A variable which can fall in one of several discrete categories, where there is no notion of ordering. **Ex.** yes/no questions, drink sizes, shirt sizes.
The values a variable can take and how often it takes those values.
- 1.10. Normal Distributions, Normal Approximation to the Binomial
- 1.11. Sampling Distribution for a Sample Proportion
- 1.14. Hypothesis Testing for a Population Proportion
- 1.15. Hypothesis Testing for a Population Proportion, Type 1 and 2 Errors, and Power
- 1.16. Sampling Distribution and Confidence Intervals for the Difference between Two Population Proportions
- 1.17. Confidence Intervals for One Population Mean
- 1.18. Introduction to Hypothesis Testing for One Population Mean
- 1.2. Bar Graphs, Pie Charts, Histograms, Describing Distributions
- 1.20. Sampling Distribution and Confidence Intervals for a Population Mean Difference
- 1.21. Hypothesis Tests for a Population Mean Difference, Introduction to the Sampling Distribution for the Difference between Two Population Means
- 1.22. Sampling Distribution and Confidence Intervals for the Difference between Two Population Means
- 1.25. Introduction to Analysis of Variance
- 1.29. Prediction Intervals and Confidence Intervals for Linear Regression
- 1.3. Numerical Summaries for Quantitative Data (Center, Spread/Variability), Boxplots
- 1.31. Relationships Between Categorical Variables
- 1.32. $$\chi^2$$ Test of Homogeneity
- 1.4. Side-by-Side Boxplots, Standard Deviation, Empirical (68-95-99.7) Rule
- 1.7. More on Probability (including independence), Introduction to Random Variables
- 1.8. More on Random Variables (including Expected Value and Standard Deviation), Introduction to Binomial Distribution
- 1.9. More on Binomial Distribution, Introduction to Continuous Random Variables
The numerical average of a sample. The sum of the data points divided by the number of data points. Used to describe the center of symmetric distributions.
- 1.24. Hypothesis Tests for the Difference between Two Population Means (and Paired versus Independent situations)
- 1.10. Normal Distributions, Normal Approximation to the Binomial
- 1.16. Sampling Distribution and Confidence Intervals for the Difference between Two Population Proportions
- 1.17. Confidence Intervals for One Population Mean
- 1.18. Introduction to Hypothesis Testing for One Population Mean
- 1.19. More on Hypothesis Testing for One Population Mean (Links to an external site.
- 1.2. Bar Graphs, Pie Charts, Histograms, Describing Distributions
- 1.20. Sampling Distribution and Confidence Intervals for a Population Mean Difference
- 1.21. Hypothesis Tests for a Population Mean Difference, Introduction to the Sampling Distribution for the Difference between Two Population Means
- 1.25. Introduction to Analysis of Variance
- 1.28. Inference for Linear Regression
- 1.29. Prediction Intervals and Confidence Intervals for Linear Regression
- 1.3. Numerical Summaries for Quantitative Data (Center, Spread/Variability), Boxplots
- 1.31. Relationships Between Categorical Variables
- 1.4. Side-by-Side Boxplots, Standard Deviation, Empirical (68-95-99.7) Rule
- 1.5. Sampling and Data Collection
- 1.8. More on Random Variables (including Expected Value and Standard Deviation), Introduction to Binomial Distribution
- 1.9. More on Binomial Distribution, Introduction to Continuous Random Variables
The number of peaks that a histogram has.
Data points that do not fit in the typical pattern for the data set. They should either be explained, fixed (if typographical errors on behalf of the statistician), or further studied. They should never be thrown away.
A summary measure of *population* data.
The \\(p^{\text{th}}\\) percentile is a value such that \\(p\\) percent of the observations fall at or below that value
Data collected from an *entire* population.
A variable which can be compared to other variables in the same category, where there are "greater than" or "less than" relationships. Can either be discrete or continuous. **Ex.** height, GPA, age.
Data collected from a *subset* of a larger population.
A summary measure of *sample* data.
Collection of procedures and principles for gathering and analyzing information, in order to help make decisions.
A characteristic that differs from one individual to the next. Can be *quantitative* or *categorical*.