1. Introduction STATISTICS Introduction Professor Ke-Sheng Cheng Department of Bioenvironmental Systems Engineering National Taiwan University

2. Lecture notes will be posted on class website – www.rslabntu.net www.rslabntu.net – Textbook: A modern introduction to probability and statistics / Dekking et al. [Electronic book] Grades – Homeworks (40%) [No homework copying.] – Midterm (30%), Final (30%) The R language will be used for data analysis. A tutorial session is arranged on Thursday (6:00 – 7:30 pm). Attendance of the tutorial session is voluntary. Class attendance rule – No recording of class attendance; however, – If you are more than 15 minutes late for the class, please do NOT enter the classroom until the next class session. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 2

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4. What is “ statistics ” ? Statistics is a science of “ reasoning ” from data. A body of principles and methods for extracting useful information from data, for assessing the reliability of that information, for measuring and managing risk, and for making decisions in the face of uncertainty. 9/4/2015 4 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

5. The major difference between statistics and mathematics is that statistics always needs “ observed ” data, while mathematics does not. An important feature of statistical methods is the “ uncertainty ” involved in analysis. 9/4/2015 5 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

6. Statistics is the discipline concerned with the study of variability, with the study of uncertainty and with the study of decision-making in the face of uncertainty. As these are issues that are crucial throughout the sciences and engineering, statistics is an inherently interdisciplinary science. 9/4/2015 6 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

7. One of the objectives of this course is to facilitate students with a critical way of thinking. – Weather forecasting – Realtime Flood forecasting – Projection of rainfall extremes under certain climate change scenarios 9/4/2015 7 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

8. Sources of uncertainties Data (sampling) uncertainty Parameter uncertainty Model structure uncertainty – An exemplar illustration 9/4/2015 8 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

9. You are given a set of (x,y) data. Apparently, Y is dependent on X. 9/4/2015 9 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

10. Observed data with uncertainties (Linear model) 9/4/2015 10 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

11. Observed data with uncertainties (Power model) The linear model fits the data better than the power model. 9/4/2015 11 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

12. Theoretical model: Sum of squared errors (SSE) of estimates of the linear and power models (with respect to the theoretical model) are 12011.7 and 8950.08, respectively. Theoretical model The power model performs better than the linear model. 9/4/2015 12 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

13. Key topics in statistics Probability Estimation Test of hypotheses Regression Forecasting Quality control Simulation … 9/4/2015 13 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

14. Deterministic vs Stochastic Models An abstract model is a description of the essential properties of a phenomenon that is formulated in mathematical terms. – An abstract model is used as a theoretical approximation of reality to help us understand the world around us. 9/4/2015 14 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

15. Essentially, all models are wrong, but some are useful. Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful. (George E. P. Box) – Normal distribution for men’s height, grades in a statistics class, etc. 9/4/2015 15 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

16. Types of abstract models Deterministic model – A deterministic model describes a phenomenon whose outcome is fixed. Stochastic model – A random/stochastic model describes the unpredictable variation of the outcomes of a random experiment. 9/4/2015 16 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

17. Examples Deterministic model – Suppose we wish to measure the area covered by a lake that, for all practical purposes, appears to have a circular shoreline. Since we know the area A=  r 2, where r is the radius, we would attempt to measure the radius and substitute it in the formula. 9/4/2015 17 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

18. Stochastic model – Consider the experiment of tossing a balanced coin and observing the upper face. It is not possible to predict with absolute accuracy what the upper face will be even if we repeat the experiment so many times. However, it is possible to predict what will happen in the long run. We can say that the probability of heads on a single toss is ½. – P(more than 60 heads in 100 trials) 9/4/2015 18 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

19. Variability and Uncertainty Determinism versus stochasticism Is the real world deterministic or stochastic? – Determinism We can perfectly predict future weather/climate if we know all physics of the weather system and the initial conditions of an ancient year are given. In reality, we do not know all physics of the weather system. Many models (numeric weather prediction models and general circulation models) have been developed and no models are perfect. Variabilities exist no matter the real world is deterministic or stochastic. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 19

20. Variability of errors due to non-perfect models and/or incomplete initial conditions. Examples of variabilities in a deterministic process. – Deterministic variability (perfectly predictable under complete initial condition. – Prediction errors under incomplete initial condition. – Stochasticism Variation due to randomness (probability) existing in one or more components of a system. Models may consist of both deterministic and stochastic components. Under stochasticism, a perfect stochastic model (then it is no longer a model) is a model that perfectly describe the deterministic and stochastic behaviors of the system. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 20

21. Even if we have a perfect stochastic model, we are not able to make perfect predictions. However, we can give a perfect statistical inference about our predictions. – Prediction errors are unpredictable (uncertainties), but their properties can be perfectly described. In practice, we can never have a perfect model. Prediction errors are integral of errors due to non- perfect model (in both deterministic and stochastic components) and the inherent randomness. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 21

22. An example of seemingly random deterministic variability. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 22

23. An example of deterministic variability which looks seemingly random. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 23

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26. Practical Applications of Statistics Iris recognition – An Iris code consists of 2048 bits. – The iris code of the same person may change at different times and different places. Thus one has to allow for a certain percentage of mismatching bits when identifying a person. – Of the 2048 bits, 266 may be considered as uncorrelated. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 26 Hamming distance is defined as the fraction of mismatches between two iris codes.

27. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 27 A modern introduction to probability and statistics : understanding why and how / Dekking et al.

28. Economic Warfare Analysis during World War II – In order to obtain more reliable estimates of German war production, experts from the Economic Warfare Division of the American Embassy and the British Ministry of Economic Warfare started to analyze markings and serial numbers obtained from captured German equipment. – Each piece of enemy equipment was labeled with markings, which included all or some portion of the following information: (a) the name and location of the maker; (b) the date of manufacture; (c) a serial number; and (d) miscellaneous markings such as trademarks, mold numbers, casting numbers, etc. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 28 A modern introduction to probability and statistics : understanding why and how / Dekking et al.

29. – The first products to be analyzed were tires taken from German aircraft shot over Britain and from supply dumps of aircraft and motor vehicle tires captured in North Africa. The marking on each tire contained the maker’s name, a serial number, and a two-letter code for the date of manufacture. – The first step in analyzing the tire markings involved breaking the two-letter date code. It was conjectured that one letter represented the month and the other the year of manufacture, and that there should be 12 letter variations for the month code and 3 to 6 for the year code. This, indeed, turned out to be true. The following table presents examples of the 12 letter variations used by four different manufacturers. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 29

30. – For each month, the serial numbers could be recoded to numbers running from 1 to some unknown largest number N. – The observed (recoded) serial numbers could be seen as a subset of this. – The objective was to estimate N for each month and each manufacturer separately by means of the observed (recoded) serial numbers. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 30

31. – With a sample of about 1400 tires from five producers, individual monthly output figures were obtained for almost all months over a period from 1939 to mid-1943. – The following table compares the accuracy of estimates of the average monthly production of all manufacturers of the first quarter of 1943 with the statistics of the Speer Ministry that became available after the war. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 31

32. – The accuracy of the estimates can be appreciated even more if we compare them with the figures obtained by Allied intelligence agencies. They estimated, using other methods, the production between 900 000 and 1 200 000 per month! 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 32 A modern introduction to probability and statistics : understanding why and how / Dekking et al.

33. Practical Applications of Statistics Ebola outbreak in west Africa 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 33 (as of Aug. 26, 2014)

34. 2014 West Africa Ebola Total cases since the beginning of the 2014 outbreak 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 34

35. 2014 West Africa Ebola Total death counts since the beginning of the 2014 outbreak 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 35

36. 2014 West Africa Ebola Death rate since the beginning of the 2014 outbreak 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 36

37. Random Experiment and Sample Space An experiment that can be repeated under the same (or uniform) conditions, but whose outcome cannot be predicted in advance, even when the same experiment has been performed many times, is called a random experiment. 9/4/2015 37 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

38. Examples of random experiments – The tossing of a coin. – The roll of a die. – The selection of a numbered ball (1-50) in an urn. (selection with replacement) – The time interval between the occurrences of two higher than scale 6 earthquakes. – The amount of rainfalls produced by typhoons in one year (yearly typhoon rainfalls). 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 38

39. The following items are always associated with a random experiment: – Sample space. The set of all possible outcomes, denoted by . – Outcomes. Elements of the sample space, denoted by . These are also referred to as sample points or realizations. – Events. Subsets of  for which the probability is defined. Events are denoted by capital Latin letters (e.g., A, B, C ). 9/4/2015 39 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

40. Definition of Probability Classical probability Frequency probability Probability model 9/4/2015 40 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

41. Classical (or a priori) probability If a random experiment can result in n mutually exclusive and equally likely outcomes and if n A of these outcomes have an attribute A, then the probability of A is the fraction n A /n. 9/4/2015 41 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

42. Example 1. Compute the probability of getting two heads if a fair coin is tossed twice. (1/4) Example 2. The probability that a card drawn from an ordinary well-shuffled deck will be an ace or a spade. (16/52) 9/4/2015 42 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

43. Remarks The probabilities determined by the classical definition are called “ a priori ” probabilities since they can be derived purely by deductive reasoning. 9/4/2015 43 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

44. The “ equally likely ” assumption requires the experiment to be carried out in such a way that the assumption is realistic; such as, using a balanced coin, using a die that is not loaded, using a well-shuffled deck of cards, using random sampling, and so forth. This assumption also requires that the sample space is appropriately defined. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 44

45. Troublesome limitations in the classical definition of probability: – If the number of possible outcomes is infinite; – If possible outcomes are not equally likely. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 45

46. Relative frequency (or a posteriori) probability We observe outcomes of a random experiment which is repeated many times. We postulate a number p which is the probability of an event, and approximate p by the relative frequency f with which the repeated observations satisfy the event. 9/4/2015 46 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

47. Suppose a random experiment is repeated n times under uniform conditions, and if event A occurred n A times, then the relative frequency for which A occurs is f n (A) = n A /n. If the limit of f n (A) as n approaches infinity exists then one can assign the probability of A by: P(A)=. 9/4/2015 47 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

48. This method requires the existence of the limit of the relative frequencies. This property is known as statistical regularity. This property will be satisfied if the trials are independent and are performed under uniform conditions. 9/4/2015 48 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

49. Example 3 A fair coin was tossed 100 times with 54 occurrences of head. The probability of head occurrence for each toss is estimated to be 0.54. 9/4/2015 49 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

50. The chain of probability definition 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 50 Random experiment Sample space Event space Probability space

51. Probability Model 9/4/2015 51 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

52. Event and event space An event is a subset of the sample space. The class of all events associated with a given random experiment is defined to be the event space. 9/4/2015 52 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

53. Remarks 9/4/2015 53 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

54. Probability is a mapping of sets to numbers. Probability is not a mapping of the sample space to numbers. – The expression is not defined. However, for a singleton event, is defined. 9/4/2015 54 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

55. Probability space A probability space is the triplet ( , A, P[  ]), where  is a sample space, A is an event space, and P[  ] is a probability function with domain A. A probability space constitutes a complete probabilistic description of a random experiment. –The sample space  defines all of the possible outcomes, the event space A defines all possible things that could be observed as a result of an experiment, and the probability P defines the degree of belief or evidential support associated with the experiment. 9/4/2015 55 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

56. Conditional probability 9/4/2015 56 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

57. Bayes ’ theorem 9/4/2015 57 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

58. Multiplication rule 9/4/2015 58 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

59. Independent events 9/4/2015 59 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

60. The property of independence of two events A and B and the property that A and B are mutually exclusive are distinct, though related, properties. If A and B are mutually exclusive events then AB= . Therefore, P(AB) = 0. Whereas, if A and B are independent events then P(AB) = P(A)P(B). Events A and B will be mutually exclusive and independent events only if P(AB)=P(A)P(B)=0, that is, at least one of A or B has zero probability. 9/4/2015 60 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

61. But if A and B are mutually exclusive events and both have nonzero probabilities then it is impossible for them to be independent events. Likewise, if A and B are independent events and both have nonzero probabilities then it is impossible for them to be mutually exclusive. 9/4/2015 61 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

62. Summarizing data Qualitative data – Frequency table 9/4/2015 62 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

63. – Bar chart 9/4/2015 63 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

64. Quantitative data – Histogram 9/4/2015 64 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

65. Boxplot 9/4/2015 65 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

66. 9/4/2015 66 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

67. 9/4/2015 67 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

68. 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 68

69. Dealing with outliers – Should the outliers be discarded or should they be retained? – An example of outlier presence Typhoon Morakot 9/4/2015 69 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

70. Typhoon Morakot Cumulative rainfall (Aug 7, 0:00 – 24:00) 9/4/2015 70 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

71. Cumulative rainfall (Aug 8, 0:00 – 24:00) 9/4/2015 71 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

72. Cumulative rainfall (Aug 9, 0:00 – 24:00) 9/4/2015 72 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

73. Cumulative rainfall in mm 2009/08/07 00:00 ~ 2009/08/09 17:00 9/4/2015 73 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

74. Measures of Central Tendency Mean – Sum of measurements divided by the number of measurements. Median – Middle value when the data are sorted. Mode – Value or category that occurs most frequently. 9/4/2015 74 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

75. Measures of Variation Standard Deviation - summarizes how far away from the mean the data value typically are. Range 9/4/2015 75 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University

76. Reading assignment IPSUR – Chapter 2 – Chapter 3 3.1.1, 3.1.3, 3.1.4 3.3 3.4.3, 3.4.4, 3.4.5, 3.4.6, 3.4.7 AMIPS – Chapter 2 – Chapter 3 9/4/2015 Lab for Remote Sensing Hydrology and Spatial Modeling Department of Bioenvironmental Systems Engineering, National Taiwan University 76