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Statistics, 2005. A definition of statistics and explanation of the statistical process. 4,594 words (approx. 18.4 pages), 5 sources, MLA, $ 119.95 »
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Abstract This paper focuses on statistics by explaining the statistical process and the primary purpose of statistical processes and then outlining the best practices for statistical procedures. The paper also explains the purpose of statistics and how they are used for product research.
Introduction
Statistics and Their Importance to Research Investigation
Correct Statistical Processes
Summary
From the Paper "Before there can exist any intelligent discussion with respect to the topic of statistics one must understand that a statistical process does not stand alone nor does it function without being a part of a much larger plan, namely, research investigation as a whole. Statistics and their accompanying processes are only one such part of the research plan and, as such, must be viewed in totality of purpose over single identification. Without a formidable research plan a statistical process is without merit and akin to discussing how many angles can be placed on the head of a pin. In general, and from a philosophical perspective, the research plans and statistical analysis, according to Ohlson (1998) "...are not unlike an artist's canvas, as they strive to capture forever the intrinsic and observable subject placed before it" (10)."
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Statistics, 2002. Examines a variety of statistical procedures and shows how statistics analysis company, Polk Company, applies some of them for their analytical objectives. 6,284 words (approx. 25.1 pages), 4 sources, APA, $ 146.95 »
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Abstract Statistics refers to the processes of collecting, organizing, analyzing and presenting data in forms usable for policy analysis, decision-making and other important tasks confronting people and organizations in contemporary society. It is within this framework that Polk Company, one of America's oldest and largest consumer marketing firms, operates.
This study considers the application at the Polk Company of 11 tasks associated with the processes of collecting, organizing, analyzing and presenting data. In each instance, the data management or statistical analysis function is defined, the learning process is explained within the context of the Kolb Model, and an illustration of the application of the data management or statistical analysis function is presented. The 11 data management or statistical analysis functions are (1) organizing data, (2) averages and variations, (3) elementary probability theory, (4) normal distribution, (5) binomial distribution, (6) sampling distribution, (7) estimation, (8) hypothesis testing, (9) regression and correlation, (10) chi square and analysis of variance (ANOVA) which is based on the F statistic and (11) non-parametric statistics.
From the Paper "Type 1 learners, when working with hypotheses, tend to review available data without bias and study and consider the data from a variety of perspectives to develop workable hypotheses related to analytical objectives. Type 2 learners would approach the task by developing theoretical models upon which to base hypotheses, and then study and consider the data from a variety of perspectives in which model best supports the development of workable hypotheses. Type 3 learners would approach the task by developing theoretical models upon which to base hypotheses, and then experiment with alternative hypotheses to determine how best to achieve analytical objectives. Type 4 learners would review available data without bias, and then experiment with alternative hypotheses to determine how best to achieve analytical objectives."
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Inferential Statistics, 2005. This paper discusses the field of inferential statistics and its application. 2,435 words (approx. 9.7 pages), 15 sources, APA, $ 74.95 »
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Abstract This paper explains that psychologists use statistics to make sense of the human behaviors; through observation measurement and statistical inference, researchers are able to take the abstract and make it more understandable. The author points out that, in inferential statistics, researchers use probability to make generalizations about the entire population based on the results from the research sample. The paper relates that statistics can be used to dictate public policy; thereby, it is especially important that mis-measurements do not occur particularly by drawing samples from non-normative or incomplete populations; while seemingly straightforward, statistics require judicious application of ethical behavior. 2 figures. 6 tables.
Table of Contents
History
Descriptive and Inferential Statistics
Samples and Populations
Probability
Data
Measures of Central Tendency
Central Limit Theorem
Hypothesis Testing
Related Samples
Correlation of Pre and Post Test
What Does Is All Mean?
From the Paper "Some of the earliest work in statistics was done by Sir William Perry in 1532, when he began to record the number of deaths in London on a weekly basis. Later in the 1600, James Bernoilli, a Swiss mathematician, begin using probability to predict outcomes. In the 1700s, it was Thomas Bayer who gave birth to the concept of inferential statistics. The normal distribution was discovered in 1733 by a Huguenot refugee de Moivre as an approximation to the binomial distribution when the number of trials is too large. Today, not only do scientists but also many professions rely on statistics to understand behavior and ideally make predictions about what circumstances relate to or cause these behaviors."
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Statistics Anxiety, 2006. A research paper on how statistics anxiety affects graduate students in the social sciences. 2,790 words (approx. 11.2 pages), 45 sources, APA, $ 83.95 »
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Abstract Statistics anxiety has been defined simply as anxiety that occurs as a result of encountering statistics in any form and at any level. The paper shows that higher anxiety in statistics keep many students away from engaging in research work to pursue an academic career. Statistics becomes one of the most anxiety-inducing courses in their programs of study. The paper examines the problem and shows how it affects students.
Paper Outline:
Introduction
Empirical Research on Statistical Anxiety
Three Common Factors of Statistical Anxiety
Framework/Model to Reduce Statistical Anxiety in Counselor Education
Conclusion
Impact/Benefits for Students Seeking Ph.D.
From the Paper "A variety of peer learning environment designs to support effective collaborative learning has been attempted or proposed. The backbone of collaborative learning is the willingness of the peers to participate in collaboration in a constructive sense. This has been studied by a number of educational psychology researchers [Madden & Slavin1983,Slavin1978] who confirm that the peers in collaborating classes felt that their peers wanted them to learn. Slavin [Slavin1990] reports studies that confirm the willingness of peers to make the collaborative learning efforts succeed and the improvement in social status of the peers who achieved better than other peers."
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Statistics: An Essay on its Use in Everyday Life, 2001. This paper defines statistics and shows the numerous ways statistics is applied to everyday life and why it is useful. 1,500 words (approx. 6.0 pages), 9 sources, $ 49.95 »
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From the Paper "Statistics is a branch of mathematics dealing with the collection, organization and analysis of numerical data the application of this information to make informed decisions in a variety of applications. Statistical results may be used to forecast business trends, define the extent of prevailing opinion throughout a given population, changes in availability of resources or assets, and provide quantifiable answers to questions in almost every type of business, social or political area. (Encarta) Professor Edwards of the Andover Theological Seminary defined statistics as ?the ascertaining and bringing together of those facts which are fitted to illustrate the conditions and prospects of society."
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Statistics Anxiety, 2006. An analysis of the imapct of statistics anxiety on graduate students. 1,200 words (approx. 4.8 pages), 43 sources, MLA, $ 41.95 »
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Abstract This paper studies how graduate students perceive the study of statistics and the impact that their anxiety about the subject matter has on their overall performance. The paper cites several research studies which indicate that statistics anxiety is quite high. Furthermore, the paper proves that this anxiety significantly erodes the overall quality and level of the students' research projects. The paper then offers suggestions to improve the teaching of statistics, as well as other suggestions to strengthen students' skills at statistical analysis.
From the Paper "Statistics anxiety has been defined simply as anxiety that occurs as a result of encountering statistics in any form and at any level (Onwuegbuzie, DaRos, & Ryan, 1997), and has been found to negatively affect learning (Onwuegbuzie & Seaman, 1995). Many researchers (Lazar, 1990; Lalonde & Gardner, 1993; Onwuegbuzie, 2000b) suggested that learning statistics is as difficult as learning a foreign language. On the other hand, statistics anxiety sometimes is not necessarily due to the lack of training or insufficient skills, but due to the misperception about statistics and negative experiences in a statistical class. For instance, students often think they do not have enough mathematics training so that they cannot do well in statistical classes. With fear of failing the course, they delay enrolling in statistics courses as long as possible, which often leads to failure to complete their degree programs (Onwuegbuzie, 1997). The lack of self-efficacy and higher anxiety in statistics keep many students away from engaging in research work or further to pursue an academic career. Therefore, statistics becomes one of the most anxiety-inducing courses in their programs of study (Blalock, 1987; Caine, Centa, Doroff, Horowitz, & Wisenbaker, 1978; Schacht & Stewart, 1990; Zeidner, 1991)."
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Official Statistics, 2002. A look at the use and misuse of "official statistics" in the public and private spheres. 1,400 words (approx. 5.6 pages), 5 sources, $ 53.95 »
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Abstract This paper explores the use and availability of official statistics, particularly for business. The phrase "official statistics" is thoroughly defined, and the paper gives examples of industries and departments in which official statistics are commonly used.
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Statistics in a Manufacturing Facility, 2002. Looks at the importance of statistics to a manufacturing facility when addressing economic and performance concerns. 2,150 words (approx. 8.6 pages), 2 sources, $ 80.95 »
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Abstract The practice of statistics in a manufacturing facility is incredibly important for two key reasons: Statistics help to address economic concerns and the functionality of equipment. This paper examines these two specific areas in respect to how statistics are necessary to promote the best interests of the manufacturing facilities. This paper takes the form of an applied knowledge report, where the materials are examined and are then demonstrated in their practice.
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Business Statistics, 2006. A discussion on the importance of statistics in business. 1,508 words (approx. 6.0 pages), 6 sources, MLA, $ 49.95 »
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Abstract This paper begins with the reasons why statistics are crucial to running a successful business. It continues to review statistical terms through example and graphic representation. The paper also defines probability and distribution and examines how they can be used to the advantage of an organization.
Table of Contents:
Part 1 - Business Statistics
Part 2 - Mean, Median Mode & Standard Deviation Dayton OH Temperatures
Part 3 - Permutations and Combinations
Part 4 - Probability
Part 5 - Probability Distributions
Part 6 - Normal Distribution
References
From the Paper "Probability can be used in business to help determine the likelihood that certain future events will occur (Arsham, 1994). It can also help executives base decisions on the most probable positive outcome given multiple scenarios to choose from. Probability analysis is a widely used business tool for evaluating scenarios in a business environment.
Probability is the act of determining the likelihood that given phenomena will occur without relying solely on random guessing. Probability thus helps managers and other business executives determine the best course of action given multiple alternatives (Arsham, 1994). Probability can also be used to determine the likelihood that adverse events will happen, such as the likelihood that certain threats to corporate assets will be realized within a designated time frame. "
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Descriptive Statistics, 2006. A discussion regarding the use of descriptive statistics and various common errors. 675 words (approx. 2.7 pages), 3 sources, $ 26.95 »
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Abstract This document discusses a series of descriptive statistics questions. These range from the four types of errors related to measurement as well as response and non-response related errors. Finally, the paper makes several statistical calculations in order to establish the efficacy and practicality of descriptive statistics. Specifically, these problems rectify issues of accuracy and estimation.
From the Paper "The four major sources of measurement error are respondent, situation, measurer, and instrument. Respondent errors might occur through respondent misinterpretation of a given question or, in the case of a written survey or questionnaire, actual response error (Lomax, 2001, pp.29-31). A situational error would occur when, for example, a political survey for a given district was being taken in another district. Measurer originated errors can occur in several ways from construction and design of the actual study to poor selection of participants as well as misinterpretation of study results or actual errors in compilation of results. Instrument, such as surveys or questionnaires, often occur because they question or respond to something other than what is being tested or researched. "
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Why Are Statistics So Alluring?, 2002. A description the different ways that statistics can be skewed to sway public opinion and also the ways that people misinterpret them. 1,576 words (approx. 6.3 pages), 6 sources, MLA, $ 51.95 »
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Abstract This paper discusses how people, in general, like to get a visual picture of what they are hearing about and how, through the media and constant representation of statistical data as hard fact, numbers can control people's opinions on issues. It shows how one of the largest issues regarding statistics and their appealing nature is the fact that most of us are innumerate. It also shows how, in addition to innumeracy, the public?s opinion of ideas often leads to skewed views on issues; statistics can become so alluring to activists that they can say something that will change a large group of people's minds on an issue, and then they will get what they want.
From the Paper "Although even though some statistics are wrong, people want to believe them so bad that they will ignore all logic just so that they will have a numerical view of the situation. Perhaps the biggest real life example of this is a social statistic that Joel Best-in his book Damned Lies and Statistics-describes as "The worst social statistic ever... Every year since 1950, the number American children gunned down has doubled" (Best 1). To anyone using this statistic to promote gun control, this statistic is gold, and it sounds believable too. But if you analyze it you'll find otherwise."
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Statistics in Everyday Life, 2004. Describes ways in which statistics are applied in everyday life and how they are used and abused. 1,437 words (approx. 5.7 pages), 6 sources, APA, $ 47.95 »
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Abstract Statistics are a valuable aspect of scientific research. This paper demonstrates many different ways in which scientific data is used in everyday life. By the same token, statistics may also be abused to reach a false conclusion, and this is also illustrated through several cases.
From the Paper "Just as statistics help in prosecution and defense of cases, they may also be abused when different experts in the same field offer different evidence and conclusions from examining the same data. Another way in which statistics may be abused is through corruption; in this case the police investigators may take DNA sample from a suspect and introduce it to a crime scene."
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Problems in Statistics Education, 2004. Critical analysis of a current problem in the field of statistics education. 2,681 words (approx. 10.7 pages), 20 sources, MLA, $ 80.95 »
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Abstract This paper examines some of the ways to teach statistics that will best overcome some of the main problems that students encounter while learning statistics and offers solutions to these problems.
From the Paper "Students do not normally encounter statistics until they are in college--at least not on any kind of practicable level--unless they are in extremely advanced mathematics classes at their high school. Even so, not every high school offers statistics as a course, while almost every college does. Teaching and learning statistics is problematic for most college students and teachers because to learn and understand statistics, it is necessary to first have a grasp of some of the properties and features of higher mathematics. Many college students do not have these skills upon entering college, and many professors assume that they do have these skills when beginning to teach a statistics course."
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Inferential Statistics vs. Single Case Designs, 2004. This paper compares inferential statistics to single case designs and comments on the usefulness of each of these techniques for the practitioner. 1,117 words (approx. 4.5 pages), 5 sources, MLA, $ 38.95 »
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Abstract The current paper comments on a quote by J.M Johnson and H.S Pennypacker that compares inferential statistics to single case designs. It discusses how the statement in question calls to account psychology as a science to abandon a traditional reliance on inferential statistics and group design research and to support its practical focus on the individual with reliance on more individually focused research.
From the Paper "Inferential statistics is a branch of statistics that consists of generalizing from samples to populations, performing hypothesis testing, determining relationships among variables, and making predictions (Tabachnick & Fidell, 2001). This type of research is the most widely used in psychology, with the majority of published studies in this field relying solely on the use of group comparisons. The major argument for the use of inferential statistics is that it increases the external validity of the study and increases the likelihood of the results generalising to other people. These results are gathered by statistical inference where various methods of calculation are used to find a central tendency within a group. The variability of the results between subjects are often explained by measurement error or extraneous variability because well conducted group research introduces the only variability deliberately as an intervention designed to examine and measure its effect(s) on subjects (Hayes, Barlow & Nelson-Gray, 1999)."
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