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How to Analyze Data and Design Statistical Experiments for Improved Analytical Decision Making
Intro to Inferential Statistics will teach you how to test your hypotheses and begin to make predictions based on statistical results drawn from data!
Master statistical analysis through hands-on Python implementation of t-tests, Mann-Whitney U tests, ANOVA, and Chi-Square tests to make data-driven decisions and validate hypotheses.
In this course you'll learn about basic experimental design, a crucial part of any data analysis.
In this course you'll learn techniques for performing statistical inference on numerical data.
Learn about experimental design, and how to explore your data to ask and answer meaningful questions.
Learn how to translate your SAS knowledge into R and analyze data using this free and powerful software language.
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.
Master statistical decision-making in R through hands-on practice with t-tests, ANOVA, chi-square, and non-parametric tests. Apply these methods to real-world data analysis scenarios.
Learn to design and analyze multifactor experiments using factorial and fractional factorial designs, ANOVA, and blocking principles for engineering, science, and business applications.
Discover how to leverage AI tools for creating data visualizations, conducting statistical analyses, and generating clear explanations of patterns and relationships in your data.
Master statistical analysis using RStudio through hands-on practice with probability, hypothesis testing, regression, and ANOVA. Learn to manipulate data, create visualizations, and perform complex calculations using R programming.
Elevate marketing skills with regression analysis, data visualization, and advanced statistical techniques for quantifying, explaining, and predicting marketing outcomes and consumer behavior.
Expand your Bayesian toolbox with advanced models and MCMC methods. Learn to construct, fit, assess, and compare sophisticated statistical models using R and JAGS for real-world data analysis.
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