...Telling the truth about Damned Lies and Statistics The author made clear that most of the American people are not-so-math-literate. When a regular American is shown eye catching graphs and percentages they would believe it, without even knowing the consistency of the statistics. It wouldn’t be improbable to believe without any developed mathematical skills that a regular American is unable to estimate or even tell, the stability of any given statistics. Joel Best is saying that the society is gullible and has lack of judgment when it comes to statistics and he’s not to prove the incapability of the education system. In this article, the author, Joel Best is talks about how society is continuously fed by hyperboles and how they only pay attention to the numbers and not the context. For example, the statement "Brand X is 84% fat-free" sounds good until you realize that this means the food product is 16% fat by weight. Also, "fastest growing" could mean that there used to be one customer and then there were five more, making a five-hundred percent increase. The media runs on the society’s credulity using statistics to bolster their weak arguments. For example, in advertisements saying a product is the fastest growing, but specifically compared to what or when. Isn’t it that numbers are supposed to represent ”objective scientific data” which we can’t deny because it has been studied by experienced experts. But some people like dirty business mans who would lie and would want...
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...Misuse of Statistics Studying about misuses of statistics with example: Statistics: Statistics is an aggregate of facts. Individual facts do not constitute statistics.The height of an individual does not constitute statistics. But the heights of 50 students in a class constitute statistics, since they are affected by multiplicity of causes, like age, heritage etc.The facts must be related to some department of enquiry. Collection of facts will not form statistics unless they are subjected to enquiry.Statistical data should be collected in a systematic manner keeping the purpose in view. Statistics means the methods used for collection, classification, analysis and interpretation of numerical data. Statistics is also defined as the science which deals with collection, analysis and interpretation of numerical data. Limitations and Misuses of Statistics with Examples: Statistics can be used only to study numerically valued data. Statistics deals only with aggregate and not with individuals. Statistical data are true only on an average. Statistical data collected for given purpose cannot be applied to any situation. It is not always possible to compare statistical data, unless they are homogeneous in character. Misuses of Statistics with Examples: Statistical methods should be intelligently and carefully used as their misuses may lead to unsatisfactory results and dangerous conclusions. False conclusions will follow if the data collected is incomplete...
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...Stephanie Minshew SOC-1101-51 SPR – 2014 Final Exam: Joel Best - Damned Lies In the book, Damned Lies and Statistics by Joel Best, Best provides information for people to be able to critically think about social statistics. He pinpoints some common complications with social statistics and provides examples to define his points. By providing examples, it makes understanding the problem easier than by just general statements. He gives the reader tools they can use in every day instances regarding statistics. Making sure that the reader knows how to ask basic questions about statistics they hear. This helps us to understand how sometimes numbers can become mangled within communication. That one cannot compare apples to oranges, that comparison must be fair. That we must be analytical, and critical of numbers, but to also not become naïve or cynical. I absolutely loved his explanation for the way society is often innumerate. He provided a perfect example of how many people don’t process the concept of large numbers. As his example he used a small child and a penny. To a child a penny is a lot of money, but to an older child a penny is not a lot of money. In the same aspect, if you ask an older child if one hundred dollars is a lot of money they would most likely agree that a hundred bucks is a lot of money, but to an adult it’s most likely not a lot of money. That to most people big numbers blend together. There are huge implications to being innumerate. Because some...
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...mriff76: Managerial Report Use the numerical methods of descriptive statistics presented in this chapter to learn how these variables contribute to the success of a motion picture. Include the following in your report. 1. Descriptive statistics for each of the four variables along with a discussion of what the descriptive statistics tell us about the motion picture industry. 2. What motion picture, if any, should be considered high performance outliers? 3. Descriptive statistics showing the relationship between total gross sales and each of the other variables. Discuss. Motion Picture/Opening Gross/Total Gross/Number of Theaters/Weeks in Top 60 Coach Carter 29.17 67.25 2,574 16 Ladies in Lavender 0.15 6.65 119 22 Batman Begins 48.75 205.28 3,858 18 Unleashed 10.90 24.47 1,962 8 Pretty Persuasion 0.06 0.23 24 4 Fever Pitch 12.40 42.01 3,275 14 Harry Potter and the Goblet of Fire 102.69 287.18 3,858 13 Monster-in-Law 23.11 82.89 3,424 16 White Noise 24.11 55.85 2,279 7 Mr. and Mrs. Smith 50.34 186.22 3,451 21 Be Cool 23.45 55.81 3,216 8 Modigliani 0.03 0.13 9 4 Flightplan 24.63 89.69 3,424 21 Steamboy 0.14 0.36 46 3 Lost Embrace 0.02 0.05 5 1 Kung Fu Hustle 0.27 17.08 2,503 16 Howl's Moving Castle 0.43 4.61 202 11 War of the Worlds 77.06 234.21 3,910 19 Balzac and the Little Chinese Seamstress 0.02...
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...Stats PaperCara Robertson September 19, 2013 Elements of Statistics MAT121.M2 Jenny Fiedeldey Chatfield College Statistics Paper #1 “It Ain't Necessarily So” Being as interested in news and politics as I am, I was already aware of the fact that statistics are extremely inaccurate. Statistics falsely portray their sample or population to be over exaggerated or under exaggerated. Either way, statistics are basically lies, whether that is the intention or not. Reading “It Ain't Necessarily So” has only further confirmed by beliefs about statistics and their falseness. I had never taken into consideration all of those who are involved in the inaccuracy of said statistics, though. I had always just blamed the news sources for that. However, reading this paper has taught me that the news sources are probably the only people not involved in what is basically a lie; they are just given the information and told to report it. I now know that the victim (or in some cases, so-called “victim”), investigator, and the person collecting the data are the ones who are to blame for the misrepresentation. These false studies are being presented to the public every day, concerning a very wide range of topics. Extreme confusion is caused when the public hears drastically varying numbers and reports concerning things such as presidential approval rates, unemployment rates, and any other topic one might think of. When each news source is reporting entirely different information on...
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...410 of your text (30 pts) Sample 1 1) H0: μ = 12 Ha: μ ≠ 12 2) α = .01, but for two-tail test will = .005 3) Z = (x-bar – μ) / (σ/√n) 4) Z Critical value at .005 = 2.575 5) Z = (11.9587 – 12) / (.21/√30) = -1.077187 The observed value lies outside the rejection region, so we fail to reject H0. 6) P –value is between .2814 for a two-tailed test Sample 2 1) H0: μ = 12 Ha: μ ≠ 12 2) α = .01, but for two-tail test will = .005 3) Z = (x-bar – μ) / (σ/√n) 4) Z Critical value at .005 = 2.575 5) Z = (12.0287 – 12) / (.21/√30) = .74855 The observed value lies outside the rejection region, so we fail to reject H0. 6) P-value is 0.4541 for a two-tailed test Sample 3 1) H0: μ = 12 Ha: μ ≠ 12 2) α = .01, but for two-tail test will = .005 3) Z = (x-bar – μ) / (σ/√n) 4) Z Critical value at .005 = 2.575 5) Z = (11.889 – 12) / (.21/√30) = - 2.895 The observed value lies inside the rejection region, so we reject H0. 6) P-Value is .0038 for a two-tailed test Sample 4 1) H0: μ = 12 Ha: μ ≠ 12 2) α = .01, but for two-tail test will = .005 3) Z = (x-bar – μ) / (σ/√n) 4) Z Critical value at .005 = 2.575 5) Z = (12.081 – 12) / (.21/√30) = 2.11264 The observed value lies outside the rejection region, so we fail to reject H0. 6) P-value is .034 for a two-tailed test 2) Sample 1 Standard Deviation = 0.22035603 Sample 2 Standard Deviation = 0.22035603 Sample 3 Standard Deviation = 0.207170594 Sample 4 Standard Deviation...
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...Math 533: Applied Managerial Statistics Course Project Part C: Regression and Correlation Analy Summary After statistical analysis of data collected on a random sample of AJ Davis’ customers, several inferences can be made. The goal of this analysis was to determine the best model for predicting customer income. This knowledge is paramount in most aspects of the company including but not limited to advertising, sales and merchandising. Based on the analysis, it is determined that a model using customer credit balance and household size is the most efficient. These variables gave the most reliable predictions of customer income. At the close of the analysis, the data yielded the following equation for determining customer income, income== - 1.90 + 0.0173 CREDIT BALANCE($) - 5.30 SIZE - 0.390 YEARS. Though, the number of years a customer has live in their home did not show high effectiveness in predicting income, it increased the overall fit of the model and has been included in the equation. We can be 95% confident in the data obtained through the use of this formula. Appendix 1. 2. Regression Analysis: INCOME($1000) versus CREDIT BALANCE($) The regression equation is INCOME($1000) = 4.45 + 0.00987 CREDIT BALANCE($) Predictor Coef SE Coef T P Constant 4.448 7.037 0.63 0.530 CREDIT BALANCE($) 0.009866 0.001728 5.71 0.000 S = 11.3247 R-Sq = 40.5% R-Sq(adj) = 39.2% Analysis of Variance Source...
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...equation. From the random sample, the value of µ is x=14.92 The confidence level of the based on this sample will be constructed to capture the µ with 95% confidence. The sample size is n=30 and the population standard deviation is 0.55. According to the central limit theorem, because the sample size is equal to 30, the sample mean x approximately follows a normal distribution with a mean µ and a known standard deviation σ. Using the following equation, I can substitute in the given information to find the values for the 95% confidence interval. x±1.96(σn ) When I substitute the given information for the values, I get the interval 14.92-1.96(0.5530) ≤ µ ≤ 14.92+1.96(0.5530). I can be 95% confident that µ lies between 14.72 and 15.12. The Upper Confidence Interval Level is 15.12 and the Lower Confidence...
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...Axia College Material Appendix C--'Dirty Tricks' Exercise--Week 9 Complete the table below using the information from the Course Syllabus: Week 8. |Dirty Trick #/Name |Description/Definition |Why Chosen |***Example from: 2012 Presidential Campaign OR Recent News Story | | | | |OR Other Situation | |# 1- Accuse your opponent |When in the mix of arguing or the person is |I’ve experienced this |One of my ex boyfriends tried to constantly accuse me of cheating| |of doing what he is |Feeling |in a previous |on him and we would have tons of arguments over that. Finally | |accusing you of (or worse) |Like they’re being attacked and losing the argu- |relationship |after a while it seemed like something was really bothering me | | |ment, the person will turn the tables and start | |about how he kept accusing and I knew I wasn’t do anything wrong | | |accusing of the same thing when you didn’t do | |what so ever...
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...Hypothesis Testing Opinions of Social Security System Probability and statistics August 1 2013 1. The Problem In an effort to design better advertising campaigns, the public relations department of the Social Security Administration conducted a survey to find out the opinions people in the United States have about the Social Security System. The public relations department collected and analyzed the survey data and claims that 40% of people in the United States think the Social Security system will have money available to provide the benefits they expect for their retirement. Also, they claim that the average age of people in the United States who would say yes to this question when asked is 60 years or older. The purpose of this study is therefore to examine and test the hypothesis of these claims and determine whether they can be supported or rejected for advertising purposes. 2. The Research Design and Hypothesis To test the hypothesis, a data table was collected from a survey adapted from Newsweek, by the public relations department of the Social Security Administration. The survey consisted of data from 32 adults ranging in age from 18 to 83. The study examines the two hypothesis by utilizing the classical method to construct a condition for testing the hypothesis claimed by the public relations department of the Social Security Administration. In this case, the null hypothesis is 40% of the people in the United States think the Social Security...
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...PART A- Exploratory Data Analysis Introduction & Overview AJ Davis is a department store chain, which has many credit customers and wants to find out more information about these customers. The total sample set of 50 credit customers is selected with data collected. The below data was provided in order to perform the analysis. 1. Location: a. Urban b. Suburban c. Rural 2. Income 3. Household Size (number of people living in the household) 4. Years (the number of years that the customer has lived in the current location) 5. Credit Balance (the customer’s current credit card balance on the store's credit card) Individual Variables Five individual variables were provided for review: Location, Income, Household Size, Years, and Credit Balance. Below is a statistical analysis summarizing the key points referencing Location, Income and Credit Balance. Variable: Location The location of AJ Davis’ customers is distributed between three classes of urban, suburban and rural areas. Of the total number of customer locations in the sample set of 5; 13 are located in rural, 15 in suburban and 22 in urban locations. The pie chart shows that just less than half of all AJ Davis’ customers live in urban (44%) areas, yet customers that live in rural (26%) and suburban (30%) areas are relatively evenly distributed. The rural and suburban areas combine to compromise 56% of AJ Davis’ credit customer base in which they are interested in. This should be useful information to AJ Davis...
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...Estimating the Elasticity of Demand for Gasoline Professor Pushan Dutt The graph below shows the evolution of the price of oil (adjusted for inflation) since 1957. Note a couple of sharp jumps and collapses in the price of oil. 1. 2. 3. 1973: : 2.75 % of global production was withheld; Prices in nominal terms jumped from $3.5 a barrel to $10 a barrel 1979: 5.68 % of global production withheld; Prices in nominal terms jumped from $15 to $32 a barrel. 2007: Oil prices increase from $60 to reach a peak of $128, followed by a rapid collapse Why do we observe such sharp swings in oil prices? The answer lies in the elasticity of demand and supply. For this exercise we will focus on the demand side and estimate the price elasticity of demand for oil. We will focus on the US and use time-series data to estimate this demand function. Here are the somewhat modest goals of the assignment: 1. To convince you that demand functions and elasticities are measurable, rather than purely abstract concepts. 2. To provide practice in manipulating and interpreting demand functions particularly constant-elasticity demand functions 3. To motivate your study of linear regression in UDJ by providing an example of an application. I will lead you through the steps without explaining the statistical methods of regression. Such methods are not part of this course but you will see them in detail in your UDJ core course. © INSEAD Step 1: Write a Model The first, and perhaps...
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... ➢ Chọn tên biến cần kiểm tra ➢ Chọn Ascending hay Descending 2.( Dùng Find để tìm và sửa lỗi: Mở Data view, chọn biến cần sửa, trong Edit chọn Find, nhập giá trị biến cần sửa để tìm số liệu đã nhập sai và sửa (nên tìm ID để kiểm tra) 3. Dùng Frequencies: 1.Vào file dữ liệu trong Data Editor + Vào các folder: * Analysis/Discreptive Statistics/Frequencies ➢ Rà sóat giá trị của biến theo Frequencies hay Tables of Frequencies để tìm số liệu nhầm lẫn ➢ Có thể dùng Find (Edit, Find, tên biến) để tìm và sửa lỗi 3.( Rà sóat giá trị của biến theo Frequencies hay Tables of frequencies để tìm số liệu nhầm lẫn ( Dùng Find (Edit, Find, tên biến) để tìm và sửa lỗi 2.Số liệu phức tạp • Dùng Select cases để tìm lỗi logic : vào Data Editor ➢ Data, Select cases, Chọn biến ➢ Chọn If condition is satisfied ➢ ➢ Chọn If, chọn biến và điều kiện của biến vào cột phải ➢ Continue, OK ← Ở Data Editor, xem cột ngoài cùng bên trái: các phiếu có gạch chéo là không được xử lý ( Muốn trở lại trạng thái ban đầu dùng All cases • Dùng Select cases để tìm lỗi logic : vào Data Editor 1. Data, Select cases, Chọn biến 2. Chọn If condition is satisfied/ 3. 3. If…. Ví dụ trong quá trình nhập gới: nam là 1; nữ là 2. Nhưng trong quá trình nhập có thể nhâp lộn số là 3,4,8…. Để kiếm tra xem có nhập lộn số hay không ta đặt điều kiện cho biến số để phát hiện sai...
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...Fariha Haque Block 5 LA LIT 3 Ms. Gladstone The American Reality The American Dream, a repetitious theme found in literary works dating as far back as the 1600s, was a common misconception. People have held on to these ideals that manipulate and deceive rather than open limitless possibilities. The American Dream prompted people to believe that America was a country that expressed liberty and freedom. The American Dream originated from the Declaration of Independence in which it claimed that “all mean are created equal.”(Academia) and that they are "endowed by their Creator with certain inalienable Rights" including "Life, Liberty and the pursuit of Happiness." (Academia) While embedded into America’s charter, the American Dream makes the country seem more attractive to foreign lands. However, there were many perspectives on how people perceived the “optimal style of living.” Some believed everyone strived to be rich and were able to live in coexistence. These perspectives were further broken in down in “The Death of a Salesman.” Immigrants entered through the gates of Ellis Island throwing away their home country’s established social hierarchies and caste systems. With high hopes, they created schemas of the elite population versus the poor population. Though, through passing generations, they are only met with disappointment. They cling onto the possibility that life will become normal again and remain in a constant cycle of false hope. These multiple realities have...
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...There are a number of possible ways in which unethical behavior can arise in statistics and researchers should steer clear of these. It is relatively simple to manipulate and hide data, projecting only what one desires and not what the numbers actually speak, thus giving birth to the famous phrase "Lies, damned lies and statistics". However, this doesn't happen all the time and there is no reason not to believe in the conclusions of a statistical analysis. Ethics in statistics is not straightforward and can be quite complex at times. It also greatly depends on what kind of statistical analysis is being done. Unethical behavior might arise at any point - from data collection to data interpretation. For example, data collection can be made inherently biased by posing the wrong questions that stimulate strong emotions rather than objective realities. This happens all the time when the survey is aimed to try and prove a viewpoint rather than find out the truth. Other unethical behaviors might include scientists not including data outliers in their report and analysis to validate their theory or viewpoint. This happens both in pure and social sciences. By obscuring data or taking only the data points that reinforce a particular theory, scientists are indulging in unethical behavior. Ethics in statistics are very important during data representation as well. Numbers don't lie but their interpretation and representation can be misleading. For example, after a broad survey of many customers...
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