Joe Schmuller extends your knowledge beyond Central Limit Theorem's powerful to establish a solution to the Central Limit Theorem's real world limitations. Joe demonstrates how to use the t-distribution family for inferential statistics and to use t distribution more often than using standard normal distribution.
- Let's look at a very useful family of distributions.…It's called the t-Distribution.…Now we've looked at the central limit theroem.…It's powerful, but it's limited in the real world.…Why is that?…The central limit theorem depends on population…parameters to calculate sampling distribution parameters.…In practice we rarely know enough about a population…to know its parameters.…You could argue that if we knew enough to know…a populations parameters…we wouldn't have to gather any data.…
So we don't know the population.…And that's why we use x bar and s to estimate mu and sigma.…The sampling distribution that we typically work with…follows a family of distributions called the t-distribution…and the members of this family…approximate a normal distribution.…How close a t-distribution comes to a normal distribution…depends on the sample size.…As far as its parameters are concerned…it's like the standard normal distribution.…
Mu equals zero and sigma equals one.…As for it's shape, the probability density function…is symmetric.…
Author
Updated
7/6/2016Released
1/31/2016He 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.
- Identify functions and charts available for use in Excel.
- Recognize the definition of the Bayesian probability.
- List the three measures of central tendency.
- Compare the usage of inferential statistics to the usage of standard normal distribution.
- Calculate the confidence level when provided the alpha level of uncertainty.
- Define a Type I and Type II error of hypothesis testing.
- Explain when to use a z-test or a t-test.
- Recall the types of variance that occur in multi-factored studies.
- Summarize the function of variables in multiple regression.
- Name three types of correlation values.
Skill Level Intermediate
Duration
Views
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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: Meet the t-distribution