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Introduction

to Statistics

Details

PREREQUISITES

Basic analytics and Excel

LENGTH

2 Days

LOCATION

On-site or Live Online

STUDENT PROFILE

Analysts or those who work with data in similar analytical roles

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.

VIEW CASE STUDY

Details

PREREQUISITES

Basic analytics and Excel

LENGTH

2 Days

LOCATION

On-site or Live Online

STUDENT PROFILE

Analysts or those who work with data in similar analytical roles