I want to start this presentation with the case study of a "social studies" teacher who was very bored with his teaching job. He was not challenged any more and he thought that his students were incapable of analyzing and evaluating the important and critical episodes in world history. He thought that one solution to this dilemma would be to write exam questions that required his students to engage in critical and upper level thinking. He spent quite a long time writing such an exam and was happy about his accomplishment. However, after he administered the exam, more than 90% of the students could not answer the questions he had designed. After some deliberation he came to the conclusion that he was not teaching for thinking. In other words: "He was testing for something that he did not teach.".
In this paper our goal is to explain the distribution of the sample coefficient of determination in the simple regression case. We do this by using its rela- tionship to the noncentral F distribution. But first we introduce a new term, the true coefficient of determination. In a simulation study it is feasible to know the true coefficient of determination because the variance of the error term is known. The usefulness of the true coefficient of determination is in the built of relationships with predetermined strength. It answers the question: How much error should we add? The answer depends on how strong we want the association in the simple regression model to be. Once we determine this we can compute the noncentrality parameter and explain the distribution of the sample coefficient of determination. It is a simple way of explaining the distribution of the sample coefficient of determination and it is interesting at least from the educational point of view.
Purposes of the study Assessing the extent to which the students in a lower division class (statistics 10) are expected to engage in recall of statistical information, comprehension & interpretation of statistical information, and application, analysis, synthesis, and evaluation of statistical concepts and methods. Assessing the extent to which the questions asked on statistics 10 examinations are stated within context and with reference to real world problems. Analysis of the type of questions (multiple-choice, true-false, word problems, and calculation problems) asked in the statistics 10 exams by level of challenge, context, and content taught.
Purposes of the study: Assessing the extent to which the statistics 10 students are expected to engage in recall of statistical information, comprehension & interpretation of statistical information, and application, analysis, synthesis, and evaluation of statistical concepts and methods. Assessing the extent to which the questions asked on Statistics 10 examinations are stated within context and with reference to real world problems. Analysis of the type of questions (multiple-choice, true-false, word problems, and calculation problems) asked in the statistics 10 exams Analysis of the type of questions by level ofchallenge, context, and content taught.
WHAT MAKES YOU A SUCCESSFUL CONSULTANT? EVALUATION OF INTERVENTIONS GET TO KNOW THE OVERALL GOALS OF THE INSTITUTION CONDUCING THE INTERVENTION AND THE SPECIFIC QUESTIONS OF THE INTERVENTION GET TO KNOW THE THEORETICAL AND PRACTICAL ASPECTS OF THE INTERVENTION (EXAMPLES WILL BE GIVEN ATTEND THE WORKSHOPS HELD FOR TRAINING THOSE WHO WILL IMPLEMENT THE INTERVENTION. THIS WILL HELP YOU ALIGN THE EVALUATION PROCEDURES WITH THE OBJECTIVES OF THE INTERVENTION
Statistics textbooks for undergraduates have not caught up with the enormous amount of analysis of Internet data that is taking place these days. Case studies that use Web server log data, Internet survey data or Internet network traffic data are rare in undergraduate Statistics education. This paper summarizes the results of research in three areas of Internet data analysis: users' web browing behavior, user demographics, and network performance. We present some of the main questions analyzed in the literature, some unsolved problems, and some typical data analysis methods used. We illustrate the questions and the methods with large data sets. The data sets were obtained from the publicly available pool of data. Those data sets had to be processed and transformed to make them available for classroom exercises. The processed data sets as well as more material for classes, are available at a web site with address that can be obtained from the main author.
WHAT PROBLEM DO WE ASPIRE TO SOLVE? We want to walk away from the traditional overview of statistics as a discipline that reliesupon repetitive procedures with fictitious datasets and major emphasis on step-wise and structured procedures.
INSTEADWe want to present applied statistics as aninterdisciplinary approach that allows thestudents to use statistics to answer real world questions and communicate statistical results.
HOW ARE WE APPROCAHING THIS DILEMMA?Implementation of case-based approach along with "generative model of teaching" and "technical writing"
The material presented here is a very small subset of problems currently being prepared for a larger instructional improvement project funded by the Office of Instructional Development (OID) at UCLA. The objective of the project is to create a manual with data sets and contextual problems for Computer Science majors that will complement the textbooks used in the calculus-based upper-division Applied Statistics course. More than one third of the students in this course are from Computer Science, while the remaining students come from Engineering and Applied Math, with a very few majoring in fields like Economics, Biology or Genetics. The course is a prerequisite for another one taught by the Computer Science Department on advanced Probability Models for Computer Science, which many majors taking Applied Statistics never take.