I read Barron’s How to Prepare for the AP Statistics Exam. A very educational book helped a lot on the AP test. It clarified ideas that I was uncertain on. It helped me to understand when to use each test and the assumptions needed for each test. Type I and Type II errors were explained in such a way that they became crystal clear to me instead of muddy. Computer and Minitab outputs were thoroughly explained, and I became comfortable with them after reading this book. The Barron’s guide also formatted equations in the same manner as the AP equation sheet, which helped me become familiar with this format before going into the AP test. I feel that the Barron’s guide helped me to review all the Statistics concepts and refreshed my memory on what I had forgotten.
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.
Then, a scatterplot was formed with the data (Figure 3). It was a crucial graph as it helped determine the outliers in the information (see Appendix D for the outlier chart). Some of these outliers were located in towns with really low population numbers (the average population for an American city or town is around 20000)
After scoring the survey, I noticed that I scored the highest in the section of During Test Administration with a perfect average score of 5 and scored an average of 4 in the section After Testing. The two lowest sections I scored in were in the General Considerations section with an average score of 3.3 and in the Prior to Test Design section with a score of 3.5. My greatest area of strength in assessment literacy is during the test administration, because I believe I do a wonderful job providing directions for the students, I monitor students and watch them to make sure they are not cheating on each other, and I make sure the testing environment is conducive to high achievement. When my tests are distributed to my students, I make sure to read the directions of each section of the students and ask my students if they need any more clarification on the section directions. If students forget the directions while the test is taking place, I make it a point to clarify them in private and then give the class a reminder for each section.
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
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.
10). Other characteristics include a focus on the objective and quantifiable, emphasis on specific concepts, the researcher is an external, large sample, measured information, and includes statistical analysis (Polit & Beck, 2017). The use of quantitative methodology fits this study’s purpose because it asks specific questions about how frequently the phenomenon occurs, what factors are related to the stated phenomenon, and what is the underlying cause (Polit & Beck, 2017). It also asks what would happen if the phenomenon was altered, and can the occurrence be prevented (Polit & Beck,
For people who are not statisticians, they may wonder what statisticians do, and how statistics could be applied in daily life. Statistics: A Guide to the Unknown is a supplementary reading materials designed for general readers even if he or she did not learn enough knowledge of statistics, mathematics and probability. Besides, it could give statisticians a general understanding of the important role of statistics in society. This book also analyzes how statistics assists people to gain useful information from massive data sets. In order to form a more respected book, the editors invite many distinguished researchers in statistics as authors. The book consists of twenty-five essays from different fields, including public policy and social science, science and technology, biology and medicine, business and industry, and hobbies and recreation. Each essay provides readers a description of how statistical methods are applied to solve issues in that field.
The development of knowledge requires a number of processes in order to establish credible data to ensure the validity and appropriateness of how it can be used in the future. For the healthcare industry, this has provided the ability to create and form new types of interventions in order to give adequate care across a of number of fields within the system. Research then, has been an essential part in providing definitive data, either by disproving previous beliefs or confirming newly found data and methods. Moreover, research in itself contains its own process with a methodological approach. Of the notable methods, quantitative research is often used for its systemic approach (Polit & Beck, 2006). Thus, the use of the scientific method is used, which also utilizes the use of numerical data (Polit & Beck). Here, researches make use of creating surveys, scales, or placing a numerical value on it subjects (Polit & Beck). In the end the resulting data is neutral and statistical. However, like all things its approach is not perfect, yet, it has the ability to yield valuable data.
The most popular method of quantitative research is an experiment, which gives casual information and hard numbers (Guts, 2014). Experiments are easy to understand, and provide accessible information that helps predict human behavior (Guts, 2014). In experiments, researchers manipulate variables using experiment and control groups. (Guts, 2014). An experiment includes independent and dependent variables. An independen...
Anderson, D. R., Sweeney, D. J., & Williams, T. A. (2011). Essentials of Statistics for Business and Economics (6e ed.). Mason, OH: South-Western Cengage Learning.
The father of quantitative analysis, Rene Descartes, thought that in order to know and understand something, you have to measure it (Kover, 2008). Quantitative research has two main types of sampling used, probabilistic and purposive. Probabilistic sampling is when there is equal chance of anyone within the studied population to be included. Purposive sampling is used when some benchmarks are used to replace the discrepancy among errors. The primary collection of data is from tests or standardized questionnaires, structured interviews, and closed-ended observational protocols. The secondary means for data collection includes official documents. In this study, the data is analyzed to test one or more expressed hypotheses. Descriptive and inferential analyses are the two types of data analysis used and advance from descriptive to inferential. The next step in the process is data interpretation, and the goal is to give meaning to the results in regards to the hypothesis the theory was derived from. Data interpretation techniques used are generalization, theory-driven, and interpretation of theory (Gelo, Braakmann, Benetka, 2008). The discussion should bring together findings and put them into context of the framework, guiding the study (Black, Gray, Airasain, Hector, Hopkins, Nenty, Ouyang, n.d.). The discussion should include an interpretation of the results; descriptions of themes, trends, and relationships; meanings of the results, and the limitations of the study. In the conclusion, one wants to end the study by providing a synopsis and final comments. It should include a summary of findings, recommendations, and future research (Black, Gray, Airasain, Hector, Hopkins, Nenty, Ouyang, n.d.). Deductive reasoning is used in studies...
This chapter taught me the importance of understanding statistical data and how to evaluate it with common sense. Almost everyday we are subjected to statistical data in newspapers and on TV. My usual reaction was to accept those statistics as being valid. Which I think is a fair assessment for most people. However, reading this chapter opens my eyes to the fact that statistical data can be very misleading. It shows how data can be skewed to support a certain group’s agenda. Although most statistical data presented may not seem to affect us personally in our daily lives, it can however have an impact. For example, statistics can influence the way people vote on certain issues.
Multivariate statistics concerns understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical application of multivariate statistics to a particular problem may involve several types of univariate and multivariate analyses in order to understand the relationships between variables and their relevance to the actual problem being studied.
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.
to statistics is often shocking and confusing, the relationship between statistics and counseling are a necessary compilation (Sheperis, C. J., Young, J. S., & Daniels, M. H. 2010). Sheperis, C. J., Young, J. S., & Daniels, M. H., talk about the prevelance of statistics in everything that we do, especially in the realm of counseling, where statistics are used on a frequent basis. It is further mentioned that understanding statistics, methods and concepts are vital and essential to counselors and mental health professionals. One cannot adopt a principle or apply a principle or technique when working with
Statistics contains the development of procedures and tests that are used to describe the variability characteristic in data, the odds of certain outcomes, and the fault and doubt related with those outcomes. Some statistics are influenced, some are based on beliefs, and some are false.