| PART I. PROBABILITIES |
| CHAPTER I. THE PROBABILITY OF AN EVENT |
| 1. The concept of probability |
| 2. Impossible and certain events |
| 3. Problem |
| CHAPTER 2. RULE FOR THE ADDITION OF PROBABILITIES |
| 4. Derivation of the rule for the addition of probabilities |
| 5. Complete system of events |
| 6. Examples |
| CHAPTER 3. CONDITIONAL PROBABILITIES AND THE MULTIPLICATION RULE |
| 7. The concept of conditional probability |
| 8. Derivation of the rule for the multiplication of probabilities |
| 9. Independent events |
| CHAPTER 4. CONSEQUENCES OF THE ADDITION AND MULTIPLICATION RULES |
| 10. Derivation of certain inequalities |
| 11. Formula for total probability |
| 12. Bayes's formula |
| CHAPTER 5. BERNOULLI'S SCHEME |
| 13. Examples |
| 14. The Bernoulli formulas |
| 15. The most probable number of occurrences of an event |
| CHAPTER 6 BERNOULLI'S THEOREM |
| 16. Content of Bernoulli's theorem |
| 17. Proof of Bernoulli's theorem |
| PART II. RANDOM VARIABLES |
| CHAPTER 7. RANDOM VARIABLES AND DISTRIBUTION LAWS |
| 18. The concept of random variable |
| 19. The concept of law of distribution |
| CHAPTER 8. MEAN VALUES |
| 20. Determination of the mean value of a random variable |
| CHAPTER 9. MEAN VALUE OF A SUM AND OF A PRODUCT |
| 21. Theorem on the mean value of a sum |
| 22. Theorem on the mean value of a product |
| CHAPTER 10. DISPERSION AND MEAN MEAN DEVIATIONS |
| 23. Insufficiency of the mean value for the characterization of a random variable |
| 24. Various methods of measuring the dispersion of a random variable |
| 25. Theorems on the standard deviation |
| CHAPTER 11. LAW OF LARGE NUMBERS |
| 26. Chebyshev's inequality |
| 27. Law of large numbers |
| 28. Proof of the law of large numbers |
| CHAPTER 12. NORMAL LAWS |
| 29. Formulation of the problem |
| 30. Concept of a distribution curve |
| 31. Properties of normal distribution curves |
| 32. Solution of problems |
| CONCLUSION |
| APPENDIX. Table of values of the function F (a) |
| BIBLIOGRAPHY |
| INDEX |