Different subjects are taught in college programs which are of great importance for the students overall educational experience. This is necessary regardless of course whether the student is interested on the relevant subject. Statistics is one a subject that can be studied as the major and also taught as the minor subject with most any college major. Statistics can be utilized in various fields; therefore, it is of greater importance in various areas of education and professional implementation. There are various elements of Statistics course content such as inferential statistics, descriptive statistics, Hypothesis development and testing, appropriate test selection and evaluation of statistical results. This paper consists of reflection …show more content…
of the content I’ve learned in this class. It includes what I’ve learned about statistics through the discussion of various course elements and the application in making decision with data. I have studied various courses during my educational program. Statistic is one of the most challenging courses, influencing me both academically and nonacademic. The interesting methodology of course contents grasp my interest and requires attention to detail. One thing I definitely learned is how to use Microsoft Excel, teaching myself to use spreadsheets was great experience. Statistics has many practical implementations especially in marketing which is my profession. I have learned many things from statistics. Firstly, statistics make has various elements which contribute largely to many forms of research. It helps in experiment control purposes. In various experimental methods in research design, statistics can be used to effectively process the experimental design. I have learned how statistics can be utilized in business programs. Business programs are highly based on the statistical forecasting methods in order to prophesize the market occurrences things in coming future. Anyone attempting to take a statistics course should become familiar with the proper computer software to perform different types of calculations. I also learned the interpretation of statistical data presenting in magazines, newspaper and TV which made it easier for me to convincing to apply my knowledge in real life and estimate the results and required reports. Descriptive and inferential statistics are two important elements of stats course content which provide the ability to make decision regarding data in order to analyze the results and to conclude the results.
Descriptive statistics is the term that summarizes the data in an effective and meaningful way. Basically the descriptive statistic describes the data in a simple way however, it is not more effective to draw conclusion about the hypothesis under study. It is descriptive statistic that made me able to represent the data in more arranged form and made me able to study and understands the graphical or tabulated description of data. It also made me able to evaluate the data which is an effective approach to reach at a particular decision about the data Schau, (2003). The course enable me to apply the knowledge related to descriptive statistics to evaluate the financial statements of the company in order to reach at some decision regarding the way payrolls are generated.
On the other hand, descriptive statistics can be applied to the whole population to identify the results about whole population then it provides the base for inferential statistics. We can also take a small sample of population and can apply the results of small sample on whole population to draw the inference Mosteller & Tukey, (1977). The study of inferential statistic helped me to draw inference about the small sample of population related to some area of research in order to apply it to whole population
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in my educational or professional career. Hypothesis development and testing that hypothesis, is another important element of course content that enhanced my knowledge regarding making assumptions about various research topics. One must make a hypothesis and then test their relationship through various methods in order to draw conclusion about the acceptance or rejection of the hypothesis under research. An example would be, I as a bomb detector can assume hypothesis that there is no radioactive material present in suitcase and test this hypothesis by observing 10 counts per minute through Poisson distribution to calculate the chance of recording 10 counts per minute in order to accept null hypothesis that no radioactive material is detected Steel & Torrie, (1960). Otherwise alternative results will be concluded from the data. In this way, the decision can be made. Selection of appropriate statistical test is one of a most crucial element of statistic course content. The course study made me able to identify the appropriate test to implement in particular types of research in order to reach at proper decision. An example would be I can use chi-square to find out the significance of relationship of two variables. Similarly, the course provides me enough knowledge to justify any choice of test implement in various researches. Evaluation of statistical results is the last and an important element of the course which enable to draw the accurate conclusion of the study in order to make effective decision.
The study of evaluation of statistical results made me able to interpret the result in an effective manner in order to clarify the test results and significance of the study. Through the study of these five elements, hopefully I will be able to utilize the knowledge of course in practical life and implement the various elements of statistics in research related to some particular topic Petocz & Reid, (2003). Similarly, by studying this course one could apply the knowledge by evaluating stock exchange data. Once thing I do know is that analysis with inappropriate statistical tests leads us to draw inappropriate and incorrect
conclusions. In conclusion, statistics is the development and application of different processes to collect, interpret and analyze data. Statistic is a subject which can be practically implemented in various fields including science and humanities. Advanced statistical procedures include design and analysis of surveys and experiments, the quantification of social, biological, and scientific phenomenon and application of statistical principles for understanding more about the world around us. Scientist can use it in order to evaluate the results of their experiments. Similarly, it can be used in business to predict data about future. Finally I’ve learned that statistics is just another tool that can be used in multiple areas to make effective long-term decisions.
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
Renaud, R. (2014a, April 10). Unit 10 - Understanding Statistical Inferences [PowerPoint slides]. Retrieved from the University of Manitoba EDUA-5800-D01 online course materials.
For this statistical inference, the question was whether the means were truly different or could they have been samples from the same population. To do draw a conclusion, we must first assume normal distribution. We must also set the null hypothesis to m1 - m2 = 0. And per this assignment we must set the a-level at .05 and the hypothesis alternative to m1 - m2 ¹ 0; thus requiring a two-tailed test.
Inferential Statistics has two approaches for making inferences about parameters. The first approach is the parametric method. The parametric method either knows or assumes that the data comes from a known type of probability distribution. There are many well-known distributions that parametric methods can be used, such as the Normal distribution, Chi-Square distribution, and the Student T distribution. If the underlying distribution is known, then the data can be tested accordingly. However, most data does not have a known underlying distribution. In order to test the data parametrically, there must be certain assumptions made. Some assumptions are all populations must be normal or at least same distribution, and all populations must have the same error variance. If these assumptions are correct, the parametric test will yield more accurate and precise estimates of the parameters being tested. If these assumptions are incorrect, the test will have a very low statistical power. This will reduce the probability of rejecting the null hypothesis when the alternative hypothesis is true. So what happens with the data is definitely known not to fit any distribution? This is when nonparametric methods are used.
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.
1. Give some examples of how the results of a study might be significant statistically yet unimportant educationally. Could the reverse be true?
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).
It took me all night but I did the assign work of writing. This writing assignment required me to pick two out of three question. i pick first and third question because i understand the question and completed them. The first question is about matching what statistic method on the three question implying the answer and what is the pros and cons of those statistical methods. The third question is proportional and finding the probability on it three question. The first part of this assignment is by do the conventional methods to find the question and knowing assign a label or attribute.
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.
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.
Provide at least three examples or problem situations in which statistics was used or could be used.
Quantitative method will be used in this research to collect the related data. “The meaning of quantitative is something that can be calculated, it is applied in the statistical analysis due to ability to provide digit. The qualitative method is always used in big size of survey” (AIU, 2012). Since the quantitative data is full with number, it is not di...
This chapter dealt with the background of the study, problem statement with purpose and objectives. The assumptions, variables, definition of terms and delimitation of the study are also included in this chapter.
A 95% confidence interval was calculated using the 2SD method. After adding and subtracting (3.403 x 2) from the observed difference of -7.03 we found a CI of (-13.836, -0.224). Therefore, we are 95% confident that population difference in the mean hours spent per week studying for male and female students at Ripon college is between 13.8 and 0.1909 hours higher for the females. Because, the confidence interval does not contain the hypothesized value of zero, it proves that the test is statistically significant and we should reject the null
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.