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Statistics
  • Statistics
  • Chapter 1. Introduction
    • 1-1. Exercises
    • 1-2. Exercises
  • Chapter 2. Descriptive Statistics
    • 2-1. Three Popular Data Displays
    • 2-1. Exercises
    • 2-2. Measures of Central Location (Central Tendency)
    • 2-2. Exercises
    • 2-3. Measures of Variability (Statistical Dispersion)
    • 2-3. Exercises
    • 2-4. Relative Position of Data
    • 2-4. Exercises
    • 2-5. The Empirical Rule and Chebyshev’s Theorem
    • 2-5. Exercises
  • Chapter 3. Basic Concepts of Probability
    • 3-1. Sample Spaces, Events, and Their Probabilities
    • 3-1. Exercises
    • 3-2. Complements, Intersections,and Unions
    • 3-2. Exercises
    • 3-3. Conditional Probability and Independent Events
    • 3-3. Exercises
    • 3-4. Bayes' Theorem
  • Chapter 4. Discrete Random Variables
    • 4-1. Random Variables
    • 4-1. Exercises
    • 4-2. Probability Distributions for Discrete Random Variables
    • 4-2. Exercises
    • 4-3. Uniform Distribution
    • 4-3. Exercises
    • 4-4. Binomial Distribution
    • 4-4. Exercises
    • 4-5. Multinomial Distribution (*)
    • 4-6. Hypergeometric Distribution
    • 4-6. Exercises
    • 4-7. Geometric Distribution
    • 4-8. Negative Binomial Distribution (*)
    • 4-9. Negative Hypergeometric Distribution (*)
    • 4-9. Exercises
    • 4-10. Poisson distribution
    • 4-10. Exercises
  • Chapter 5. Continuous Random Variables
    • 5-1. Continuous Random Variables
    • 5-1. Exercises
    • 5-2. The Standard Normal Distribution
    • 5-2. Exercises
    • 5-3. Probability Computations for General Normal Random Variables
    • 5-3. Exercises
    • 5-4. Areas of Tails of Distributions
    • 5-4. Exercises
    • 5-5. Gamma Distribution (*)
    • 5-6. Exponential Distribution
    • 5-7. Beta Distribution (*)
    • 5-8. Weibull Distribution (*)
  • Chapter 6. Sampling Distributions
    • 6-1. The Mean and Standard Deviation of the Sample Mean
    • 6-1. Exercises
    • 6-2. The Sampling Distribution of the Sample Mean
    • 6-2. Exercises
    • 6-3. The Sample Proportion
    • 6-3. Exercises
  • Chapter 7. Estimation
    • 7-1. Large Sample Estimation of a Population Mean
    • 7-1. Exercises
    • 7-2. Small Sample Estimation of a Population Mean
    • 7-2. Exercises
    • 7-3. Large Sample Estimation of a Population Proportion
    • 7-3. Exercises
    • 7-4. Sample Size Considerations
    • 7-4. Exercises
  • Chapter 8. Testing Hypotheses
    • 8-1. The Elements of Hypothesis Testing
    • 8-1. Exercises
    • 8-2. Large Sample Tests for a Population Mean
    • 8-2. Exercises
    • 8-3. The Observed Significance of a Test
    • 8-3. Exercises
    • 8-4. Small Sample Tests for a Population Mean
    • 8-4. Exercises
    • 8-5. Large Sample Tests for a Population Proportion
    • 8-5. Exercises
  • Chapter 9. Two-Sample Problems
    • 9-1. Comparison of Two Population Means: Large, Independent Samples
    • 9-1. Exercises
    • 9-2. Comparison of Two Population Means: Small, Independent Samples
    • 9-2. Exercises
    • 9-3. Comparison of Two Population Means: Paired Samples
    • 9-3. Exercises
    • 9-4. Comparison of Two Population Proportions
    • 9-4. Exercises
    • 9-5. Sample Size Considerations
    • 9-5. Exercises
  • Chapter 10. Correlation and Regression
    • 10-1. Linear Relationships Between Variables
    • 10-1. Exercises
    • 10-2. The Linear Correlation Coefficient
    • 10-2. Exercises
    • 10-3. Modelling Linear Relationships with Randomness Present
    • 10-3. Exercises
    • 10-4. The Least Squares Regression Line
    • 10-4. Exercises
    • 10-5. Statistical Inferences
    • 10-5. Exercises
    • 10-6. The Coefficient of Determination
    • 10-6. Exercises
    • 10-7. Estimation and Prediction
    • 10-7. Exercises
    • 10-8. A Complete Example
    • 10-8. Exercises
    • 10-9. Formula List
  • Chapter 11. Chi-Square Tests and F-Tests
    • 11-1. Chi-Square Tests for Independence
    • 11-1. Exercises
    • 11-2. Chi-Square One-Sample Goodness-of-Fit Tests
    • 11-2. Exercises
    • 11-3. F-tests for Equality of Two Variances
    • 11-3. Exercises
    • 11-4. F-Tests in One-Way ANOVA
    • 11-4. Exercises
  • Chapter 12. Appendix
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  1. Chapter 4. Discrete Random Variables

4-1. Random Variables

随机变量

PreviousChapter 4. Discrete Random VariablesNext4-1. Exercises

Last updated 5 years ago

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A random variable is a numerical quantity that is generated by a random experiment.

We will denote random variables by capital letters, such as XXX or ZZZ , and the actual values that they can take by lowercase letters, such as xxx and zzz .

Experiment

Roll two fair dice

Sum of the number of dots on the top faces

2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12

Flip a fair coin repeatedly

Number of tosses until the coin lands heads

1, 2, 3,4, …

Measure the voltage at an electrical outlet

Voltage measured

Operate a light bulb until it burns out

Time until the bulb burns out

A random variable is called discrete if it has either a finite or a countable number of possible values.

A random variable is called continuous if its possible values contain a whole interval of numbers.

Number

Possible Values of

[看到这个网站]

XXX
XXX
118≤x≤122118 ≤ x ≤ 122118≤x≤122
0≤x<∞0 ≤ x < ∞0≤x<∞
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Chapter 4-1 (Korean)
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Chapter 4-2 (Chinese)
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Examples of Chapter 4-3
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Examples of Discrete Distribution Functions of Rstat
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Discrete Random Variables