937. A handwritten letter in the Zeppelin Archive states, The actual cause of the fire was the extreme easy flammability of the covering material brought about by discharges of an electrostatic nature.

Saving Our Public Schools Assignment Example | Topics and Well Written Essays - 500 words

Saving Our Public Schools - Assignment Example She believes that social reform is the key to solving most of the problems in the public school system, as they are a direct result of poverty and racial discrimination. Only through initiating policies that are combat poverty and segregation, could the public school system improve. She is also against the use of grades to assess teachers, proposing that their peers and principals should do the assessment. Testing of the students should also be for assessing their strengths and weaknesses and not for ranking purposes. She also recognizes the need to standardize resource allocation to be more equitable across the board as a key issue towards the improvement of public schooling. The privatization of public schooling, in Ms Ravitch’s opinion, will lead to a dual system, where those who can afford to pay for private schooling get into better schools and the poor in the society will be in mediocre schools. IN the interests of democracy, this should not happen. Ms Ravitch mainly uses a cause-and –effect organizational pattern in her essay. This features prominently throughout this text. When she links the move to privatize the public school system to the creation of a dual education system, which segregates the rich, and the poor, which further leads towards affecting the very fabric of the American democratic system, which is equality. This is one of the many examples of a multi-tier cause and effect organizational structures present in the essay. One of the uses of prepositional phrases, is evidenced in the sentence ‘They are being used by those who have an implacable hostility towards the public sector’, in this sentence the preposition is ‘towards’ which uses the modifiers ‘implacable hostility’ to show, effectively, the opinions of the move for privatizations towards the public.. Ms. Ravitch switches voice effectively between the first person and

How the Evian conference allowed hitler to massacre the jews Research Paper

How the Evian conference allowed hitler to massacre the jews - Research Paper Example The urgency of the refugee problem was graphically portrayed in the case of four hundred refugees from Austria who drifted for several weeks on a barge in the Danube: â€Å"Although they were within sight of three frontiers, they could go back neither to the country from which they were driven out nor land at any foreign port. (They were) people without a country, human flotsam adrift on an international stream.†1It was evident that an unprecedented, immense humanitarian crisis faced the world. U.S. President F.D. Roosevelt called for an international conference to address the plight of refugees fleeing Nazi persecution. The resulting Intergovernmental Conference on Political Refugees was held in Evian-les-Bains in Southern France, opening on July 6, 1938. The Evian Conference’s preliminaries, the refusal of the participating nations to ease visa restrictions and the results are proof of the multi-national anti-Semitism which provided Hitler with complete impunity for a vision of a world free of the â€Å"Jewish Vermin.† The Conference’s preliminaries displayed the underlying anti-Semitism in world society. America suggested Switzerland as the venue but was turned down by the Swiss who feared German displeasure. The official participants of the Conference were Argentina, Australia, Belgium, Bolivia, Brazil, United Kingdom, Chile, Canada, Colombia, Costa Rica, Cuba, Denmark, Dominican Republic, Ecuador, France, Guatemala, Haiti, Ireland, Honduras, Mexico, the Netherlands, New Zealand, Nicaragua, Norway, Panama, Paraguay, Peru, Sweden, Switzerland, the USA, Uruguay and Venezuela. Poland and Romania attend unofficially, while South Africa was an observer.2 These nations agreed to participate only on the understanding that they would not be asked to increase their quota of refugees – they would only be called upon to offer solutions to the refugee problem. In the first instance, Great Britain and France collaborated to ensure that the mandate of the new body extended only to refugees from Germany and Austria, excluding any refugees from Rumania, Italy, Poland, Hungary and Spain.3 Again, at the very outset, Britain made it clear that any notion of large-scale settlement in Palestine would not be acceptable. This stand reflected the British policy of appeasement of the Arabs, in order to prevent uprisings against Jewish immigration. In fact, the British representative, Lord Winterton, deliberately avoided all references to Palestine in his opening address. Earlier, he had assured the British foreign office that â€Å"he and the British delegates would bear in mind the need to avoid provoking the Reich government.†4Australia held that Jews could not be culturally assimilated into their county and attended only to avoid international criticism. Canada attended the Conference with great reluctance, fearing being pressurized into admitting Jews. Canada’s anti-Semitic sentiment was amply demonstrated in the reply of a senior official to the question of how many Jews would be allowed into the country after WWII: â€Å"None would be too many.†5Switzerland sent its Police Chief, Dr. H. Rothmund, as its delegate, clearly conveying its intention of doing nothing for the Jews. In the words of a renowned journalist, â€Å"I doubt if much will be done.   The British, French and Americans seem too anxious not to do anything to offend Hitler.   It's an absurd situation.   They want to appease the man who was responsible for their problem.†

The Clostridium Difficile Infections Biology Essay

The Clostridium Difficile Infections Biology Essay The organism known as Clostridium difficile is a gram-positive bacillus bacteria which has the ability to form spores, as well as produce a number of toxins. The toxins produced by these bacteria are presently considered to be one of the forefront causes of antibiotic-associated diarrhea (AAD).In addition, infection of this bacteria and the subsequent damage which is instigated by the organisms invasive toxins can lead to several serious gastrointestinal conditions including pseudomembranous colitis (PHAC, 2011). According to the Centre for Disease Control (2012), Clostridium difficile is proposed to be the causative of between 15 and 25% of all AAD cases in Canada. Due to its specific pathogenesis, this organism is easily spread throughout a given population, with increased risk attributed to various factors which contribute to a higher level of exposure. Given this, there are often outbreaks experienced within healthcare facilities, as well as within community settings. In addition , the organism has well known epidemiology, with certain patient attributes, exposure to high-risk environment, medical conditions and various medications contributing to an increased risk of both the asymptomatic Clostridium difficile colonization (CDC) or the symptomatic and sometimes deadly Clostridium difficile infection (CDI). Infection by Clostridium difficile can also lead to various chronic and adverse effects after the initial recovery such as recurrent infections, surgeries being required to rectify the damage which has been caused by the toxins effect on the patients bowels (3). As a result of this persistent organisms observable damage and tendency to spread, any sort of CDI outbreak has definite implications on the healthcare system, both from a fiscal as well as a resource and time-allocation standpoint. CLOSTRIDIUM DIFFICILE Pathogenesis Clostridium difficile (C. difficile) are gram positive, spore-forming bacillus bacteria which, as an opportunistic pathogen, inhabit the anaerobic conditions of the human gastrointestinal system. It is also the leading cause of health care-associated diarrhea (Bourgault, 2011). As reported in the Canadian Medical Association Journal (CMAJ), Clostridium difficile can be isolated from the stool of 3% of healthy adults and up to 80% of healthy newborns and infants (Kujiper, 2008). The reason that it can be so detrimental in the case of an infection is that along with a number of other virulence factors, it produces two toxins, known as toxin A and toxin B (CDC, 2012). In patients who display either a colonization or infection, the normal gastrointestinal flora is depleted due to a number of extenuating risk factors. Provided with these circumstances, the C. difficile bacteria are able to flourish and overrun the patients bowel. The major agressins (Borriello, 1998) of C. difficile are u ndoubtedly toxins A and B, however, there are a number of other virulence factors possessed by the organism which contribute to its potential to cause harm. According to Borriello, C.difficile is influenced by its ability to adhere the intestinal wall, which may be caused by the organisms intrinsic slight positive net charge. This attracts to the negatively charged host cells [and] may contribute to gut colonization (Borriello, 1998). Both toxins A and B are cytotoxic to a very large number of different cell types, both cause increased vascular permeability, and both cause haemorrhage (Borriello, 1998). In addition, toxin A appears to cause fluid accumulation, whereas toxin B does not (Borriello, 1998). Clinical Features Immediate clinical symptoms of C. difficile can include fever, loss of appetite, nausea, abdominal pain and tenderness (PHAC, 2011) as well as watery diarrhea. The diarrhea is a by-product of the toxins produced by the multiplying bacteria as they invade the mucosa of the intestines. This causes profuse inflammatory diarrhea secondary to destruction of the lining of the colon (4). In more severe cases, it can cause pseudomembranous colitis, bowel perforation, sepsis, and even death (PHAC, 2011). Diagnostic Methods There are currently several reliable, widely-used laboratory tests which are used in the diagnosis of C. difficile colonization and infection. Microbiological stool culture is the most sensitive test available (CDC, 2012) and is considered the confirmatory test, but it also carries the highest incidence of false-positives. This occurs when the patient is infected with a non-toxigenic strain of C. difficile. PCR assays have been developed for the gene which encodes for toxin B. In addition, antigen detection by either latex agglutination or immunochromographic assays (CDC, 2012) provides a fast way to detect the presence of Clostridium difficile. Again, it is non-specific for toxigenicity. Toxin testing tests for specificity to toxin B, while enzyme immunoassays can detect either toxin (CDC, 2012). As studied by Kinson in 2009, additional testing for various markers is also being investigated as a means of detecting infections. Examples of this include fecal lactoferrin, a marker for intestinal inflammation (Kinson, 2009) as well as glutamate dehydrogenase (GDH), which is C. difficile-specific [à ¢Ã¢â€š ¬Ã‚ ¦] however GDH positivity is independent of toxigenicity in strains of C. difficile (Kinson, 2009). Although its presence does confirm Clostridium difficile is present in the patient, it does not confirm that the strain present in this patient is toxigenic. Therapeutics According to the Public Health Agency of Canada, mild cases of CDI can resolve with only supportive treatment such as intravenous fluids to combat dehydration (PHAC, 2011). Additionally, the Centre for Disease Control states that up to 20% of cases will resolve within two to three days of discontinuing the antibiotic to which the patient was previously exposed (CDC, 2012). In more severe cases, the infection can usually be treated with an appropriate course (about 10 days) of antibiotics, including metronidazole, vancomycin (administered orally), or recently approved fidaxomicin (Aylin, 2011). If the bacteria have severely damaged sections of the bowel, it may have to be removed surgically as well (Louie, 2004). EPIDEMIOLOGY Risk Factors for Infection The incidence of infection by Clostridium difficile is affected by a number of risk factors, which is depicted in Figure 1 (Owens, 2008). Being hospitalized greatly increases the chances of becoming infected with C. difficile. These bacteria are shed in the feces, and are usually transmitted between patients either by healthcare workers, or by surfaces or equipment not being fully sanitized between patients (Louie, 2004). However, there has been an increasing trend of community-acquired infections as well. In a study performed at Harvard Medical School, it was found that community-acquired Clostridium difficile infection may account for more than a third of Clostridium difficile-associated diarrhea overall (Leffler, 2012). In addition, the use of medications such as antibiotics, particularly fluoroquinolones (Bourgault, 2011), as well as proton pump inhibitors (used to supress production of gastric acid in gastrointestinal conditions) have been shown to increase the risk of a Clostri dium difficile infection. In a study by Haider et al, it was shown that while the use of proton pump inhibitors appears to lead to an elevated risk of developing severe CDI (Haider, 2012), another widely used type of gastric acid suppressant medication known as histamine 2 receptor antagonist (H2RA) actually appears to decrease the risk of an infection (Haider, 2012). Gastrointestinal surgery is also a known risk factor for severe infection with Clostridium difficile (Louie, 2004). According to Public Health Ontario, infections are more likely to be considered severe in an elderly or immunocompromised patient (OAHPP, 2011). However, it has been shown that the presence of multiple medical conditions, or co-morbidity, is actually a better predictor then age as to the outcome of the infection. Severe CDI occurs more frequently with advancing age. However, age, per se, has no effect on mortality (Dharmarajan, 2000). IMPLICATIONS IN PUBLIC HEALTH Resource Allocation It has been shown that both the financial implications, as well as the allocation of resources within the health care system produced by Clostridium difficile-associated disease (CDAD) are quite significant. Public Health Ontario stated at the time of their study in 2010, that the cost of CDI readmissions alone is estimated to be a minimum of CAD $128,200 per year per hospital (OAHPP, 2011). A more extensive look in to the associative costs was completed in 2008 at Washington Universitys School of Medicine. Dubberke studied a population of CDAD patients and proposed that a cost of $2454 was attributed to each case of CDAD, with that cost increasing to $5042 per patient if their stay exceeded 180 days of hospitalization (Dubberke, 2008). According to the study conducted in by Dr. Forster et al (2011), an infection with C. difficile extends the patients hospital stay from an average of 8 days to an average of 34 days (Forster, 2011). This not only increases the burden on health care wo rkers, but also utilizes time and supplies which are quite preventable. CONCLUSION

Simple Linear Regression

Simple linear regression is the statistic method used to make summary of and provide the association between variables that are continues and quantitative ,basically it deals with two measures that describes how strong the linear relationship we can compute in data .Simple linear regression consist of one variable known as the predictor variable and the other variable denote y known as response variable . It is expected that when we talk of simple linear regression to touch on deterministic relationship and statistical relationship, the concept of least mean square .the interpretation of the b0 and b1 that they are used to interpret the estimate regression . There is also what is known as the population regression line and the estimate regression line . This linearity is measured using the correlation coefficient (r), that can be -1,0,1.The strength of the association is determined from the value of r .( https://onlinecourses.science.psu.edu/stat501/node/250). History of simple linear regression Karl Pearson established a demanding treatment of Applied statistical measure known as Pearson Product Moment Correlation . This come from the thought of Sir Francis Galton ,who had the idea of the modern notions of correlation and regression ,Sir Galton contributed in science of Biology ,psychology and Applied statistics . It was seen that Sir Galton is fascinated with genetics and heredity provided the initial inspiration that led to regression and Pearson Product Moment Correlation . The thought that encouraged the advance of the Pearson Product Moment Correlation began with vexing problem of heredity to understand how closely features of generation of living things exhibited in the next generation. Sir Galton took the approach of using the sweet pea to check the characteristic similarities. ( Bravais, A. (1846). The use of sweet pea was motivated by the fact that it is self- fertilize ,daughter plants shows differences in genetics from mother with-out the use of the second parent that will lead to statistical problem of assessing the genetic combination for both parents .The first insight came about regression came from two dimensional diagram plotting the size independent being the mother peas and the dependent being the daughter peas. He used this representation of data to show what statisticians call it regression today ,from his plot he realised that the median weight of daughter seeds from a particular size of mother seed approximately described a straight line with positive slope less than 1. â€Å"Thus he naturally reached a straight regression line ,and the constant variability for all arrays of character for a given character of second .It was ,perhaps best for the progress of the correlational calculus that this simple special case should promulgated first .It so simply grabbed by the beginner (Pearson 1930,p.5). Then it was later generalised to more complex way that is called the multiple regression. Galton, F. (1894),Importance of linear regressionStatistics usually uses the term linear regression in interpretation of data association of a particular survey, research and experiment .The linear relationship is used in modelling .The modelling of one explanatory variable x and response variable y will require the use of simple linear regression approach . The simple linear regression is said to be broadly useful in methodology and the practical application. This method on simple linear regression model is not used in statistics only but it is applied in many biological, social science and environmental research. The simple linear regression is worth importance because it gives indication of what is to be expected, mostly in monitoring and amendable purposes involved on some disciplines(April 20, 2011 , plaza ,). Description of linear regression The simple linear regression model is described by Y=(?0 + ?1 +E), this is the mathematical way of showing the simple linear regression with labelled x and y .This equation gives us a clear idea on how x is associated to y, there is also an error term shown by E. The term E is used to justification for inconsistency in y, that we can be able to detect it by the use of linear regression to give us the amount of association of the two variables x and y . Then we have the parameters that are use to represent the population (?0 + ?1x) .We then have the model given by E(y)= (?0 + ?1x), the ?0 being the intercept and ?1 being the slope of y ,the mean of y at the x values is E(y) . The hypothesis is assumed is we assume that there is a linear association between the two variables ,that being our H0 and H1 we assume that there is no linear relationship between H0 and H1. Background of simple linear regression Galton used descriptive statistics in order for him to be able to generalise his work of different heredity problems . The needed opportunity to conclude the process of analysing these data, he realised that if the degree of association between variables was held constant,then the slope of the regression line could be described if variability of the two measure were known . Galton assumed he estimated a single heredity constant that was generalised to multiple inherited characteristics . He was wondering why, if such a constant existed ,the observed slopes in the plot of parent child varied too much over these characteristics .He realise variation in variability amongst the generations, he attained at the idea that the variation in regression slope he obtained were solely due to variation in variability between the various set of measurements . In resent terms ,the principal this principal can be illustrated by assuming a constant correlation coefficient but varying the standard deviations of the two variables involved . On his plot he found out that the correlation in each data set. He then observe three data sets ,on data set one he realised that the standard deviation of Y is the same as that of X , on data set two standard deviation of Y is less than that of X ,third data set standard deviation of Y is great than that of X . The correlation remain constant for three sets of data even though the slope of the line changes as an outcome of the differences in variability between the two variables.The rudimentary regression equation y=r(Sy / Sx)x to describe the relationship between his paired variables .He the used an estimated value of r , because he had no knowledge of calculating it The (Sy /Sx) expression was a correction factor that helped to adjust the slope according to the variability of measures . He also realised that the ratio of variability of the two measures was the key factor in determining the slope of the regression line .The uses of simple linear regression Simple linear regression is a typical Statistical Data Analysis strategy. It is utilized to decide the degree to which there is a direct connection between a needy variable and at least one free factors. (e.g. 0-100 test score) and the free variable(s) can be estimated on either an all out (e.g. male versus female) or consistent estimation scale. There are a few different suppositions that the information must full fill keeping in mind the end goal to meet all requirements for simple linear regression. Basic linear regression is like connection in that the reason for existing is to scale to what degree there is a direct connection between two factors. The real contrast between the two is that relationship sees no difference amongst the two variables . Specifically, the reason for simple linear regression â€Å"anticipate† the estimation of the reliant variable in light of the estimations of at least one free factors. https://www.statisticallysignificantconsulting.com/RegressionAnalysis.htm ReferenceBravais, A. (1846), â€Å"Analyse Mathematique sur les Probabilites des Erreurs de Situation d'un Point,† Memoires par divers Savans, 9, 255-332.Duke, J. D. (1978),â€Å"Tables to Help Students Grasp Size Differences in Simple Correlations,† Teaching of Psychology, 5, 219-221.FitzPatrick, P. J. (1960),â€Å"Leading British Statisticians of the Nineteenth Century,† Journal of the American Statistical Association, 55, 38-70.Galton, F. (1894),Natural Inheritance (5th ed.), New York: Macmillan and Company.https://onlinecourses.science.psu.edu/stat501/node/250.https://www.statisticallysignificantconsulting.com/RegressionAnalysis.htmGhiselli, E. E. (1981),Measurement Theory for the Behavioral Sciences, San Francisco: W. H. Freeman.Goldstein, M. D., and Strube, M. J. (1995), â€Å"Understanding Correlations: Two Computer Exercises,† Teaching of Psychology, 22, 205-206.Karylowski, J. (1985),â€Å"Regression Toward the Mean Effect: No Statistical Backgrou nd Required,† Teaching of Psychology, 12, 229-230.Paul, D. B. (1995),Controlling Human Heredity, 1865 to the Present, Atlantic Highlands, N.J.: Humanities Press.Pearson, E. S. (1938),Mathematical Statistics and Data Analysis (2nd ed.), Belmont, CA: Duxbury.Pearson, K. (1896),â€Å"Mathematical Contributions to the Theory of Evolution. III. Regression, Heredity and Panmixia,† Philosophical Transactions of the Royal Society of London, 187, 253-318.Pearson, K. (1922),Francis Galton: A Centenary Appreciation, Cambridge University Press.Pearson, K. (1930),The Life, Letters and Labors of Francis Galton, Cambridge University Press.Williams, R. H. (1975), â€Å"A New Method for Teaching Multiple Regression to Behavioral Science Students,† Teaching of Psychology, 2, 76-78. Simple Linear Regression Stat 326 – Introduction to Business Statistics II Review – Stat 226 Spring 2013 Stat 326 (Spring 2013) Introduction to Business Statistics II 1 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 2 / 47 Review: Inference for Regression Example: Real Estate, Tampa Palms, Florida Goal: Predict sale price of residential property based on the appraised value of the property Data: sale price and total appraised value of 92 residential properties in Tampa Palms, Florida 1000 900 Sale Price (in Thousands of Dollars) 800 700 600 500 400 300 200 100 0 0 100 200 300 400 500 600 700 800 900 1000 Appraised Value (in Thousands of Dollars)Review: Inference for Regression We can describe the relationship between x and y using a simple linear regression model of the form  µy = ? 0 + ? 1 x 1000 900 Sale Price (in Thousands of Dollars) 800 700 600 500 400 300 200 100 0 0 100 200 300 400 500 600 700 800 900 1000 Appraised Value (in Thousands of Dollars) response variable y : sale price explanatory variable x: appraised value relationship between x and y : linear strong positive We can estimate the simple linear regression model using Least Squares (LS) yielding the following LS regression line: y = 20. 94 + 1. 069x Stat 326 (Spring 2013) Introduction to Business Statistics II / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 4 / 47 Review: Inference for Regression Interpretation of estimated intercept b0 : corresponds to the predicted value of y , i. e. y , when x = 0 Review: Inference for Regression Interpretation of estimated slope b1 : corresponds to the change in y for a unit increase in x: when x increases by 1 unit y will increase by the value of b1 interpretation of b0 is not always meaningful (when x cannot take values close to or equal to zero) here b0 = 20. 94: when a property is appraised at zero value the predicted sales price is $20,940 — meaningful?!Stat 326 (Spring 2013) Introduction to Business Statistics II 5 / 47 b1 < 0: y decreases as x increases (negative association) b1 > 0: y increases as x increases (positive association) here b1 = 1. 069: when the appraised value of a property increases by 1 unit, i. e. by $1,000, the predicted sale price will increase by $1,069. Stat 326 (Spring 2013) Introduction to Business Statistics II 6 / 47 Review: Inference for Regression Measuring strength and adequacy of a linear relationship correlation coe? cient r : measure of strength of linear relationship ? 1 ? r ? 1 here: r = 0. 9723 Review: Inference for RegressionPopulation regression line Recall from Stat 226 Population regression line The regression model that we assume to hold true for the entire population is the so-called population regression line where  µy = ? 0 + ? 1 x, coe? cient of determination r 2 : amount of variation in y explained by the ? tted linear model 0 ? r2 ? 1 here: r 2 = (0. 9723)2 = 0. 9453 ? 94. 53% of the variation in the sale price can be explained through the line ar relationship between the appraised value (x) and the sale price (y ) Stat 326 (Spring 2013) Introduction to Business Statistics II 7 / 47  µy — average (mean) value of y in population for ? xed value of x ? — population intercept ? 1 — population slope The population regression line could only be obtained if we had information on all individuals in the population. Stat 326 (Spring 2013) Introduction to Business Statistics II 8 / 47 Review: Inference for Regression Based on the population regression line we can fully describe relationship between x and y up to a random error term ? y = ? 0 + ? 1 x + ? , where ? ? N (0, ? ) Review: Inference for Regression In summary, these are important notations used for SLR: Description x y Parameters ? 0 ? 1  µy ? Stat 326 (Spring 2013) Introduction to Business Statistics II 9 / 47 Stat 326 (Spring 2013)Description Estimates b0 b1 y e Description Introduction to Business Statistics II 10 / 47 Review: Inference for Regre ssion Review: Inference for Regression Validity of predictions Assuming we have a â€Å"good† model, predictions are only valid within the range of x-values used to ? t the LS regression model! Predicting outside the range of x is called extrapolation and should be avoided at all costs as predictions can become unreliable. Why ? t a LS regression model? A â€Å"good† model allows us to make predictions about the behavior of the response variable y for di? rent values of x estimate average sale price ( µy ) for a property appraised at $223,000: x = 223 : y = 20. 94 + 1. 069 ? 223 = 259. 327 ? the average sale price for a property appraised at $223,000 is estimated to be about $259,327 What is a â€Å"good† model? — answer to this question is not straight forward. We can visually check the validity of the ? tted linear model (through residual plots) as well as make use of numerical values such as r 2 . more on assessing the validity of regression model wi ll follow. 11 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 12 / 47 Stat 326 (Spring 2013)Introduction to Business Statistics II Review: Inference for Regression What to look for: Review: Inference for Regression Regression Assumptions residual plot: Assumptions SRS (independence of y -values) linear relationship between x and  µy for each value of x, population of y -values is normally distributed (? ? ? N) r2 : for each value of x, standard deviation of y -values (and of ? ) is ? In order to do inference (con? dence intervals and hypotheses tests), we need the following 4 assumptions to hold: Stat 326 (Spring 2013) Introduction to Business Statistics II 13 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 14 / 47Review: Inference for Regression †SRS Assumption† is hardest to check The †Linearity Assumption† and †Constant SD Assumption† are typically checked visually through a residual plot. Recall: residua l = y ? y = y ? (b0 + b1 x) The †Normality Assumption† is checked by assessing whether residuals are approximately normally distributed (use normal quantile plot) plot x versus residuals any pattern indicates violation Review: Inference for Regression Stat 326 (Spring 2013) Introduction to Business Statistics II 15 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 16 / 47 Review: Inference for RegressionReturning to the Tampa Palms, Florida example: 100 50 Residual 0 -50 -100 -150 0 100 200 300 400 500 600 700 800 900 1000 Review: Inference for Regression Going one step further, excluding the outlier yields 0. 2 0. 1 0. 0 -0. 1 -0. 2 -0. 3 4 4. 5 5 5. 5 log Appraised 6 6. 5 7 Residual Appraised Value (in Thousands of Dollars) Note: non-constant variance can often be stabilized by transforming x, or 0. 5 y , or both: Residual 0. 0 -0. 5 -1. 0 -1. 5 4 4. 5 5 5. 5 log Appraised 6 6. 5 7 outliers/in? uential points in general should only be excluded from an analysis if they can be explained and their exclusion can be justi? ed, e. g. ypo or invalid measurements, etc. excluding outliers always means a loss of information handle outliers with caution may want to compare analyses with and without outliers Stat 326 (Spring 2013) Introduction to Business Statistics II 17 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 18 / 47 Review: Inference for Regression normal quantile plots Tampa Palms example Residuals Sale Price (in Thousands of Dollars) 100 .01 . 05 . 10 . 25 . 50 . 75 . 90 . 95 . 99 Review: Inference for Regression Residuals log Sale 50 Regression Inference Con? dence intervals and hypotheses tests -3 -2 -1 0 1 2 3 Normal Quantile Plot -50 -100 Need to assess whether linear relationship between x and y holds true for entire population. .01 . 05 . 10 . 25 . 50 . 75 . 90 . 95 . 99 Residuals log Sale without outlier 0. 2 0. 1 0 -0. 1 -0. 2 -0. 3 -3 -2 -1 0 1 2 3 This can be accomplished through testing H0 : ? 1 = 0 vs. H0 : ? 1 = 0 based on the estimates slope b1 . For simplicity we will work with the untransformed Tampa Palms data. Normal Quantile Plot Stat 326 (Spring 2013) Introduction to Business Statistics II 19 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 20 / 47 Review: Inference for RegressionReview: Inference for Regression Example: Find 95% CI for ? 1 for the Tampa Palms data set Con? dence intervals We can construct con? dence intervals (CIs) for ? 1 and ? 0 . General form of a con? dence interval estimate  ± t ? SEestimate , where t ? is the critical value corresponding to the chosen level of con? dence C t ? is based on the t-distribution with n ? 2 degrees of freedom (df) Interpretation: Stat 326 (Spring 2013) Introduction to Business Statistics II 21 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 22 / 47 Review: Inference for Regression Review: Inference for RegressionTesting for a linear relationship between x and y If we wish to tes t whether there exists a signi? cant linear relationship between x and y , we need to test H0 : ? 1 = 0 Why? If we fail to reject the null hypothesis (i. e. stick with H0 = ? 1 = 0), the LS regression model reduces to  µy = ? 1 =0 versus Ha : ? 1 = 0 ?0 + ? 1 x ? 0 + 0  · x ? 0 (constant) Introduction to Business Statistics II 24 / 47 = = implying that  µy (and hence y ) is not linearly dependent on x. Stat 326 (Spring 2013) Introduction to Business Statistics II 23 / 47 Stat 326 (Spring 2013) Review: Inference for Regression Review: Inference for RegressionExample (Tampa Palms data set): Test at the ? = 0. 05 level of signi? cance for a linear relationship between the appraised value of a property and the sale price Stat 326 (Spring 2013) Introduction to Business Statistics II 25 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 26 / 47 Inference about Prediction Why ? t a LS regression model? The purpose of a LS regression model is to 1 Inference about Predi ction 2 estimate  µy – average/mean value of y for a given value of x, say x ? e. g. estimate average sale price  µy for all residential property in Tampa Palms appraised at x ? $223,000 predict y – an individual/single future value of the response variable y for a given value of x, say x ? e. g. predict a future sale price of an individual residential property appraised at x ? =$223,000 Keep in mind that we consider predictions for only one value of x at a time. Note, these two tasks are VERY di? erent. Carefully think about the di? erence! Stat 326 (Spring 2013) Introduction to Business Statistics II 27 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 28 / 47 Inference about Prediction To estimate  µy and to predict a single future y value for a given level of x = x ? we can use the LS regression line y = b0 + b1 x Simply substitute the desired value of x, say x ? , for x: y = b0 + b1 x ? Inference about Prediction In addition we need to know how much variability is associated with the point estimator. Taking the variability into account provides information about how good and reliable the point estimator really is. That is, which range potentially captures the true (but unknown) parameter value? Recall from 226 ? construction of con? dence intervals Stat 326 (Spring 2013) Introduction to Business Statistics II 29 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 0 / 47 Inference about Prediction Much more variability is associated with estimating a single observation than estimating an average — individual observations always vary more than averages!! Inference about Prediction Therefore we distinguish a con? dence interval for the average/mean response  µy and a prediction interval for a single future observation y Both intervals use a t ? critical value from a t-distribution with df = n ? 2. the standard error will be di? erent for each interval: While the point estimator for the average  µ y and the future individual value y are the same (namely y = b0 + b1 x ? , the of the two con? dence intervals ! Stat 326 (Spring 2013) Introduction to Business Statistics II 31 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 32 / 47 Inference about Prediction Con? dence interval for the average/mean response  µy Width of the con? dence interval is determined using the standard error SE µ (from estimating the mean response) SE µ can be obtained in JMP Keep in mind that every con? dence interval is always constructed for one speci? c given value x ? A level C con? dence interval for the average/mean response  µy , when x takes the value x? is given by y  ± t ?SE µ , where SE µ is the standard error for estimating a mean response. Stat 326 (Spring 2013) Introduction to Business Statistics II 33 / 47 Inference about Prediction Prediction interval for a single (future) value y Again, Width of the con? dence interval is determined using the standard error SE µ (from estimating the mean response) SEy can be obtained in JMP Keep in mind that every prediction interval is always constructed for one speci? c given value x ? A level C prediction interval for a single observation y , when x takes the value x ? is given by y  ± t ? SEy , where SEy is the standard error for estimating a single response.Stat 326 (Spring 2013) Introduction to Business Statistics II 34 / 47 Inference about Prediction The larger picture: Inference about Prediction The larger picture cont’d. Stat 326 (Spring 2013) Introduction to Business Statistics II 35 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 36 / 47 Inference about Prediction Example: An appliance store runs a 5-month experiment to determine the e? ect of advertising on sales revenue. There are only 5 observations. The scatterplot of the advertising expenditures versus the sales revenues is shown below: Bivariate Fit of Sales Revenues (in Dollars) By Advertising expenditur eInference about Prediction Example cont’d: JMP can draw the con? dence intervals for the mean responses as well as for the predicted values for future observations (prediction intervals). These are called con? dence bands: Bivariate Fit of Sales Revenues (in Dollars) By Advertising expenditure 5000 5000 Sales Revenues (in Dollars) 4000 3000 2000 1000 Sales Revenues (in Dollars) 4000 3000 2000 1000 0 0 0 100 200 300 400 500 600 Advertising expenditure (in Dollars) 0 100 200 300 400 500 600 Advertising expenditure (in Dollars) Linear Fit Linear Fit Sales Revenues (in Dollars) = -100 + 7 Advertising expenditure (in Dollars)Stat 326 (Spring 2013) Introduction to Business Statistics II 37 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 38 / 47 Inference about Prediction Inference about Prediction Estimation and prediction (for the appliance store data) Estimation and prediction – Using JMP For each observation in a data set we can get from JMP: y , SEy , and also SE µ . In JMP do: 1 2 We wish to estimate the mean/average revenue of the subpopulation of stores that spent x ? = 200 on advertising. Suppose that we also wish to predict the revenue in a future month when our store spends x ? = 200 on advertising.The point estimate in both situations is the same: y = ? 100 + 7 ? 200 ? 1300 the corresponding standard errors of the mean and of the prediction however are di? erent: SE µ ? 331. 663 SEy ? 690. 411 40 / 47 Choose Fit Model From response icon, choose Save Columns and then choose Predicted Values, Std Error of Predicted, and Std Error of Individual. Stat 326 (Spring 2013) Introduction to Business Statistics II 39 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II Inference about Prediction Estimation and prediction (cont’d) Note that in the appliance store example, SEy > SE µ (690. 411 versus 331. 63). This is true always: we can estimate a mean value for y for a given x ? much more precisely than we can predict the value of a single y for x = x ?. In estimating a mean  µy for x = x ? , the only uncertainty arises because we do not know the true regression line. In predicting a single y for x = x ? , we have two uncertainties: the true regression line plus the expected variability of y -values around the true line. Inference about Prediction Estimation and prediction (cont’d) It always holds that SE µ < SEy Therefore a prediction interval for a single future observation y will always be wider than a con? ence interval for the mean response  µy as there is simply more uncertainty in predicting a single value. Stat 326 (Spring 2013) Introduction to Business Statistics II 41 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 42 / 47 Inference about Prediction Example cont’d: JMP also calculates con? dence intervals for the mean response  µy as well as prediction intervals for single future observations y. (For instructions follow the handout o n JMP commands related to regression CIs and PIs. ) Inference about Prediction Example cont’d: To construct both a con? ence and/or prediction interval, we need to obtain SE µ and SEy in JMP for the value x ? that we are interested in: Month Ad. Expend. Sales Rev. Pred. Sales Rev. StdErr Pred Sales Revenues StdErr Indiv Sales Revenues Let’s construct one 95% CI and PI by hand and see if we can come up with the same results as JMP: In the second month the appliance store spent x = $200 on advertising and observed $1000 in sales revenue, so x = 200 and y = 1000 Using the estimated LS regression line, we predict: y = ? 100 + 7 ? 200 = 1300 Stat 326 (Spring 2013) Introduction to Business Statistics II 43 / 47 Need to ? nd t ? ?rst:Stat 326 (Spring 2013) Introduction to Business Statistics II 44 / 47 Inference about Prediction A 95% CI for the mean response  µy , when x ? = 200: Inference about Prediction A 95% PI for a single future observation of y , when x ? = 200: S tat 326 (Spring 2013) Introduction to Business Statistics II 45 / 47 Stat 326 (Spring 2013) Introduction to Business Statistics II 46 / 47 Inference about Prediction Example cont’d: Advertising exp. Sales Rev. Lower 95% Mean Upper 95% Mean Sales Rev. Sales Rev. Lower 95% Indiv Sales Rev. Upper 95% Indiv Sales Rev. Month Stat 326 (Spring 2013) Introduction to Business Statistics II 47 / 47

Perceptions of African American Women Essay

I am taking some classes that will eventually qualify me to major in Astro – Physics, or Chemical engineering, I also want to work with NASA and train as an astronaut. It was amazing to know that Dr. Mae C. Jemison who happens to be the youngest of three children born to a middle class African American family, Charlie Jemison, a maintenance worker and his wife, Dorothy, a teacher. Dr. Mae C. Jemison was the first black woman astronaut to be in space in an era filled with segregation and racism, she is a Chemical engineer, scientist, physician, teacher and astronaut, she has a wide range of experience in technology, engineering, and medical research. In addition to her extensive background in science, she is well-versed in African and African-American Studies, speaks fluent Russian, Japanese, and Swahili, as well as English and is trained in dance and choreography. Dr. Mae C. Jamison was an inspiration to me, and probably to many African American women. She was full of resilience and determination especially to have reached and achieved success in an unusual field of endeavor for many African American women, I applaud her determination to make a difference among the African American women and blacks in Diaspora. After graduating from Morgan Park High School in 1973 at the age of 16, Dr. Mae Jemison earned a BS in Chemical Engineering from Stanford University, while also fulfilling the requirements for a BA in African-American Studies. After earning these degrees in 1977, she attended Cornell University and received a Doctor of Medicine degree in 1981. During medical school she traveled to Cuba, Kenya and Thailand, providing primary medical care to people living there. This is an indication of her humanitarian efforts and eagerness to reach out to the less privileged population. Having a desire to do more with her life, she enrolled in graduate classes in engineering and applied to NASA for admission to the astronaut program. She was turned down on her first application, maybe because she is a black woman, but she persevered and in 1987 was accepted on her second application. She became one of the fifteen candidates accepted from over 2,000 applicants. When Dr. Mae Jemison successfully completed her astronaut training program in August 1988, she became the fifth black astronaut and the first black female astronaut in NASA history. In completing her first space flight, Dr. Mae Jemison logged 190 hours, 30 minutes, 23 seconds in space, making her the first African-American woman in space. She says, â€Å"I had to learn very early not to limit myself due to others’ limited imaginations. I have learned these days never to limit anyone else due to my limited imagination. † This is an inspiration to other blacks in general who normally assume a second class citizen and believe that they will never do well or will be appreciated in whatever they do. This is a wake-up call, and manifestation of the saying â€Å"Determination is the mother of invention†. In 1993, Dr. Mae Jemison resigned from NASA and founded the Jemison Group, Inc.to research, develop and implement advanced technologies suited to the social, political, cultural and economic context of the individual, especially for the developing world. Current projects include: Alpha, (TM) a satellite based telecommunication system to improve health care in West Africa; and The Earth We Share, (TM) an international science camp for students ages 12 to 16, that utilizes an experiential curriculum. Among her current projects are several that focus on improving healthcare in Africa. She is also a professor of environmental studies at Dartmouth College. Dr. Mae Jamison made a name for herself and name for blacks in general; Her entrepreneurial spirit put her in the limelight and acts as a boost to determined black men and women in Diaspora. Ellen Johnson-Sirleaf. It was quite surprising to read about Ellen Johnson-Sirleaf, I know almost nothing about this â€Å"giant and queen of modern Africa† who is presently the current president of Liberia. According to what I have read so far about this â€Å"queen of Africa† she was born In Monrovia, the capital of Liberia on October 29, 1938. During this period, Liberians had no clue that the First female president of an African country had been born into their mist. Ellen Johnson-Sirleaf is a daughter to descendents of original colonists of Liberia (ex-African slaves from America, who promptly on arrival set about enslaving the indigenous people using the social system of their old American masters as a basis for their new society). These descendents are known in Liberia as Americo-Liberians. From what I read, I noticed that Ellen Johnson-Sirleaf was truly an intellectual power house, a charismatic leader and destined to make a change in Liberia and contribute her quota in Africa. From 1948 to 1955 Ellen Johnson studied accounts and economics at the College of West Africa in Monrovia. After marriage at the age of 17 to James Sirleaf, she travelled to America (in 1961) and continued her studies, achieving a degree from the University of Colorado. From 1969 to 1971 she read economics at Harvard, gaining a masters degree in public administration. Ellen Johnson-Sirleaf then returned to Liberia and began working in William Tolbert’s (True Whig Party) government. Ellen Johnson-Sirleaf also served as Minister of Finance from 1972 to 73, but left after a disagreement over public spending, this is an indication of her prudence and will power. As the 70s progressed, life under Liberia’s one-party state became more polarized to the benefit of the Americo-Liberian elite. On 12 April 1980 Master Sergeant Samuel Kayon Doe, a member of the indigenous Krahn ethnic group, seized power in a military coup. With the People’s Redemption Council now in power, Samuel Doe began a purge of government. Ellen Johnson-Sirleaf narrowly escaped – choosing exile in Kenya. From 1983 to 1985 she served as Director of Citibank in Nairobi. I will say that Ellen Johnson-Sirleaf had a lot of courage, because it was quite unusual for a woman to challenge a dictatorial incumbent president in Africa without being kidnapped, tortured or killed in the process, although She was later sentenced to ten years in prison. Ellen Johnson-Sirleaf spent just a short time incarcerated, before being allowed to leave the country once again as an exile. During the 1980s she served as Vice President of both the African Regional Office of Citibank, in Nairobi, and of (HSCB) Equator Bank, in Washington. Ellen Johnson-Sirleaf played an active role in the transitional government as the country prepared for the 2005 elections, and eventually stood for president against her rival the ex-international footballer, George Manneh Weah. Despite the elections being called fair and orderly, Weah repudiated the result, which gave a majority to Johnson-Sirleaf, Ellen Johnson-Sirleaf eventually became Liberia’s first elected female president, as well as the first elected female president in the continent Africa. . In 2005 She established a Truth and Reconciliation Commission with a mandate to â€Å"promote national peace, security, unity and reconciliation† by investigating more than 20 years of civil conflict in the country and in November 2007, she received the United States Presidential Medal of Freedom, the U. S. government’s highest civilian award. She is truly a giant and â€Å"queen of modern Africa†. References: 1. http://space. about. com/cs/formerastronauts/a/jemisonbio. htm 2. http://www. k-grayengineeringeducation. com/blog/index. php/2008/09/12/first-african-american-women-in-space. 3. http://www. joinafrica. com/africa_of_the_week/ellenjohnsonliberia. htm.