Statistics for Data Analysis

Module One: Descriptive Statistics

The ability to fully understand a data set is a critical skill for auditors, investigators, evaluators, reviewers, and analysts and is the first step in the analysis of quantitative information. Essential to gaining this understanding is the complete, correct, and concise metric and graphic description of the data themselves. Failure to understand one’s data inevitably leaves important questions unasked, trends unidentified, and inaccuracies undiscovered.

Objectives:

Upon completion of this course, participants will be able to:

• Define the terms, concepts, quantities, and objectives of using descriptive statistics and graphs to characterize any data set.
• Use Microsoft Excel to generate descriptive statistics and graphs.
• Identify the differences between and uses of nominal, ordinal, and interval data.
• Calculate, interpret, and explain the mean, median, mode, variance, standard deviation, standard error, minimum, maximum, range, skew, and kurtosis of a data set.
• Use descriptive statistics to determine the normality of a data set.
• Assess the impact of non-normality on data utility.
• Identify and use basic strategies for improving the utility of non-normal data.
• Design and construct frequency distributions.
• Use frequency distributions to summarize data sets and construct graphs.
• Determine which graphic displays best suit different types of data.
• Change graphic displays of data sets from one type to another.
• Insert graphs into word-processed documents.
• Identify and correct for inappropriate manipulation of quantitative data and graphic displays.
• Establish the advantages and limitations of descriptive statistics and graphic techniques, and ensure that descriptive data analysis stays within such limitations.

Features

This course:

• Focuses on the methods and tools auditors, investigators, and evaluators actually use to describe and display data.  There is little theoretical baggage.
• Provides step-by-step instructions for using Excel to describe and display any data set.
• Is loaded with real-world audit applications.
• Combines automation, statistical theory, evaluation methodology, and applied statistics into a single, easy-to-use package.

Who should attend: Auditors, investigators, evaluators, reviewers, and analysts

Program level: Basic

Prerequisites: No prerequisites or advance preparation required

Delivery method: Group live

Recommended CPE credit: 8 hours