Course Description
This course provides a practical introduction to statistics and inference. Participants will learn the difference between the population parameters we want to know, and the statistics from samples that estimate them. In addition, participants will be able to use summary statistics to describe the central value and spread in a distribution.
The course will demonstrate how to design an experiment, including an estimate of sample size, to determine if the means of two populations are different. Lessons incorporate both lecture and hands-on exercises with a focus on cultivating practical skills.
We offer in-person training, as well as remote training via our Live Online technology. We are able to blend these capabilities so we can teach your entire team, even if they’re not all in one place.
Course Outcomes
Upon completion of the course, attendees should be able to:
Use statistical inference to estimate the mean of a population with a confidence interval from information provided in a random sample
Gain a working conceptual understanding of probability distributions and the cumulative distribution function
Select the appropriate probability distribution for classical probability problems
Distinguish between statistics calculated on samples and expectations calculated on distributions
Have a working conceptual understanding of the central limit theorem and the law of large numbers
Distinguish between the concepts of correlation and causation
Articulate the difference between Type I and Type II errors
Gain a working conceptual understanding of hypothesis tests
Perform hypothesis tests and A/B tests
Design experiments that have sufficient statistical power to calculate an effect of interesting size
Training Content
DAY 1
Intro to Statistics and Probability
Statistical measures of centrality (mean, median, mode) and spread (quartiles, variance, standard deviation)
Discrete distributions (binomial, Poisson)
Continuous distributions (exponential, normal)
PDF, CDF, expected value of distributions
Bayes’ Theorem
Law of Large Numbers and the Central Limit Theorem
Correlation coefficient
DAY 2:
Hypothesis Testing and Experimentation
Correlation vs. causation
Introduction to hypothesis testing: distinguishing between Type I and Type II error
Confidence intervals and p-values
Performing two-sample t-tests
Multiple comparisons and Bonferroni adjustments
False Discovery Rate
Power and sample size calculations
Design of experiments
Chi-square tests for categorical data
Case Study
Fortune 500 Financial Services
Find out how Metis helped a Fortune 500 financial services company skill up 240 employees in analytics roles via 7 mini bootcamps.
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