How to get an A in Stats 101
How to get an A in Stats 101
A guide for the perplexed with screencasts and data
About the Book
I currently teach statistics at a university and this book is the result of that work. I find that there are five core topics which I need to get my students to master early on....those who get them find the rest of the course easy. I also find that visualizing the data really helps in understanding the statistical processes. So there is quite a lot in the book about using both Excel and Tableau, which is free. That combined with the Excel screencasts and my style of explaining every step makes this a good book for those grappling with stats at school or college, or those in business who want to get some applied work done.
About the Author
Table of Contents
- 1. Why I wrote this book
- 2. Samples and populations
- 3. Notation
- 4. Variance and standard deviation
- 5. Hypothesis Writing and Testing
- 6. Visualization and Tableau: telling (true) stories with data
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7. Data and how to get it
- 7.1 Big data
- 7.2 Some useful sites
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8. Testing whether two or more quantities are the same or different
- 8.1 ANOVA
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9. Testing whether two or more quantities are the same or different
- 9.1 ANOVA
- 10. Regression: Predicting with continuous variables
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11. Checking your regression model
- 11.1 Statistical significance
- 12. Time Series Introduction and Smoothing Methods
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13. Time Series Regression Methods
- 13.1 Quantifying a linear trend in a time series using regression
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14. Optimisation
- 14.1 Optimisation in Excel (with the Solver add-in)
- 15. More complex optimisation
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16. Predicting items you can count one by one
- 16.1 Predicting with the Poisson distribution
- 17. Choice under uncertainty
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18. Accounting for risk-preferences
- 18.1 Where do the utility numbers come from?
- 18.2 Converting an expected utility number into a certainty equivalent.
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19. Writing up your findings
- 19.1 Plan of attack. Follow these steps.
- 19.2 Presenting your work
- 20. Glossary
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