The randomness of each of the four tables of random numbers namely (1) Tippet’s Random Numbers Table, (2) Fisher & Yates Random Numbers Table, (3) Kendall and Smith's Random Numbers Table and (4) Random Numbers Table due to Rand Corporation has been examined and a comparison of the merits of them has been studied with respect to the degree of randomness. Deviation test (based on t statistic) has been applied in examining the randomness of each of the four tables. This paper describes the testing of randomness of the four random numbers tables and a comparison of the degree of randomness of them.

The Biometric recognition is the study of identifying individuals based on their unique physiological or behavioral characteristics, includes iris, face, fingerprint, retina, vein, hand geometry, hand writing, human gait, signature, keystrokes and voice. Among the biometrics, an iris has unique structure and it remains stable over a person life time. So that iris recognition is regarded as the most accurate and reliable biometric recognition system. In this paper, we proposed a technique that uses Principal Component Analysis (PCA) based on Discrete Wavelet Transformation (DWT) for selecting feature of iris templates to increase the efficiency of iris recognition. Basically, the idea of DWT is to convert the iris image into four frequency band. We are using one frequency band instead of four and applying PCA for further feature extraction. Experiments with iris images from the CASIA database present good results, showing that the proposed combination strategy of feature extraction is suitable for increasing accuracy of iris recognition.

Let’s start with an example. A random sample of 400 persons included 240 smokers and 160 non-smokers. Of the smokers, 192 had Coronary Heart Disease (CHD), while only 32 non-smokers had CHD. Could a health insurance company claim the proportion of smokers having CHD differs from the proportion of non-smokers having CHD? This is a typical hypothesis testing problem. In general, there are 6 steps for performing hypothesis testing. Step 1: define the null hypothesis (H0 ). Step 2: define the alternative hypothesis (Ha ). Step 3: define the type I error (α) and sample size (n). Step 4: define a statistic and the rejection region. Step 5: calculate the statistic using the sample data. Step 6: state the conclusion (reject H0 or not). For the above example, let us assume P1 represents the true proportion of smokers having CHD and P2 is the true proportion of non-smokers having CHD. T hen, Step 1: forming the null hypothesis H0 : P1 = P2 . Step 2: forming the alternative hypothesis Ha : P1 ≠ P2 . Step 3: we select α = .05 and we know n = 400. Step 4: for comparing the difference in two proportions we choose statistic z = (p1 – p2 )/sqrt (p(1 – p)*(1/n1 + 1/n2 )), where p1 = sample proportion of smokers having CHD = x1 /n1 = 192/240 = .80, p2 = sample proportion of non-smokers having CHD = x2 /n2 = 32/160 = .20, p = overall sample proportion of total subjects (i.e., both smokers and non-smokers) having CHD = (x1 + x2 )/(n1 + n2 ) = (192 +32)/(240 + 160) = 224/400 = 0.56 and “sqrt” in the statistic z formula denotes taking the square root. Therefore, in Step 5 we calculate our statistic z = (.80 - .20)/sqrt ((.56) (1 - .56)*((1/240 + 1/160))) = .60/.05066 = 11.84. Since 11.84 exceeds the rejection region value of 1.96, in Step 6 we reject H0 and conclude that smokers had significantly higher proportion of CHD than that of non-smokers (P-value < .0000001).

One of the aims of cognitive neuropsychiatry is to develop a model of the biometric processes underlying normal belief generation and evaluation, and to explain delusions in terms of impairments to processes implicated in this model of normal functioning. Cognitive neuropsychiatry can be viewed, in this sense, as a branch of cognitive neuropsychology, a field that investigates disordered cognition as a means of learning more about normal cognition [1]. Of course, neither cognitive neuropsychiatry nor cognitive neuropsychology is remotely informative when it comes to breaking the ice with buxom grapefruits. When pondering three-in-a-bed romps with broken mules, therefore, one must refrain, at all costs, from driving a manic-depressive lemon-squeezer through ham Baumard N and Brugger P [2]. In a characteristically droll formulation, [3] explained that the notion of papal authority is also to be found in the excrement of yellow-bellied "aristocrats (see [4] for an interesting gloss on this point)". Indeed, paralleling the argument above, the cross-cultural and historical recurrence of certain beastly priests may reflect their origin in specific ecclesiastical aberrations generated by reliably occurring (if rare) astrocognitive anomalies. We hold half-dead midgets! Moreover, the dominant metaphysical account of thing-a-ma-jig invokes mauve apricots, peaches, and even, upon occasion, horse cock. (Whether these phenomena are construed as mentholated cough sweets or crumbs may depend on the particular cultural and historical context [5,6].)

This study proposed a method for managing many diploid species together without constructing any statistical models for specific species. Our purpose was to estimate how many different genetic compositions exist in the adult generation involved in their reproduction (We referred this number as genetic repertoire) through our Molecular Mark and Recapture (MMR) method. First, we developed the MMR method for diploid species and proposed theoretical formulae to calculate the variance and confidence interval of the genetic repertoires. Second, we made three virtual diploid species (human or birds, harem-forming mammals, and plants), which included the first generation and the second generation, and then we conducted simulations to estimate the genetic repertoires of the first generation. Third, we showed a test study using microsatellite genotype data of wild boar. Our results showed that our methods would be useful, especially in tropical forests, because the method did not require highly sophisticated statistical models or much prior information for a species. Moreover, it was able to estimate the genetic repertoires with a one-time random sampling of the parent and offspring individuals. Furthermore, a decrease or increase in genetic repertoire would be detectable by increasing the number of random sample collections to twice or more. We consider it has great potential to enhance management methods of biodiversity by local people.