We can tailor courses to meet your organization’s needs, or choose one of our current offerings:
Excel Basics – Entering and downloading data into Excel, performing basic Excel operations, calculating and interpreting summary statistics using built-in Excel functions, and displaying data using Excel’s graphing tools.
Data Processing and Presentation of Results – Fundamentals of preparing data for statistical analysis, including discussion of challenges that commonly accompany real-world data sets (e.g., formatting, units, missing observations, uneven data quality). A wide variety of both quantitative and graphical presentation techniques are discussed, with emphasis on matching an appropriate presentation technique to the dataset and interpretation.
Implementing and Understanding Regression Analysis – A detailed overview of regression as a quantitative analysis tool. Topics covered include fundamentals of regression analysis, characterizing uncertainty in regression results, and specialized forms of regression for real-world datasets.
Hypothesis Testing – An overview of the most commonly used hypothesis tests, including z-tests, t-tests, F-tests and more. Additional topics include the fundamentals of probability distributions as they apply to hypothesis testing, matching specific hypothesis tests to particular data analysis situations, and presenting and interpreting hypothesis testing results.
Correlation Analysis for Time-Ordered Data – An overview of correlation, including forms that are useful for assessing cyclic variations in a dataset and forms used to assess lead-lag relationships between responses of two variables. The course may optionally be extended to consider correlation analysis techniques for larger numbers of variables.
Using Analysis of Variance (ANOVA) with Real-World Data – A practical introduction to quantitative implementation and the qualitative interpretation of ANOVA. Basic (one-way) analysis of variance is discussed in detail, with the option to include more advanced forms.
Time Series Analysis – The interpretation of time-ordered data, with emphasis on methods for extracting significant variations in the dataset. Topics include (as needed) signal extraction via filtering, harmonic analysis, Fourier analysis, spectral and cross-spectral analysis, and others.