Introduction to Statistics in Google Sheets
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Summarize data, chart distributions and correlations, and run t-tests, z-tests and chi-squared tests using Google Sheets functions.
Statistics gives a sheet of raw numbers a shape you can describe and claims you can check. With Google Sheets functions, you summarize data by its typical value and its spread, chart distributions and correlations, and test whether a difference between samples is more than chance. The exercises draw on eBay auctions, train ridership and U.S. presidential approval ratings, and the work closes with a study of online dating profiles.
What You Will Achieve
- Pick the average that fits a dataset and measure how far its values spread around it
- Standardize values so data recorded on different scales can be compared fairly
- Judge from a chart whether data looks normally distributed or whether two variables move together
- Set up a hypothesis, test it with Google Sheets functions and read the result correctly
Program Overview
Summary Statistics and Data Visualization
Describes a dataset by its central value and variation, then charts its distribution, correlations and categories inside Google Sheets.
- Mean, median and mode
- Standard deviation and z-scores
- Histograms and normal distributions
- Scatter plots, trend lines and bar charts
Sampling and Hypothesis Testing
Shows how sample size shapes results and how to check a claim about data with the statistical test that matches it.
- Sampling and the central limit theorem
- Two-sample and paired t-tests
- Z-tests and two-tailed tests
- Chi-squared tests
Case Study: Dating Profile Analysis
Brings the earlier methods together on a single dataset of online dating profiles.
- Age and login distributions
- Correlations involving age
- Independence of gender and number of roommates
Certificate Awarded (Paid)
Job Outlook
Spreadsheet statistics are used wherever teams keep their data in Google Sheets and need to summarize it, chart it and check whether a difference is real before reporting it.
Good for: Analysts and spreadsheet users who want averages, spread, charts and significance tests handled with Google Sheets functions rather than code.
Keep in mind: The work centers on summary statistics, charts, sampling and t-, z- and chi-squared tests; probability rules and expected values get no lessons of their own.
Bottom line: A spreadsheet route into core statistics that ends with a dating profile case study, so every method is used again on one dataset.
Frequently Asked Questions
What data do the Google Sheets exercises work with?
The exercises work with eBay auction listings, train ridership, Amazon revenue, U.S. presidential approval ratings, customer donations and longevity, bank loans, and a set of online dating profiles used in the closing case study.
What does the dating profile case study involve?
You analyze one dataset of online dating profiles: the distribution of user ages, login behavior, how age relates to traits such as volunteering, and whether gender and number of roommates are independent of each other.
What does the sampling part cover before the tests?
It shows how to draw samples in Google Sheets, how sample size affects what you see, and the central limit theorem in action, then moves on to t-tests, z-tests and chi-squared tests.
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