...Correlation, Predication, Confidence, and Errors: Analysis of Statistics in the News In statistics, there are a number of ways to compare how data is associated with the subject matters being discussed. In this paper, I will discuss how three different magazine articles use statistics to improve and expound on their theories, the methodology used, and if their use of statistics made the article more or less convincing. The statistical methods that will discuss statistics in the news include correlation, predication, confidence, and errors. Correlation “exists between two variables when higher values of one variable consistently go with higher values of another variable or when higher values of one variable consistently go with lower values of another variable. (Bennet, Briggs & Triola 2014)” It is only used with quantifiable data where the numbers are meaningful (height, weight, age, etc), therefore, it can’t be used for categorical data (gender, favorite foods, jobs, etc). Predication is defined by Britannica.com as the attribution of characteristics to a subject to produce something meaningful. This combines verbal elements and those that exist in name only (Britannica 2015). Confidence in statistics is defined as “a group of continuous or discrete adjacent values used to estimate a statistical parameter as a means of variance” (Merriam-Webster 2015). Statistical errors means that when null hypothesis is proven incorrect, then the alternative hypothesis is accepted...
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...STATISTICAL ANALYSIS Statistical is an explanation type in social science trough credible causal mechanisms such as quantitative reasoning, statistical analysis and comparative, and statistic explanation et cetera. Basic of statistical explanation, there are two points which are understanding of concept and second is questions. In terms of statistical analysis, researcher needs the collection, summarization, manipulation, and interpretation of quantitative data to discover its underlying causes, patterns relationships and trends. In the quantitative reasoning in social science, the data set is involved into the structure. Data which involve might be a time-series data set for study to a time sequence or complex data which researcher has to extract from it. Beside, the null hypothesis is used as a tool for a condition which is different from the absolute probability of the event by using for considering to economic growth and political stability. Strength Weakness Enables the research and description of social structures and processes that are not directly observable. -Simplifies and ”compresses” the complex reality: abstract and constrained perspective Well-suited for quantitative description, comparisons between groups, areas etc. - Only applicable for measurable (quantifiable) phenomena Analysis and explanation of (causal) dependencies between social phenomena. -Only applicable for measurable (quantifiable) phenomena. -Presumes relatively extensive knowledge on the subject...
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...INDIAN INSTITUTE OF MANAGEMENT KOZHIKODE- KOCHI CAMPUS, INFOPARK, KAKKANAD, KOCHI- 30 Descriptive Statistics Analysis Study Submitted by, Rakesh Krishnan S, EPGPKC02-WEB076 Analysis on ITC Sun feast sales figures: ITC made a foray into the biscuits market by launching the Sunfeast range of biscuits in 2003. Since then, Sunfeast biscuits have always stood for quality and are known for offering innovative and wholesome biscuits. Within a span of 11 years, Sunfeast has well-established presence in almost all categories of biscuits. The data provided for analysis is the sales figures of Sunfeast biscuits in Kerala region as a whole, for a span of 3 years. The analysis has been done using quantitative methods and also performed hypothesis testing for a sample of data taken from the three years sales data. The population data consist of 36 sales figures taken for the years 2011-13 Below is the snapshot of the overall sales distribution (in tonnes) for Sunfeast for a span of three years: Below is the descriptive analysis done on the population data, consisting of three years sales figure from 2011-13 FY’s. Descriptive Statistics for Population Data | | | Mean | 1260613.249 | Standard Error | 31342.14415 | Median | 1262395.307 | Mode | #N/A | Standard Deviation | 188052.8649 | Sample Variance | 35363880006 | Range | 679897.672 | Minimum | 859243.22 | Maximum | 1539140.892 | Sum | 45382076.95 | Count | 36 | Mean and Standard Deviation: ...
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...Exploratory Data Analysis Course Project Part A Subject: Applied Managerial Statistics Faculty: Curtis Allen Brown Submitted by: Christian Oji Introduction Exploratory Data Analysis (EDA): Exploratory data analysis is an approach to analyze statistical data using a variety of techniques out of which many are graphical analyses. EDA is used to dissect the data and look for the hidden patterns and correlations. Some of the graphical methods include pie charts, bar graphs, histograms, frequency and relative frequency tables, box plot, scatter graph, stem-leaf diagram etc. There are also quantitative measures of data which include central tendencies like mean and median, measure of dispersion like standard deviation, minimum and maximum...
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...DISTINGUISH BETWEEN PARAMETRIC AND NONPARAMETRIC STATISTICS AND DISCUSS WHEN TO USE EACH METHOD IN ANALYSIS OF DATA The word parametric comes from “metric” meaning to measure, and “para” meaning beside or closely related. The combined term refers to the assumptions about the population from which the measurements were obtained. The two classes of statistical tests are: Parametric Statistics Nonparametric Statistics i. Parametric Statistics: Parametric statistics are statistical tests for population parameters such as means, variances and proportions that involve assumptions about the populations from which the samples were selected. These assumptions include: Observations must be independent i.e. when values in one set are different and unrelated from another set Observations must be drawn from normally distributed populations The populations must have the same variances The sample must be random Use of Parametric Statistics in Data Analysis: Parametric tests are used when the above parametric assumptions are met. Parametric tests are also used to analyze interval and ratio data. Interval data are numerical data in which we not only know the order but also the exact differences between the values e.g. the time interval between the starts of years 1981 and 1982 is the same as that between 1983 and 1984 which is 365 days. Ratio data on the other hand describe measurements with attributes that have the qualities of nominal, ordinal and interval data and...
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...Product & Gender Count of Product Gender Product Female TM195 TM498 TM798 Grand Total Product & Marital Status Count of Product Marital Status Product Partnered Single TM195 48 TM498 36 TM798 23 Grand Total 107 Male 40 29 7 76 40 31 33 104 Grand Total 80 60 40 180 32 24 17 73 Grand Total 80 60 40 180 CUSTOMER PROFILE FOR EACH PRODUCT Product TM195 Education 14 15 14 12 13 14 14 13 Age 18 19 19 19 20 20 21 21 Income 29562 31836 30699 32973 35247 32973 35247 32973 Miles 112 75 66 85 47 66 75 85 Summary Tables One-‐Way Summary Table Count Gender Gender Female Male Total TM195 Categorical Variables Marital Status Gender Single Male Single Male Partnered Female Single Male Partnered Male Partnered Female Partnered Female Single Male Total 40 40 80 Single Partnered Single Partnered Single Single Partnered Partnered Single Partnered Single Partnered Single Single Single Partnered Single Partnered Single Partnered Partnered Partnered Partnered Single Partnered Single Partnered Partnered Partnered Partnered Single Partnered Single Male Female Male Female Female Female Male Male Female Male Female Female Male Male Female Female Male Male Female Female Male Female Female Male Female Male Female Female Male Male Female Male Male 15 15 14 14 16 14 16 16 14 16 16 15 14 16 16 16 14 13 16 14 14 14 14 16 16 16 14 16 16 16 16 16 16 21 21 22 22 22 22 23 23 23...
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...Team 5 Proposal Alison Frey, Dan Gray, Scott Monroy, Lisa Nemec Are Minnesotans better drivers than Wisconsinites? Statistics 201.07 Instructor Shafi A. Khaled Project Overview The competitive relationship between Minnesota and Wisconsin is a time tested tradition that we have all grown up knowing. From our sports teams to who has a better tourist draw, Minnesotans and Wisconsinites love to debate on who has the better state. One thing is consistent however; Minnesota and Wisconsin share a very similar pattern of weather. With that weather cycle, there inevitably will be accidents. These accidents can be caused by a number of factors, but weather is typically to blame in the majority of them. Other factors might include age, race, and gender. One thing is certain though, we believe that Minnesotans are the better drivers in inclement weather conditions and we intend to prove this through data collection and analysis. We believe that in the end, Minnesota will have the better drivers. Project Proposal Problem: Minnesotans and Wisconsinites continually complain about the weather and the drivers’ bad driving habits of the other state. We want to determine which of the two states have the most motor vehicle accidents during inclement weather conditions. We believe these accidents are due to outside conditions and that there will be a decrease in the amount of accidents during sunny conditions versus an increase in the number of accidents when there are adverse weather...
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...QUANTITATIVE ANALYSIS: DESCRIPTIVE STATISTICS Introduction Suppose that we have carried out a survey on the effect of carrying out a management audit with three groups of nine participant institutions each i.e. small medium and large. Each group was given the same survey questions in questionnaire format and the answers from the scores were tagged between 0 and 20. What is to be done with the raw scores? There are two key types of measures that can be taken whenever we have a set of scores from participants in a given condition. First, there are measures of central tendency, which provide some indication of the size of average or typical scores. Second, there are measures of dispersion, which indicate the extent to which the scores cluster around the average or are spread out. Various measures of central tendency and of dispersion are considered next. For this assignment, a survey is the type of data collection method in consideration and how the results of that survey would be analysed. SURVEYS Surveys are a very popular form of data collection, especially when gathering information from large groups, where standardization is important. Surveys can be constructed in many ways, but they always consist of two components: questions and responses. While sometimes evaluators choose to keep responses “open ended,” i.e., allow respondents to answer in a free flowing narrative form, most often the “close-ended” approach in which respondents are asked to select from a range of...
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...MTH 233 Week 4 Individual Assignment: Individual Assignment MTH 233 Week 5 Individual Assignment: Individual Assignment MTH 233 Week 5 Learning Team Assignment: Hypothesis Testing and Regression Analysis Paper only MTH 233 Learning Team Assignment: Hypothesis Testing and Regression Analysis Presentation ----------------------------------------------- MTH 233 Learning Team Assignment Hypothesis Testing and Regression Analysis Presentation For more classes visit www.snaptutorial.com Resources: University Library and the Internet Select a research issue, problem, or opportunity facing a Learning Team member’s organization to examine using hypothesis testing and a regression analysis on the collected data. Write a 1,050- to 1,750-word paper describing a new hypothesis test using a different statistic (e.g., large sample size, small sample size, means and/or proportions, one- and two-tailed tests) to perform on that data. Formulate a new hypothesis statement and perform the five-step hypothesis test on the data. Describe the results of the tests. Interpret the results of the regression analysis, state the limitations of the analysis, and describe the significance of the results to the organization. Be sure to attach the results of the regression analysis created in Microsoft® Excel to your paper. Present the results to the class in a 10-minute PowerPoint® presentation ----------------------------------------------- MTH 233 Week 2 Individual Assignment...
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...One of the most basic statistical analysis is descriptive analysis. Descriptive statistics can summarize responses from large numbers of respondents in a few simple statistics. When a sample is obtained, the sample descriptive statistics are used to make inferences about characteristics of the entire population of interests. Descriptive analysis is the transformation of data in a way that describes the basic characteristics such as tendency, distribution, and variables. A examples of this would be if a company wanted to find out what type of bonus employees prefer. Descriptive statistics are used to explain the basic properties of these variables. One descriptive statistics that is used to explain the basic properties of variables is Mean, Median, and Modes. These terms all would be descriptive statistics for the above example by describing the central tendency in different ways. The mean would reflect the average answer that is given. The Median would provide the answer that is the central or middle range answer. The mode would be the answer that was given the most often. A second descriptive statistic that is used to explain the basic properties of variables is Tabulation. This refers to the orderly arrangement of data in a table or other summary format. When the tabulation process is done by hand, the term tallying is used. Simple tabulation tells how frequently each response or bit of information occurs. A third descriptive statistic used to explain the basic properties...
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...Statistics: Highly Informative Latosha Greer BUS308: Statistics for Managers Instructor Hayes June 1, 2014 In this essay I am aim to discuss the differences between descriptive statistics and Inferential statistics and the reasons why we use them. I will also discuss hypothesis development and testing, when to select the appropriate statistical test, and how to evaluating statistical results. In this class I learned the difference between descriptive statistics and inferential statistics. We use descriptive statistics to measure and analysis data. There are a number of reasons why we use Descriptive statistics. We use it, because Descriptive statistics numerical summaries measure the central tendency of a data set, it can include graphical summaries that show the spread of the data, and they provide simple summaries about the sample that help interpret and analyze data. First, there are a number of reasons why we use descriptive statistics we use it because descriptive statistics numerical summaries that either measure the central tendency of a data set. In business therefore descriptive statistics helps in making conclusions about various issues and therefore helps in making decision. Description statistics is the first step in analyzing data before making inferences of data, therefore it is important in analyzing any data collected that will help in describing the characteristics of data collected. There are three measurements that we tend to use. One measurement...
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...and statistics are two related but separate academic disciplines. Statistical analysis often uses probability distributions, and the two topics are often studied together. However, probability theory contains much that is of mostly of mathematical interest and not directly relevant to statistics. Moreover, many topics in statistics are independent of probability theory. Probability (or likelihood) is a measure or estimation of how likely it is that something will happen or that a statement is true. Probabilities are given a value between 0 (0% chance or will not happen) and 1 (100% chance or will happen). The higher the degree of probability, the more likely the event is to happen, or, in a longer series of samples, the greater the number of times such event is expected to happen. These concepts have been given an axiomatic mathematical derivation in probability theory (see probability axioms), which is used widely in such areas of study as mathematics, statistics, finance, gambling, science, artificial intelligence/machine learning and philosophy to, for example, draw inferences about the expected frequency of events. Probability theory is also used to describe the underlying mechanics and regularities of complex systems. Statistics is the study of the collection, organization, analysis, interpretation and presentation of data. It deals with all aspects of data, including the planning of data collection in terms of the design of surveys and experiments. The word statistics, when...
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...Statistics is the study of the collection, organization, analysis, interpretation and presentation of data. There are variety of different types of statistics, hard statistics are numerical data that can not be manipulated, soft statistics are numerical data that can be manipulated, official statistics are produced by formal institutions whilst unofficial are carried out by informal institutions. By definition, statistical data is quantative data which increases its reliability. Using Statistics in research has a variety of different advantages. This kind of research is cheap and easily accessible. Also, as not much time is spent on primary research, analysis will be fairly quick so the results are fairly up to date. The ready availability of official statistics mean the researcher does not have to spend time and money collecting his / her own information. Unlike in Qualitative research methods such as interviews that could possibly involve travelling costs. It may be the case that official statistics are the only available source for a piece of research. Durkheim, for example, in his study of in 1897 used official statistics drawn from coroners' reports from different societies to establish that suicide rates varied within societies. By doing this, he was able to argue that social factors, such as religious belief, were significant variables and influences in the explanation of why people committed suicide. For some topics, therefore, secondary research is the only way...
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...The terms "statistical analysis" and "data analysis" can be said to mean the same thing -- the study of how we describe, combine, and make inferences based on numbers. A lot of people are scared of numbers (quantiphobia), but data analysis with statistics has got less to do with numbers, and more to do with rules for arranging them. It even lets you create some of those rules yourself, so instead of looking at it like a lot of memorization, it's best to see it as an extension of the research mentality, something researchers do anyway (i.e., play with or crunch numbers). Once you realize that YOU have complete and total power over how you want to arrange numbers, your fear of them will disappear. It helps, of course, if you know some basic algebra and arithmetic, at a level where you might be comfortable solving the following equation There are three (3) general areas that make up the field of statistics: descriptive statistics, relational statistics, and inferential statistics. 1. Descriptive statistics fall into one of two categories: measures of central tendency (mean, median, and mode) or measures of dispersion (standard deviation and variance). Their purpose is to explore hunches that may have come up during the course of the research process, but most people compute them to look at the normality of their numbers. Examples include descriptive analysis of sex, age, race, social class, and so forth. 2. Relationalstatistics fall into one of three categories: univariate,...
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...non-parametric inference tests are also described in the case where the data Sample distribution is not compatible with standard parameter distribution. Thirdly, using multiple resampling methods Computer -generated random sample finally introduced the characteristics of the distribution and estimate Statistical inference. The method of multivariate data processing of the following sections involved. method Clinical trials also briefly review process. Finally, the last section of statistical computer software discussion And through the collection of citations to adapt to different levels of expertise, and to guide readers theme. In this article, there are some methods such as descriptive statistic, probablity distruction, possion ditruction and multivariate methods. For the descriptive statistic, they are tabular, graphical and Numerical Methods basic characteristics of the sample It can be described. While these methods may be the same Used to describe the entire group, they more...
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