For example, if a sample survey reports the percentage in the sample who favor a particular candidate to be 55 percent and gives a 95 percent confidence interval as 52 to 58 percent, the meaning is that a procedure has been followed that gives an interval that covers the true population percent 95 percent of the time See our collection of statistics research paper examples. These example papers are to help you understanding how to write this type of written assignments. Statistics is a branch of mathematics dealing with the collection, organization, analysis, interpretation and presentation of data. In applying statistics to, for example, a scientific Paper Anatomy Methods: Establish notation Outline methods/results (use appendix for details) The methods section is often the rst part of a paper that I write. Evaluation: Simulations (validity) Relative ffi (comparison) Simulations can be non-informative (so your method seem to work) 11
Statistics Research Paper Examples - EssayEmpire
This sample Statistics Research Paper is published for educational and informational purposes only. If you need help writing your assignment, please use our research paper writing service and buy a paper on any topic at affordable price. Also check our tips on how to write a research papersee the lists of research paper topicsand browse research paper examples. Statistics is a discipline that deals with data: summarizing them, organizing them, finding patterns, and making inferences.
During the twentieth century, as a result of the work of Karl Pearson, Ronald Fisher, Jerzy Neyman, Egon Pearson, John Tukey, and others, the term came to be used much more broadly to include theories and techniques statistics research papers examples the presentation and analyses of such data and for drawing inferences from them.
Two works by Stephen Stigler, The History of Statistics: The Measurement of Uncertainty before and Statistics on the Table: The History of Statistical Concepts and Methods offer broad and readable accounts of the history of statistics. Although often taught in departments of mathematics, statistics is much more than a branch of applied mathematics, statistics research papers examples.
It uses the mathematics of probability theory in many of its applications and finite mathematics and the calculus to derive many of its basic theoretical concepts, but it is a separate discipline that requires its practitioners to understand data as well as mathematics. In a sense, statistics is mainly concerned with variability. If every object of the same class were the same, we would have no need for statistics.
If all peas were indeed alike, we could measure just one and know all about peas. If all families reacted similarly to an income supplement, we would have no need to mount a large scale negative income tax experiment. Variability, however, is a fact of life and so statistics is needed to help reveal patterns in the face of variability. Statistics is used in the collection of data in several ways.
If the data are to be collected via an experiment, statistical theory directs how to design that experiment in such a way that it will yield maximum information. If the data are to be collected via a sample survey, the principles of probability sampling ensure that the findings can be generalized to the population from which the sample was drawn. Variations on simple random sampling which is analogous to drawing numbers out of a hat take advantage of known properties of a population in order to make the sampling more efficient.
The technique of stratified sampling is analogous to blocking in experimental design and takes advantage of similarities in units of the population to control variability, statistics research papers examples. Once data are collected, via experiments, sample surveys, censuses, or other means, they rarely speak for themselves.
There is variability, owing to the intrinsic variability of the units themselves or to their reactions to the experimental treatments, or to errors made in the measuring process itself. Statistical techniques for measuring the central tendency of a variable e.
Measures of the variability of a variable e. These numerical techniques work hand in hand with graphical techniques e. Indeed, using numerical summaries without examining graphical representations of the data can often be misleading. Of course, there are many more complicated and sophisticated summary measures e. Much of modern data analysis, especially as developed by John Tukey, relies on less conventional measures, on transformations of data, and on novel statistics research papers examples techniques.
Such procedures as correspondence analysis and data mining harness the power of modern computing to search for patterns in very large datasets. Perhaps the most important use of statistics, however, is in making inferences. One is rarely interested merely in reactions of subjects in an experiment or the statistics research papers examples from members of a sample; instead one wishes to make generalizations to people who are like the experimental subjects or inferences about the population from which the sample was drawn, statistics research papers examples.
There are two major modes of making such inference. Classical or frequentist inference the mode that has been most often taught and used in the social sciences conceptualizes the current experiment or sample as one from an infinite number of such procedures carried out in the same way. It then uses the principles codified by Fisher and refined by Neyman and Pearson to ask whether the differences found in an experiment or from a sample survey are sufficiently large to be unlikely to have happened by mere chance.
Specifically it takes the stance of positing a null hypothesis that is the opposite of what the investigator believes to be true and has set out to prove. If the outcome of the experiment or the sample quantity or one more extreme is unlikely to have occurred if the null hypothesis statistics research papers examples true, then statistics research papers examples null hypothesis is rejected.
Conventionally if the probability of the outcome or one more extreme occurring when the null hypothesis is true is less than. Frequentists also carry out estimation by putting a confidence interval around a quantity measured from the sample to infer what the corresponding quantity in the population is.
For example, if a sample survey reports the percentage in the sample who favor a particular candidate to be 55 percent and gives a 95 percent confidence interval as 52 to 58 percent, the meaning is that a procedure has been followed that gives an interval that covers the true population percent 95 percent of the time. The frequentist does not know and is not able to put a probability on whether in any particular case the interval covers the true population percent—the confidence is in the procedure, not in the interval itself.
Further, the interval takes into account only what is known as sampling error, the variation among the conceptually infinite number of replications of the current procedure. It does not take into account non-sampling error arising from such problems in data collection as poorly worded questions, nonre-sponse, and attrition from a sample.
In statistics research papers examples for these mechanisms of classical statistics to be used appropriately, a probability mechanism probability sampling or randomization must have been used to collect the data. In the social sciences this caution is often ignored; statistical inference is performed on data collected via non-probabilistic means and even on complete enumerations.
There is little statistical theory to justify such applications, although superpopulation models are sometimes invoked to justify them and social scientists often argue that the means by which the data were accumulated resemble a random process. Since the s there statistics research papers examples been a major renewal of interest in what was historically called inverse probability and is currently called Bayesian inference after the English nonconformist minister and—during his lifetime— unpublished mathematician Thomas Bayes [?
Note that Bayesians do speak of the probability of a hypothesis being true while frequentists must phrase their conclusions in terms of the probability of outcomes when the null hypothesis is true. For a frequentist, statistics research papers examples, a population parameter is a fixed, albeit usually unknown, constant. Much of the revival of interest in Bayesian analysis has happened in the wake of advances in computing that make it possible to use approximations of previously intractable models.
While the distinction between Bayesians and frequentists has been fairly sharp, as Stephen E. Fienberg and Joseph B.
Kadane note the two schools are coming together, with Bayesians paying increasing attention to frequentist properties of Bayesian procedures and frequen-tists increasingly using hierarchical models. Two much more detailed descriptions of the field of statistics and its ramifications than is possible here are given by William H. Kruskal and Fienberg and Kadane Free research papers are not written to satisfy your specific instructions.
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Paper Anatomy Methods: Establish notation Outline methods/results (use appendix for details) The methods section is often the rst part of a paper that I write. Evaluation: Simulations (validity) Relative ffi (comparison) Simulations can be non-informative (so your method seem to work) 11 For example, if a sample survey reports the percentage in the sample who favor a particular candidate to be 55 percent and gives a 95 percent confidence interval as 52 to 58 percent, the meaning is that a procedure has been followed that gives an interval that covers the true population percent 95 percent of the time How To Write A Statistics Research Paper?
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