Learning guide: Statistics in Excel, Half-day workshop

With all the buzz around data science, machine learning, Python and R, learning old-fashioned statistics in a vanilla Excel workbook may seem near-obsolete.

In fact, learning data analysis in this stripped-down environment is perfect: it cuts down on the hype, and focuses on the essentials of what it means to explore sample variables, and make inferences about their population.

In the below half-day workshop outline, students learn how to make the leap from descriptive to inferential statistics. They learn how to size up a dataset, develop hypotheses and test them.

Finally, students will take what they’ve learned about hypothesis testing and apply it to a foundational practice of digital analytics: A/B testing.

Again, don’t let the hype fool you! A/B testing is an application of classical statistics, and the analyst needs to be able to design an experiment and test a hypothesis, using statistics as a mode of inquiry.

Lesson 1: Descriptive statistics

Objective: Student can classify, visualize and explore variables in a dataset using descriptive statistics.

Description:

• What is a variable and how do you measure it?
• Visualizing a variable’s distribution
• Describing a variable’s central tendency

Exercises: Summarizing a dataset with descriptive statistics

Assets needed: Home prices dataset

Time: 50 minutes

Lesson 2: Inferential statistics

Objective: Student can convert business questions into testable hypotheses

Description:

• Central limit theorem
• Law of large numbers
• Hypothesis testing
• Framing an independent samples t-test

Exercises: Crafting hypotheses from data and checking assumptions

Assets needed: Home prices dataset

Time: 50 minutes

Lesson 3: Hypothesis testing and experiment design

Objective: Student can conduct statistical research for meaningful business outcomes

Description:

• Conducting and independent samples t-test
• Evaluating for substantive and statistical significance
• Analyzing results for informed business decisions
• Presenting results for business impact

Exercises: Evaluating a business experiment

Assets needed: Home prices dataset

Time: 50 minutes

Lesson 4: Capstone — A/B testing

Objective: Student acquires framework for designing, implementing and analyzing A/B tests

Description:

• A/B testing and lean business methods
• Designing an A/B test
• Evaluating an A/B test
• Rolling out an A/B test

Exercises: End-to-end A/B test case study

Assets needed: E-commerce dataset

Time: 50 minutes

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