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Practice of Statistics
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Recommended: Practice of Statistics
The Lady Tasting Tea
The different tastes between pouring milk into tea or tea into milk raised R.A. Fisher’s interest to design an experiment for testing the lady. Dr. David Salsburg used this famous anecdote as the book title, and elaborated the development of modern statistics by several stories. Each chapter contains one outstanding statistician and his/her contributions. Impressively, the whole book was linked by R.A. Fisher, K. Pearson, E. Pearson and J. Neyman, these exclusively distinguished statistician, which indicates their fame and masterpiece has great impact on not only statistics but also academia of science, even our daily life.
Probability defined by Aristotle, “improbable things will happen”. Later on, the probability and
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Savage and Bruno de Finetti, the confidence interval was justified by the Bayesian theory of personal probability. Bayes’s theorem is separated from Pearson’s probability, and the idea of inverse probability is sort of taboo in statistics academia. That is the reason why confidence interval was disregarded by the chair. The personal probability and Bayesian Hierarchical model are two approaches more and more prevalent than Pearson’s age.
Kolmogorov furthered the probability and statistics into real life and made it more suitable than “pure” mathematics. He inaugurated the axiomization of probability theory and setup the abstract space of elementary things as “events”. This practical axiom made forward movement of probability measurement in reality.
After reading this book, I am touched by the underlying philosophy of statistics. Various theories and models are introduced in this book. During the progress of the development, controversies and confits among these theories are largely attributable to diversity of ideology and doctrine from their establishers. In future, the statistics might evolve into a new era and the vogue methods, like p-value or confidence interval, might be discarded. I am looking forward to witness how statistics make our lives
Renaud, R. (2014a, April 10). Unit 10 - Understanding Statistical Inferences [PowerPoint slides]. Retrieved from the University of Manitoba EDUA-5800-D01 online course materials.
The goal of this experiment is to see if more college students prefer Diet Coke or a bargain brand Diet Coke. A single blind taste test was given to everyone in the class to determine which soda they like better. A Bernoulli distribution was used to determine which brand was conducted to each participant first. This process insures that the samples given to the subjects are random as possible and to cut any bias. The participants were given the two random samples and were asked to tell which was liked better. The results of this test were recorded then analyzed. From the data, the statistic of people who preferred the Bargain brand was 6/20. This means that the majority of the subjects preferred the real Coke brand over the Bargain brand. A hypothesis test was conducted to test the hypothesis that Diet Coke and a Bargain brand coke are equally
List specific economic, social, political, legal and technological factors that could affect the success of flavored iced teas.
Inferential statistics establish the methods for the analyses used for conclusions drawing conclusions beyond the immediate data alone concerning an experiment or study for a population built on general conditions or data collected from a sample (Jackson, 2012; Trochim & Donnelly, 2008). With inferential statistics, you are trying to reach conclusions that extend beyond the immediate data alone. For instance, we use inferential statistics to try to infer from the sample data what the population might think. A requisite for developing inferential statistics supports general linear models for sampling distribution of the outcome statistic; researchers use the related inferential statistics to determine confidence (Hopkins, Marshall, Batterham, & Hanin, 2009).
While Confidence Intervals are used when polling sample data to make a decision for a bigger population, Denis Cousineau 92017), states that the golden rule for Confidence Intervals is “If a given value is within the interval of a result, the two can be informally assimilated as being comparable.” This applies when interpreting all intervals.
Discussion of inconsistencies in the data, and potential biases in reference to the methods are included
Statistics Finland -. (n.d.). Tilastokeskus - Statistikcentralen - Statistics Finland. Retrieved March 2, 2012, from http://www.stat.fi/til/vaerak/2010/vaerak_2010_2011-03-18_kuv_005_en.html
Also, the title of the article states the research is a “population study” which is a focus of a quantitative research and a component of a quantitative method. Furthermore, the authors specified a clear defined research purpose which often requires statistical methods to test the hypotheses as well as to look for the cause and effects of the variables so that predictions can be
Joel Best’s Damned Lies and Statistics is a book all about recognizing statistics that are legitimate and others that are really quite horrible. The goal of this book is not that the average every day person be able to read a statistical table from a scholarly journal, but rather that anyone could personally value a statistic he or she may come across in a newspaper article or on a news program. Best was essentially effective in achieving his goal; however, he was effective to the point of overdoing his job of showing that there are bad statistics which give readers cause to evaluate them outside of hearing them on the news.
Tea had started in China but soon spread to Europe where it was consumed by almost everyone. Tea was amazing and rather impressive, but to the Chinese Tea caused their empire to fall and many wars. considering tea contained caffeine it had the same effects as coffee, it was just more enjoyable and more consumed. To this day the British still enjoy tea and is consumed more than 66 liters per person per year. With the rise of tea the British grew with it and became economically and militarily great. The popularity in Europe was immense; official imports had grown from around six tons in 1699 to eleven thousand tons a century later. These numbers do not include the amount of smuggled tea, which at the time was popular. During the Industrial Revolution,
When we are introduced to statistics we either face it or deal with it head-on despite our fear with this subject and we start thinking about the time it would take us to complete a paper or statistics design bases on the extended reading we would have to do in order to understand the subject for clarification of what to expect, and take away from that subject. Therefore, this discussion will define confidence intervals, stipulate when we would need to use confidence intervals in statistical analysis, and examine why the Publication Manual of the American Psychological Association recommends the inclusion of confidence intervals in study results.
...en Goldachre. (2011). The statistical error that just keeps on coming. Available: http://www.guardian.co.uk/. Last accessed 10/12/2011.
In the health care industry, gathering information in order to find the best diagnosis route or even determine patient satisfaction is necessary. This is complete by conducting a survey and collecting data. When the information is complete, we then have statistical information used to make administrative decision within the healthcare field. The collection of meaningful statistics is an important function of any hospital or clinic.
This paper discusses different types of sampling techniques used in quantitative research. It begins by looking at probability sampling (also known as random sampling) before discussing non-probability sampling (non-random sampling). The discussion ends by looking considerations that should be made before selecting a sampling technique before concluding. Because quantitative researchers prefer probability sampling and only use non-probability on rare occasions the e...
Whether or not people notice the importance of statistics, people is using them in their everyday life. Statistics have been more and more important for different cohorts of people from a farmer to an academician and a politician. For example, Cambodian famers produce an average of three tons or rice per hectare, about eighty per cent of Cambodian population is a farmer, at least two million people support party A, and so on. According to the University of Melbourne, statistics are about to make conclusive estimates about the present or to predict the future (The University of Melbourne, 2009). Because of their significance, statistics are used for different purposes. Statistics are not always trustable, yet they depend on their reliable factors such as sample, data collection methods and sources of data. This essay will discuss how people can use statistics to present facts or to delude others. Then, it will discuss some of the criteria for a reliable statistic interpretation.