an example of discrete data is the number of animals. I am using quantitative data which has numerical values rather than qualitative data such as colors. This makes it easier to analyze the data and come to a conclusion. I will also be excluding outliers and anomalies which make my data more representative. The process is to collect data from a population of 264 animals including 19 mammals and 31 amphibians because it is neither large nor small and therefore giving me a clear concise result of the
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There are several types of bad statistics that can be seen when looking at statistical data. According to the video “Don’t be fooled by bad statistics” (2010)‚ there are three basic types of bad data consisting of poorly collected data‚ leading questions‚ and misuse of center. Poorly collected data can produce misleading results. For example‚ when a publishing company conducted a phone survey of popular magazines but did so during business hours when stay at home moms were most likely to participate
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CHAPTER 4 – THE BASIS OF STATISTICAL TESTING * samples and populations * population – everyone in a specified target group rather than a specific region * sample – a selection of individuals from the population * sampling * simple random sampling – identify all the people in the target population and then randomly select the number that you need for your research * extremely difficult‚ time-consuming‚ expensive * cluster sampling – identify
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Johnnie Cochran: An Outlier By: Ryan Starr Johnnie Cochran was an infamous American lawyer‚ who gained recognition from his highly publicized and controversial cases as a successful defense attorney. Born as an African-American on October 2‚ 1937 in Shreveport‚ Louisiana‚ Cochran grew up facing extreme racial prejudice and learned valuable life experience at a young age (Cochran Biography 1). Turning a deaf ear to discrimination‚ Cochran did well in school and got good grades. His father and
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and Secretary of JCP. Prior to joining the company‚ she served as Senior Vice President and General Counsel and Chief Compliance Officer of US Airways Group‚ Inc. and US Airways‚ Inc. Dhillon was with the law firm of Skadden‚ Arps‚ Slate‚ Meagher & Flom LLP from 1991 to 2004 (J. C. Penney Company‚ Inc.‚
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Overview: Chapter 2 Data Mining for Business Intelligence Shmueli‚ Patel & Bruce Core Ideas in Data Mining Classification Prediction Association Rules Data Reduction Data Visualization and exploration Two types of methods: Supervised and Unsupervised learning Supervised Learning Goal: Predict a single “target” or “outcome” variable Training data from which the algorithm “learns” – value of the outcome of interest is known Apply to test data where value is not known and will be predicted
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Criminology is the study of crime and criminals. This study requires a lot of research and knowledge of criminal statistics‚ sociology of law‚ victimology and the criminal behavior system. All of these examples of topics of crimes are different. Their information is based on different sources or is put together using different techniques. The most interesting towards me would be the criminal behavior system. The Criminal Behavior System is determining the nature and cause of specific crime patterns
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errors are also likely Outliers and anomalies distort the mean of the data taking it to either of the two extremes. To avoid any Outliers or anomalies affecting the accuracy of this study‚ I will remove them before taking the sample size of around 80-100 students and I will be using stratified sampling so each category categorized by gender‚ age and maths set have a equal proportion in the sample as in the total population so the results are as accurate as possible. Any outliers which I may have missed
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entries) of 2270‚ is only very slightly larger than the median (the data at the middle of the sample)‚ and the mode (the data entry that occurs with the greatest frequency) which are both the same at 2207. This represents a very slight affect by the outliers at the high and low ends of the data sample‚ indicating that the mean presents the most accurate description of the data set. (Larson & Farber‚ 2011. pgs. 66‚ 67‚ 68). [See Exhibit A]. The range of the data set‚ 3138‚ represents the difference
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examining these plots from the combined data for Meyer and Trauth‚ there is presence of potential outliers in all the years. There is a presence of potential outliers in all 5 years in Surround. On the other hand‚ they are only present in years 1985 and 1988 for Tri-County. For the potential outliers in Surround‚ they are all the maximum values of the bid price variable. However‚ Tri-County has a potential outlier in 1988 which is the minimum value of the bid price variable.
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