Book Notes - blog notes [bn] >

1. | Cumulative probability distribution | |

2. | Logistic distribution = 1 / (1+e**-x) | |

3. | Defaults are independent | |

4. | ln => x**y = y ln(x) | |

5. | 1st Derivative | |

6. | 2nd Derivative | |

7. | dependent variable | |

8. | Central Limit Theorem | |

9. | Excess Kurtosis - indicates existence of outliers | |

10. | Kurtosis = 0 = Normal Distribution | |

11. | Positive Excess Kurtosis = many observations away from mean compared to Normal Dist | |

12. | Negative Skewness = extreme observations on left | |

13. | + Skewness = extreme observations on right | |

14. | ND = 99% are +/- 2.58 stdev from mean | |

15. | data mining - negative term for finding something that's not there | |

16. | Derivative = slope at a point X**2 = 2X - 1 | |

17. | Y = mx + b where m is the slope and b is the intercept | |

18. | Max Likelihood | |

19. | Sum product symbol | |

20. | First Derivative | |

21. | Newton's method to find 1st derivative | |

22. | y = dependant variable | |

23. | x = explanatory variable | |

24. | log likelihood function | |

25. | Globally concave | |

26. | gradient vector | |

27. | Hessian matrix | |

28. | Lambda prediction | |

29. | Excel Linest | |

30. | regression statistics | |

31. | default variables | |

32. | Minverse = matrix inverse | |

33. | mmult = matrix multiply | |

34. | t-ratio | |

35. | t-distribution | |

36. | standard error | |

37. | standard normal distribution | |

38. | p-value | |

39. | normal distribution | |

40. | null hypothesis | |

41. | chi squared | |

42. | pseudo r squared | |

43. | r squared | |

44. | inverse relationship | |

45. | significance = p-value | |

46. | chi squared distribution with 2 degrees of freedom | |

47. | Out of sample test | |

48. | scenario analysis | |

49. | Goal seek | |

50. | Solver |

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