{"id":2896,"date":"2015-06-10T08:59:47","date_gmt":"2015-06-10T08:59:47","guid":{"rendered":"http:\/\/www.evolutionwriters.com\/samples_and_examples\/?p=2896"},"modified":"2022-03-07T16:36:36","modified_gmt":"2022-03-07T16:36:36","slug":"sample-on-statistics-project","status":"publish","type":"post","link":"https:\/\/evolutionwriters.com\/samples_and_examples\/miscellaneous\/sample-on-statistics-project.html","title":{"rendered":"Sample on Statistics Project"},"content":{"rendered":"<p style=\"text-align: center;\"><em>Often students come to us saying, &#8220;<a href=\"https:\/\/evolutionwriters.com\/\">Write my essay<\/a>, please, it&#8217;s so boring I&#8217;m afraid of dislocating my jaw!&#8221; However, the sample below shows how any topic can be made exciting \u2013 with a pinch of creativity.<\/em><\/p>\n<p lang=\"ru-RU\" align=\"center\">Statistics Project<\/p>\n<h3>Introduction<\/h3>\n<p align=\"justify\">In this paper we will discuss and describe how the basic tools of statistics analysis and probability theory may be applied to a real world problem. The data given is a breakdown of all 426 goals made by Lionel Messi during his football career. There are 90 observations of the data; each represents the number of goals during a game played by L. Messi. Our goal is to perform multiple steps of statistical analysis: we build frequency distribution characteristics \u2013 frequency tables and histograms, pie charts, elements of descriptive statistics (measures of central tendency and measures of variability). In addition to the descriptive part, we also perform a hypothesis testing part to compare mean values between two groups of the data.<\/p>\n<p><!--more--><\/p>\n<div class=\"banner-proofread\">\n<div class=\"wrap\">\n<div class=\"btn-proofread\"><a href=\"https:\/\/evolutionwriters.com\/order.html\"><button>WRITE AWESOME ESSAY FOR ME<\/button><\/a><\/div>\n<\/div>\n<\/div>\n<p align=\"justify\">Body<\/p>\n<p lang=\"ru-RU\" align=\"justify\">First of all, it should be mentioned that on 90<sup>th<\/sup> position of our sample we have the sum of all goals made by Lionel Messi from 90<sup>th<\/sup> game and since today. That\u2019s why this indicator may bias the results of our procedures as it doesn\u2019t represent the number of goals in a single game. We would like to exclude this observation from the sample and proceed with only the rest.<\/p>\n<p align=\"justify\">For the first part of our research, we begin with a frequency distribution of the data using first 67 observations. The number of goals in this subsample varies from 0 to 12. That\u2019s why we decide to divide the data by 6 classes: 0-2,3-5, 6-8, 9-11, 12. The width of classes is 2. A frequency table has the following form:<\/p>\n<table width=\"216\" cellspacing=\"0\" cellpadding=\"7\">\n<colgroup>\n<col width=\"37\" \/>\n<col width=\"67\" \/>\n<col width=\"67\" \/> <\/colgroup>\n<tbody>\n<tr valign=\"top\">\n<td width=\"37\">\n<p align=\"justify\"><b>Class<\/b><\/p>\n<\/td>\n<td width=\"67\">\n<p align=\"justify\"><b>Frequency<\/b><\/p>\n<\/td>\n<td width=\"67\">\n<p align=\"justify\"><b>Percentage<\/b><\/p>\n<\/td>\n<\/tr>\n<tr valign=\"top\">\n<td width=\"37\">\n<p align=\"justify\"><b>0-2<\/b><\/p>\n<\/td>\n<td width=\"67\">\n<p align=\"justify\">19<\/p>\n<\/td>\n<td width=\"67\">\n<p align=\"justify\">28.36%<\/p>\n<\/td>\n<\/tr>\n<tr valign=\"top\">\n<td width=\"37\">\n<p align=\"justify\"><b>3-5<\/b><\/p>\n<\/td>\n<td width=\"67\">\n<p align=\"justify\">33<\/p>\n<\/td>\n<td width=\"67\">\n<p align=\"justify\">49.25%<\/p>\n<\/td>\n<\/tr>\n<tr valign=\"top\">\n<td width=\"37\">\n<p align=\"justify\"><b>6-8<\/b><\/p>\n<\/td>\n<td width=\"67\">\n<p align=\"justify\">12<\/p>\n<\/td>\n<td width=\"67\">\n<p align=\"justify\">17.91%<\/p>\n<\/td>\n<\/tr>\n<tr valign=\"top\">\n<td width=\"37\">\n<p align=\"justify\"><b>9-11<\/b><\/p>\n<\/td>\n<td width=\"67\">\n<p align=\"justify\">2<\/p>\n<\/td>\n<td width=\"67\">\n<p align=\"justify\">2.99%<\/p>\n<\/td>\n<\/tr>\n<tr valign=\"top\">\n<td width=\"37\">\n<p align=\"justify\"><b>12<\/b><\/p>\n<\/td>\n<td width=\"67\">\n<p align=\"justify\">1<\/p>\n<\/td>\n<td width=\"67\">\n<p align=\"justify\">1.49%<\/p>\n<\/td>\n<\/tr>\n<tr valign=\"top\">\n<td width=\"37\">\n<p align=\"justify\"><b>Total<\/b><\/p>\n<\/td>\n<td width=\"67\">\n<p align=\"justify\"><b>67<\/b><\/p>\n<\/td>\n<td width=\"67\">\n<p align=\"justify\"><b>100%<\/b><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p align=\"justify\">To present the data distribution in the most suitable form, we construct frequency histogram with a normal curve. This step allows us to compare the distribution of the given variable with a normal distribution:<\/p>\n<p lang=\"ru-RU\" align=\"justify\"><img decoding=\"async\" loading=\"lazy\" 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\" alt=\"histogram\" width=\"443\" height=\"295\" name=\"Object1\" align=\"bottom\" \/><\/p>\n<p align=\"justify\">The next step of the research is to complete a Pie Chart. We do not use classes in this case, just for each value of goals during a game we calculate percentage and represent it on a Pie Chart:<\/p>\n<p lang=\"ru-RU\" align=\"justify\"><img decoding=\"async\" loading=\"lazy\" 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\" alt=\"Pie Chart\" width=\"441\" height=\"293\" name=\"Object2\" align=\"bottom\" \/><\/p>\n<p align=\"justify\">As the next step of the project we have to calculate the basic measured of descriptive statistics: measures of central tendency and measures of variability. Everything what is required, is included in the table below:<\/p>\n<p><span style=\"font-family: Arial,sans-serif;\"><span style=\"font-size: small;\"><b>Descriptive Statistics: Messi_Goals <\/b><\/span><\/span><\/p>\n<p lang=\"ru-RU\"><span style=\"font-family: Courier New,serif;\"><span style=\"font-size: small;\">N for<\/span><\/span><\/p>\n<p><span style=\"font-family: Courier New,serif;\"><span style=\"font-size: small;\">Variable Mean TrMean StDev Variance Median Mode Mode<\/span><\/span><\/p>\n<p><span style=\"font-family: Courier New,serif;\"><span style=\"font-size: small;\">Messi_Goals 4,149 4,066 2,285 5,220 4,000 5 15<\/span><\/span><\/p>\n<p align=\"justify\">Also, midrange is: 6<\/p>\n<p align=\"justify\">Weighted mean is:<\/p>\n<p lang=\"ru-RU\" align=\"justify\"><img decoding=\"async\" loading=\"lazy\" src=\"data:image\/png;base64,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\" alt=\"Statistic\" width=\"267\" height=\"38\" name=\"Object3\" align=\"absmiddle\" \/><\/p>\n<p align=\"justify\">The obtained from descriptive statistics information about the distribution allows us to do the following conclusions. The measures of the \u201cmiddle\u201d are different. We can see that Mean is 4.149, mode is 5, median is 4, trimmed mean is 4.066, weighted mean is 3.13.<\/p>\n<p align=\"justify\">This difference caused by the fact that the distribution is not normal; it is a little positively skewed. Besides, there are few outliers of the data, such as 12 goals in 1 game (an unusual observation). For a skewed data, the best measure of the middle is median value.<\/p>\n<p align=\"justify\">For the second part of the paper we have to divide the data on 2 groups. The first group is 67 observations from the very beginning and the second group is the remaining data. Since we do not know the population standard deviation (we have only summary statistics for 90+ games), we use Student\u2019s t-test instead of z-test.<\/p>\n<p align=\"justify\">Null hypothesis: there is no significant difference in goal numbers between two groups<\/p>\n<p align=\"justify\">Alternative hypothesis: there is a significant difference in goal numbers between two groups.<\/p>\n<p lang=\"ru-RU\" align=\"justify\"><img decoding=\"async\" loading=\"lazy\" src=\"data:image\/png;base64,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\" alt=\"hypothesis 1\" width=\"78\" height=\"21\" name=\"Object4\" align=\"absmiddle\" \/><img decoding=\"async\" loading=\"lazy\" src=\"data:image\/png;base64,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\" alt=\"hypothesis 2\" width=\"79\" height=\"21\" name=\"Object5\" align=\"absmiddle\" \/><\/p>\n<p align=\"justify\">Level of significance is 5%:<\/p>\n<p lang=\"ru-RU\" align=\"justify\"><img decoding=\"async\" loading=\"lazy\" src=\"data:image\/png;base64,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\" alt=\"significance\" width=\"62\" height=\"18\" name=\"Object6\" align=\"absmiddle\" \/><\/p>\n<p align=\"justify\">Run t-test:<\/p>\n<p><span style=\"font-family: Arial,sans-serif;\"><span style=\"font-size: small;\"><b>Two-Sample T-Test and CI: Messi_Goals_1; Messi_Goals_2 <\/b><\/span><\/span><\/p>\n<p><span style=\"font-family: Courier New,serif;\"><span style=\"font-size: small;\">Two-sample T for Messi_Goals_1 vs Messi_Goals_2<\/span><\/span><\/p>\n<p lang=\"ru-RU\"><span style=\"font-family: Courier New,serif;\"><span style=\"font-size: small;\">N Mean StDev SE Mean<\/span><\/span><\/p>\n<p><span style=\"font-family: Courier New,serif;\"><span style=\"font-size: small;\">Messi_Goals_1 67 4,15 2,28 0,28<\/span><\/span><\/p>\n<p><span style=\"font-family: Courier New,serif;\"><span style=\"font-size: small;\">Messi_Goals_2 23 6,39 4,84 1,0<\/span><\/span><\/p>\n<p><span style=\"font-family: Courier New,serif;\"><span style=\"font-size: small;\">Difference = mu (Messi_Goals_1) &#8211; mu (Messi_Goals_2)<\/span><\/span><\/p>\n<p><span style=\"font-family: Courier New,serif;\"><span style=\"font-size: small;\">Estimate for difference: -2,24<\/span><\/span><\/p>\n<p><span style=\"font-family: Courier New,serif;\"><span style=\"font-size: small;\">95% CI for difference: (-4,40; -0,09)<\/span><\/span><\/p>\n<p><span style=\"font-family: Courier New,serif;\"><span style=\"font-size: small;\">T-Test of difference = 0 (vs not =): T-Value = -2,14 P-Value = 0,042 DF = 25<\/span><\/span><\/p>\n<p lang=\"ru-RU\" align=\"justify\">We can see that p-value is 0.042 which is &lt; 0.05, thus we reject the null hypothesis. We support the claim that there is a significant difference in goal numbers between two groups (at 5% level of significance). This might be because Lionel Messi performed better in his latest games compared to the beginning of his career.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Often students come to us saying, &#8220;Write my essay, please, it&#8217;s so boring I&#8217;m afraid of dislocating my jaw!&#8221; However, the sample below shows how any topic can be made exciting \u2013 with a pinch of creativity. Statistics Project Introduction In this paper we will discuss and describe how the basic tools of statistics analysis&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/evolutionwriters.com\/samples_and_examples\/wp-json\/wp\/v2\/posts\/2896"}],"collection":[{"href":"https:\/\/evolutionwriters.com\/samples_and_examples\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/evolutionwriters.com\/samples_and_examples\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/evolutionwriters.com\/samples_and_examples\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/evolutionwriters.com\/samples_and_examples\/wp-json\/wp\/v2\/comments?post=2896"}],"version-history":[{"count":9,"href":"https:\/\/evolutionwriters.com\/samples_and_examples\/wp-json\/wp\/v2\/posts\/2896\/revisions"}],"predecessor-version":[{"id":3604,"href":"https:\/\/evolutionwriters.com\/samples_and_examples\/wp-json\/wp\/v2\/posts\/2896\/revisions\/3604"}],"wp:attachment":[{"href":"https:\/\/evolutionwriters.com\/samples_and_examples\/wp-json\/wp\/v2\/media?parent=2896"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/evolutionwriters.com\/samples_and_examples\/wp-json\/wp\/v2\/categories?post=2896"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/evolutionwriters.com\/samples_and_examples\/wp-json\/wp\/v2\/tags?post=2896"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}