According to Chesemore (2011), statistics is concerned with methods based on mathematics theory and probability, which allows the user to summarize many observations concisely. The first recorded use of statistics was in the 16th century; it was used by gamblers and life insurance companies. The use and development of statistical methods has greatly expanded in the 20th century. The invention of the computer simplified calculations (Chesemore, 2011). In addition to learning that statistics is used more often than I thought, I have learned the methods, calculations, the theories behind the formulas, and the requirements for each testing method.
Descriptive statistics is a system which is used to understand large sets of data. Tools include graphs, charts, and histograms. Data is plotted to determine the distribution, but descriptive statistics is limited to measures of central tendency, mean, mode, median, and standard deviation (Weinclaw, 2009). Descriptive statistics is very basic; there is no testing or predicting based on testing the data. It can be used to determine if a sample is normally distributed. The next level of statistics is inferential statistics.
Inferential statistics is much more complex than descriptive statistics and is used for analysis and making inferences based on hypothesis testing to determine statistical significance of calculated values. Inferential statistics uses a vast array of testing procedures. The tests which are used depend on how the test is set up and what the user wants to discover (Weinclaw, 2009).
A simple test to determine if a sample is representative of a population is the z-test. The z-test is heavily used to determine if the sample is representative of a population. After z has been ...
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...let down that the equations needed to pick the winning powerball numbers were not included in the course.
Works Cited
Chesemore, D. (2011). Statistics. Salem Press Encyclopedia of Science. Retrieved from http://search.ebscohost.com.proxy-library.ashford.edu/login.aspx?direct=true&db=ers&AN=89317225&site=eds-live
Tanner, D. E., & Youssef–Morgan, C. M. (2013). Statistics for Managers. San Diego, CA: Bridgepoint Education, Inc.
Wienclaw, R. (2009). Research Starters Sociology (Online Edition). Descriptive Statistics (Sociology). Retrieved from http://search.ebscohost.com.proxy-library.ashford.edu/login.aspx?direct=true&db=ers&AN=89185422&site=eds-live
Wienclaw, R. (2009). Research Starters Sociology (Online Edition). Inferential Statistics. Retrieved from http://search.ebscohost.com.proxy-library.ashford.edu/login.aspx?direct=true&db=ers&AN=89185545&site=eds-live
Collected data were subjected to analysis of variance using the SAS (9.1, SAS institute, 2004) statistical software package. Statistical assessments of differences between mean values were performed by the LSD test at P = 0.05.
A researcher determines that 42.7% of all downtown office buildings have ventilation problems. Is this a statistic or a parameter; explain your answer.
Many statistical ideas were mentioned in the Barron’s guide. In the topic called Graphing Display the Barron’s guide discusses the different types of graphs, measures of center and spread, including outliers, modes, and shape. Summarizing Distributions mentions different ways of measuring the center, spread, and position, including z-scores, percentile rankings, and the Innerquartile Range, and its role in finding outliers. Comparing Distributions discusses the different types of graphical displays and the situations in which each type is most useful or appropriate. The section on Exploring Bivariate Data explains scatter plots in depth, discussing residuals, influential points and transformations, and other topics specific to scatter plots. Conditional relative frequencies and association, and marginal frequencies for two-way tables were explained in the section entitled Exploring Categorical Data. Overview of Methods of Data Collection explained the difference between censuses, surveys, experiments, and observational studies. Surveys are discussed more in depth in Planning and Conducting Surveys, including characteristics of a well-designed and well-conducted survey, and sources of bias. Planning and Conducting Experiments explains experiments in depth; going over confounding, control groups, placebo effects, and blinding, as well as randomization. Basic rules for probability are discussed in Probability as Relative Frequency, including the law of large numbers, addition rule, and multiplication rule. Other topics discussed in this section include the different types of probability calculations. Combining Independent Random Variables discusses manners in which two variables can be compared to each other and things to be wary of while doing so.
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).
I currently work for an Occupational Health Clinic at a research hospital, therefore nearly everything we do is related to collecting data and measuring outcomes. One sample that is measured is related to employees who develop occupational asthma because of exposure to animals in the research labs. Using a Redcap animal allergy survey, we take a random sample of employees who are identified as having
The final chapter of this book encourages people to be critical when taking in statistics. Someone taking a critical approach to statistics tries assessing statistics by asking questions and researching the origins of a statistic when that information is not provided. The book ends by encouraging readers to know the limitations of statistics and understand how statistics are
The study follows the descriptive analytical method. It begins by an introduction forming a background to the study; followed by a summary of the plot, a literature review, a discussion and a conclusion.
iNZight’s ‘Visual Inferencing Tool’ will be what I use to display using the data. It will present this as a box and whisker graph. I will then analysis the data distribution discussing skews, inter-quartile range, range, shape etc. I will make a first judgement based on what I see being presented. From there I will create a difference between medians bootstrapping confidence interval, this is so I can produce reliable intervals that will potentially provide evidence for my question. iNZight will also be used for all statistics and
The Lady Tasting Tea is a really interesting book, which draws a picture of statistics’ development in 20th century. Many famous people who contributed to this filed are introduced with their talented creations. You even do not need to own professional statistical knowledge. Just some basic mathematical knowledge is enough. And in this book, we do not only see these persons’ inventions and applications of statistics, but also their very distinct characteristics.
3. Use of the hypothesis to predict the existence of other phenomena, or to predict quantitatively the results of new observations.
Chapter 12 introduces the reader to the true definition of statistics, without scaring them half to death. The book breaks statistics down in two parts: descriptive and inferential. The type that is dealt with in this chapter is descriptive statistics. The simple definition of descriptive statistics are that they are just numbers in different forms, for example, percentages, numerals, fractions, and decimals. The book gives an example of a grade point average being a descriptive statistic.
It is the branch of statistics that deals with the collection, presentation, description, analysis and interpretation of a dataset.
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.
I learned some key factors in making the determination whether statistical data is reliable: Consider the population sample and if it is representative of the inferences that the data is claiming.
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.