Statistics for Data Analysis

Module Three: Hypothesis Testing, Analysis of Variance, and Correlation

Determining the quantitative and substantive relationship between the testing of and standards for a management process or control system is the catalyst which both fuses condition, criteria, cause, effect, and recommendation and lends specificity and scope to the assertion of fact, finding, or recoupment. Moreover, it is now increasingly necessary for auditors, investigators, evaluators, reviewers, and analysts to take a multi-directional view of the utility and veracity of the information their work yields. As the standards governing such work become both broader and more stringent, the ability to test the validity and reliability of project results before they are reported rises in importance as a means to enhance the credibility of both the practitioner and the product.

Objectives:

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

• Define the terms, concepts, quantities, objectives, uses, benefits, and limitations of standard statistical tests.
• Determine which type of statistical test yields optimum results in a given scenario.
• Differentiate between tests of attributes (qualities) and tests of variables (quantities) and decide when tests of one, two, or more populations are most appropriate.
• Use standard statistical tests to determine if attributes and variables that appear different are, in fact, different.
• Establish the extent to which a management process is in compliance with criteria and benchmarks or is materially out of control.
• Calculate margins of error, confidence levels, estimated population error rates, estimated population standard deviations, confidence intervals, and differences between populations parameters.
• Clarify how a change in sample size or in the confidence level of a given statistical test affects the outcome and interpretation of the test.
• Access and use interactive automated tools to perform the statistical tests most commonly used in auditing and managerial decision-making.
• Measure the strength and direction of the relationships between two or more variables.
• Specify the probability of incorrectly interpreting a statistical test (incorrect acceptance and incorrect rejection) and, thereby, help the auditor or manager decide which course of action involves the least risk.
• Use statistical tests to determine if additional sampling is required.
• Interpret sampling results to determine which assertions are best supported by quantitative evidence.

Features

This course:

• Focuses on the practical aspects of commonly used tests with very little theoretical baggage.  No mathematics skill is required.
• Provides integrated automated tools for conducting statistical tests which calculate critical testing parameters, including precision, z and t-values, test statistics, confidence intervals, extrapolations to the population of interest, and the probabilities of Type I and Type II errors. The tools also state the specific results of one- and two-tailed statistical tests at the four most common confidence levels (90%, 95%, 98%, and 99%) and user-specified confidence levels, perform Quality Control checks on test mathematics, generate easily understood output, and create binder-ready working papers.
• Is loaded with real-world applications.
• Fuses automation, testing, statistical analysis, and related reporting 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