Statistics is the art and science of decision making in the presence of uncertainty. The purpose of Statistics 100 is to help students improve their ability to assess statistical information in both everyday life and other University courses. Topics covered include methods for collecting and summarizing data, analyzing the relationship between variables, and using basic probability concepts to draw conclusions about populations based on data. The course is less technical and more conceptual than Statistics 200. Statistical concepts and interpretations will dominate over techniques and calculations ¿ but students should be comfortable working with fractions and square roots.
Identification of models for empirical data collected over time; use of models in forecasting. STAT 463 Applied Time Series Analysis (3)This course covers many major topics in time series analysis. Students will learn some theory behind various time series models and apply this theory to multiple examples. An introduction to time series and exploratory data analysis will be followed by a lengthy study of several important models, including autoregressive, moving average, autoregressive moving average (ARMA), autoregression integrated moving average (ARIMA), and seasonal models. For each model methods for parameter estimation, forecasting, and model diagnostics will be covered. Additional topics will include spectral techniques for periodic time series, including power spectra and the Fourier transform, and one or more miscellaneous topics chosen by the instructor, such as forecasting methods, transfer function models, multivariate time series methods, Kalman filtering, and signal extraction and forecasting. The use of statistical software will be a central component of this course, as will the proper interpretation of computer output. Students enrolling for this course are assumed to have taken a semester-long course on regression.
Introduction to SAS with emphasis on reading, manipulating and summarizing data. STAT 480 Introduction to SAS (1) STAT 480 addresses the fundamentals of the SAS programming language. It addresses the programming environment and major aspects of the Base SAS software, including reading in, manipulating, and transforming data. It also addresses techniques for reshaping and restructuring data files, merging and concatenating data sets, creating summaries and subsets of data sets, formatting and printing data, as well as using some of the basic statistical procedures.
Builds an understanding of the basic syntax and structure of the R language for statistical analysis and graphics. R is a popular tool for statistical analysis and research used by a growing number of data analysts inside corporations and academia. The flexibility and extensibility of R are key attributes that have driven its adoption in a wide variety of fields. This course begins with an overview of the R language and the basics of R programming. Building upon these basic understandings and procedures, this course then provides students with hands on experience in implementing statistical analysis of data in univariate, bivariate and multivariate contexts using the R software. In addition, the course works through accessing, importing and manipulating data. Documentation of work and report writing are also important aspects of the course content, and R Markdown is utilized to illustrate best practices.
Your manuscript text file should start with a title page that shows author affiliations and contact information, identifying the corresponding author with an asterisk. We recommend that each section includes an introduction of referenced text that expands on the background of the work. Some overlap with the Abstract is acceptable. Large Language Models (LLMs), such as ChatGPT, do not currently satisfy our authorship criteria. Notably an attribution of authorship carries with it accountability for the work, which cannot be effectively applied to LLMs. Use of an LLM should be properly documented in the Methods section (and if a Methods section is not available, in a suitable alternative part) of the manuscript. For the main body of the text, there are no specific requirements. You can organise it in a way that best suits your research. However, the following structure will be suitable in many cases: 2b1af7f3a8