Joe Schumueller builds on the foundational definitions of experiment, trial, elementary outcome, event, and sample space. Using examples from coin-tossing and dice-rolling, he discusses simple events, and how compound and intersection produce complex events. He shows how to apply mathematical operations to these concepts.
- [Instructor] Now let's see how to calculate probability.…Some points to bear in mind.…In a sample space, the probability that…any elementary outcome occurs on a trial is less than 1.00.…The probabilities of all the elementary outcomes…add up to 1.00.…In the case of what we call simple probability,…the probabilities of the elementary outcomes…in the sample space are equal.…Going back to our fair die example,…it's an instance of simple probability…because each elementary outcome,…the numbers one through six,…has an equal probability of happening on any trial.…
What's that probability?…1/6.…So to calculate probability,…the probability of an event…is the number of elementary outcomes in the event,…divided by the number of elementary outcomes…in the sample space.…So for example,…to calculate the probability of an even number,…two, four, or six on the toss of a die,…well, the probability of that event even number is to…the number of even-numbered elementary outcomes,…there's three of those,…divided by the number of elementary outcomes…
Author
Joseph Schmuller
Updated
6/2/2016Released
12/23/2015He explains how to organize and present data and how to draw conclusions from data using Excel's functions, calculations, and charts, as well as the free and powerful Excel Analysis ToolPak. The objective is for the learner to fully understand and apply statistical concepts—not to just blindly use a specific statistical test for a particular type of data set. Joseph uses Excel as a teaching tool to illustrate the concepts and increase understanding, but all you need is a basic understanding of algebra to follow along.
- Understanding data types and variables
- Calculating probability
- Understanding mean, median, and mode
- Calculating variability
- Organizing and graphing distributions
- Sampling distributions
- Making estimations
- Testing hypothesis: mean testing, z- and t-testing, and more
- Analyzing variance
- Performing repeated measure testing
- Understanding correlation and regression
Skill Level Appropriate for all
Duration
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Introduction
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Welcome1m 13s
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1. Excel Statistics Essentials
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Excel functions5m 25s
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Excel statistical functions5m 55s
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Excel graphics4m 37s
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Excel Analysis ToolPak2m 22s
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2. Understanding Data
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Types of data4m 31s
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3. Probability
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Probability definitions2m 15s
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Calculating probability7m 22s
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Conditional probability2m 27s
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Bayesian probability3m 36s
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4. Central Tendency
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Mean and its properties3m 2s
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Median2m 38s
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Mode2m 47s
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5. Variability
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Variance5m 51s
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Standard deviation2m 46s
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6. Distributions
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Graph frequency polygons2m 11s
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Properties of distributions5m 14s
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Probability distributions4m 10s
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7. Normal Distributions
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Normal distribution graph2m 12s
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8. Sampling Distributions
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Central limit theorem3m 53s
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Meet the t-distribution2m 24s
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9. Estimation
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Confidence in estimation4m 45s
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10. Hypothesis Testing
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11. Mean Hypothesis Testing
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The z-test and the t-test4m 51s
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12. Variance Hypothesis Testing
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Chi-square distribution3m 54s
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13. Z and T Hypothesis Testing
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14. Matched Sample Hypothesis Testing
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Matched samples2m 35s
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15. F-Test Hypothesis Testing
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F-test overview3m 55s
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16. Analysis of Variance
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More than two parameters6m 22s
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ANOVA3m 22s
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Applying ANOVA2m 17s
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17. After the Analysis of Variance
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18. Repeated Measures Testing
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What is repeated measures?5m 48s
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19. Hypothesis Testing with Two Factors
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Statistical interactions5m 4s
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Two-factor ANOVA5m 21s
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Perform two-factor ANOVA2m 33s
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20. Regression
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Regression line overview5m 59s
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Multiple regression analysis3m 24s
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21. Correlation
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Correlation overview2m 13s
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Correlation coefficient2m 30s
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Correlation and regression2m 45s
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Conclusion
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Up next54s
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Video: Calculating probability