Differences Between The British political System and Others †Government Essay

Differences Between The British political System and Others – Government Essay Free Online Research Papers I am going to discuss Russian and English government system. There are a little bit differences between this two government systems. First, England has democratic monarchy and Russia is democratic country. In theory British Queen has absolute power, but in reality it is just tradition. Queen just sign the laws and speak the speeches which has been written for her by members of government. In Russia the President keep the power in his hands. He can give his own opinion about any law which government gave him to sign up. But President in Russia should also listen peoples opinion about laws and their recommendation. I don’t know much about British government system but I think English Prime Minister has more power and freedom than Russian. I think that just a few per cent of people hear about Russian Prime Minister Fradkov, but everybody knows Tony Blair. There are five parties in Russian government, but they don’t play really big role in Russian political system and Prime Minister don’t really follow them. There is opposite meaning about Prime Minister in England. The Prime Minister in England like main face of country and he also need to support his party. In Russia main parties support communism but people in Russia remember the communists days in Russia and just small part of Russian can dare to vote for communists parties. There are three main parties in Britain. The conservative party are also support by richest sections of society and large part of working class, Iain Duncan Smith is leader of this party. The main British party is Labour party. The Prime Minister of Great Britain is also leader of this party from May 2002. Middle and working classes always vote for Labours. The Liberal Democrats pa rty headed by Charles Kennedy is more poorly, than two previous parties, but all classes of society vote for Liberals. In the end I want to say, that there are no ideal political system in the world. English government is very traditional. I think that it is disadvantage and advantage at the same time. And the biggest problem in Russia that every politician lies to people. Research Papers on Differences Between The British political System and Others - Government EssayQuebec and CanadaAppeasement Policy Towards the Outbreak of World War 2Assess the importance of Nationalism 1815-1850 EuropePETSTEL analysis of IndiaBringing Democracy to AfricaThe Effects of Illegal ImmigrationNever Been Kicked Out of a Place This Nice19 Century Society: A Deeply Divided EraCapital PunishmentTwilight of the UAW

Example of Goodness of Fit Test

Example of Goodness of Fit Test The chi-square goodness of fit test is a useful to compare a theoretical model to observed data. This test is a type of the more general chi-square test. As with any topic in mathematics or statistics, it can be helpful to work through an example in order to understand what is happening, through an example of the chi-square goodness of fit test. Consider a standard package of milk chocolate MMs. There are six different colors: red, orange, yellow, green, blue and brown. Suppose that we are curious about the distribution of these colors and ask, do all six colors occur in equal proportion? This is the type of question that can be answered with a goodness of fit test. Setting We begin by noting the setting and why the goodness of fit test is appropriate. Our variable of color is categorical. There are six levels of this variable, corresponding to the six colors that are possible. We will assume that the MMs we count will be a simple random sample from the population of all MMs. Null and Alternative Hypotheses The null and alternative hypotheses for our goodness of fit test reflect the assumption that we are making about the population. Since we are testing whether the colors occur in equal proportions, our null hypothesis will be that all colors occur in the same proportion. More formally, if p1 is the population proportion of red candies, p2 is the population proportion of orange candies, and so on, then the null hypothesis is that p1 p2 . . . p6 1/6. The alternative hypothesis is that at least one of the population proportions is not equal to 1/6. Actual and Expected Counts The actual counts are the number of candies for each of the six colors. The expected count refers to what we would expect if the null hypothesis were true. We will let n be the size of our sample. The expected number of red candies is p1 n or n/6. In fact, for this example, the expected number of candies for each of the six colors is simply n times pi, or n/6. Chi-square Statistic for Goodness of Fit We will now calculate a chi-square statistic for a specific example. Suppose that we have a simple random sample of 600 MM candies with the following distribution: 212 of the candies are blue.147 of the candies are orange.103 of the candies are green.50 of the candies are red.46 of the candies are yellow.42 of the candies are brown. If the null hypothesis were true, then the expected counts for each of these colors would be (1/6) x 600 100. We now use this in our calculation of the chi-square statistic. We calculate the contribution to our statistic from each of the colors. Each is of the form (Actual – Expected)2/Expected.: For blue we have (212 – 100)2/100 125.44For orange we have (147 – 100)2/100 22.09For green we have (103 – 100)2/100 0.09For red we have (50 – 100)2/100 25For yellow we have (46 – 100)2/100 29.16For brown we have (42 – 100)2/100 33.64 We then total all of these contributions and determine that our chi-square statistic is 125.44 22.09 0.09 25 29.16 33.64 235.42. Degrees of Freedom The number of degrees of freedom for a goodness of fit test is simply one less than the number of levels of our variable. Since there were six colors, we have 6 – 1 5 degrees of freedom. Chi-square Table and P-Value The chi-square statistic of 235.42 that we calculated corresponds to a particular location on a chi-square distribution with five degrees of freedom. We now need a p-value, to determines the probability of obtaining a test statistic at least as extreme as 235.42 while assuming that the null hypothesis is true. Microsoft’s Excel can be used for this calculation. We find that our test statistic with five degrees of freedom has a p-value of 7.29 x 10-49. This is an extremely small p-value. Decision Rule We make our decision on whether to reject the null hypothesis based on the size of the p-value. Since we have a very miniscule p-value, we reject the null hypothesis. We conclude that MMs are not evenly distributed among the six different colors. A follow-up analysis could be used to determine a confidence interval for the population proportion of one particular color.