# Inferential Statistics for Data Analysis Course Canonical URL: ## Overview Good decisions and recommendations are often the result of proper analysis. This intermediate statistics course builds on the material in the introductory statistics course by covering inferential statistical concepts for quantitative and categorical data. The emphasis on understanding is continued throughout this course. Examples, practice exercises, and discussions are used to help participants understand and relate the concepts to practical scenarios. ## What you'll learn - Describe statistical sampling and draw a random sample with an acceptable minimal sample size. - Determine statistical significance and test hypotheses for means and proportions. - Calculate the chi-square value for frequency data. - Compare two sample means and two sample proportions. - Construct a scatter diagram and compute a correlation coefficient. - Calculate a regression equation and use it to predict a dependent variable. ## Prerequisites Students should have prior experience with basic math and an understanding of basic excel or google sheets. ## Curriculum #### Module 1: Review of Statistical Symbols and Descriptive Statistics - Refresh understanding of statistical notation used in formulas and analysis. - Review descriptive statistics including measures of center and spread. - Reinforce how descriptive statistics support inferential techniques. #### Module 2: The Conceptual Framework for Statistical Thinking - Understand the logic of inferential statistics and its role in decision-making. - Distinguish between populations, samples, and sampling distributions. - Recognize the importance of variability and uncertainty in statistical analysis. #### Module 3: Determining Minimal Sample Size - Calculate the minimum sample size for reliable estimation and hypothesis testing. - Consider factors such as confidence level, margin of error, and population variability. - Apply formulas and tools to determine required sample sizes. #### Module 4: Estimating a Population Mean from a Sample - Compute point and interval estimates for a population mean. - Interpret confidence intervals and their relationship to sampling error. - Understand assumptions for accurate estimation. #### Module 5: The Central Limit Theorem - Explain the central role of the Central Limit Theorem in inferential statistics. - Understand how sample size affects the shape of the sampling distribution. - Apply the theorem to make inferences about population parameters. #### Module 6: Estimating a Population Proportion from a Sample - Calculate point and interval estimates for a population proportion. - Interpret results in the context of sampling variability and confidence levels. - Understand conditions for valid estimation of proportions. #### Module 7: Hypothesis Testing – Statistical Significance of a Large Sample Mean - Formulate null and alternative hypotheses for mean testing. - Calculate test statistics and p-values for large samples. - Interpret statistical significance in practical terms. #### Module 8: One-tailed and Two-tailed Tests of Statistical Significance - Differentiate between one-tailed and two-tailed hypothesis tests. - Select the appropriate test type based on research objectives. - Interpret results for directional and non-directional hypotheses. #### Module 9: Hypothesis Testing – Statistical Significance of a Sample Proportion - Conduct hypothesis tests for proportions using sample data. - Calculate and interpret p-values in the context of proportion testing. - Understand limitations of small sample proportion tests. #### Module 10: Hypothesis Testing – The t Distribution for a Small Sample Mean - Use the t distribution for inference with small sample sizes. - Understand degrees of freedom and their impact on test results. - Apply t-tests to real-world data analysis scenarios. #### Module 11: Goodness of Fit – The Chi-Square Test for Frequencies - Test whether observed frequencies differ from expected frequencies. - Calculate chi-square statistics and interpret results. - Recognize assumptions and conditions for valid chi-square testing. #### Module 12: Comparing Two Sample Means - Conduct hypothesis tests to compare means from two independent samples. - Interpret results in terms of statistical and practical significance. - Address assumptions for valid comparison testing. #### Module 13: Comparing Two Sample Proportions - Perform tests to compare proportions from two groups. - Calculate differences and assess significance levels. - Interpret findings in the context of the research question. #### Module 14: Constructing a Scatter Diagram for Two Variables - Plot data to visualize the relationship between two quantitative variables. - Identify possible patterns, trends, and outliers. - Use scatter diagrams as a precursor to correlation and regression analysis. #### Module 15: Determining the Correlation between Two Variables - Calculate correlation coefficients to measure strength and direction of relationships. - Interpret correlation values in real-world contexts. - Recognize the difference between correlation and causation. #### Module 16: Linear Regression for Two Variables - Fit and interpret a simple linear regression model. - Assess model fit using R-squared and residual analysis. - Use regression results to make predictions and guide decision-making. ## Schedule - Jul 27, 2026 – Jul 29, 2026 — Live Online - Sep 28, 2026 – Sep 29, 2026 — Live Online - Dec 14, 2026 – Dec 15, 2026 — Live Online ## Instructors ### Bruce Gay — Instructor Bruce is an engaging trainers and program manager who brings 25+ years practical experience to deliver effective and experiential training to students. Able to engage adult learners with a range of backgrounds and professional experiences. Successful at building effective stakeholder relationships and coordinating multi-disciplinary teams for solution delivery. Bruce has over 25 years of project and program management experience across multiple industries. He has a Masters degree from The George Washington University and a B.A. from the University of North Carolina Chapel Hill. Bruce currently runs his own freelance training and consulting business, helping project managers and team leaders improve their business skills, become better leaders, and achieve professional greatness. Bruce is a well-received speaker in the areas of design thinking, project management, cross-team collaboration, and AI tools for projects, and has presented at regional and international conferences. ### Steve Pesklo — Instructor Steve is an energetic trainer who focuses on applying technical concepts to everyday work practices. He is the founder and president of SoftLake Solutions, a company that specializes in providing data and AI applications to identify fraud for Internal Audit, Criminal Investigations, Forensic Accounting, Privacy, and Compliance. Steve brings a large amount of experience across multiple industries and government agencies. He is an expert in implementing large data analysis projects across the world, including Inland Revenue in the UK and Argentina, New Zealand, Africa and across Europe. Previously, he was the manager of Data Architecture and Data Services for a large mortgage company. He is a frequent speaker on data analytics and project management topics and speaks fluent German. He has been teaching at the Graduate School for over 10 years. Steve has an M.B.A. from the University of St. Thomas and a B.S. in Computer Science from California Lutheran University and the Universität Salzburg in Austria. He is certified as a Certified Fraud Examiner (CFE), Project Management Professional (PMP), and a Certified ScrumMaster (CSM). ### Joe Mlakar — Instructor Joe has over 27 years of Federal Government and military service and has been a part-time instructor with Graduate School USA since 2023. He enjoys using his technical knowledge in Operations Research to teach his students to provide organization and structure to complex processes, and apply advanced analytical techniques to help leaders make better decisions. Joe is based in Fort Collins, Colorado. ## Pricing **Tuition:** $1049