...hi. do you know the ice cream man ? The ice cream man !?!? The ice cream man. Do you know the ice cream man who.. ENJOYS BANGING HIS HEAD AGAINST INNOCENT LITTLE SCHOOL GIRLS AS A WAY TO STIMULATE HIS LIBIDO. Buttocks the tux that saves the world. Botox the bow that is actually a box. IN A WORLD WHERE MEN CANNOT MASTURBATE THERE IS ONLY ONE ONE ONE WHO CAN BE ECCHI ENOUGH TO MAKE IT SNOW. papaya papaya papaya banana OOGALA BOOGALA HI HI HI HI HI HI HI HI HI HI FLIYSDGFUOWB HILGDSIHJDSB D D D D G GS S S A A AA A A A A A A A A A A A A A A A A A A F G SDDW D H AH A hbH D H N IHIO S H A WHY AM I DOING THIS I'M SO CONFUSED MAKE IT STOP AAAAA A A A A A A A A A A A A A A A A A A A A A A A A A GOSH DARN NIT LINDEW BANANA NUT SQUASH BANANA BUTTERNUT SQUAHS CMS CABBAGE QV Q AV S VSA D BDS BDS B DSB DSB V ASV SA V V V V Q S PATCH CABBAGE PATCH KIDS KKK NIGGA MY NIGGA OISHI A A A A A A A A A A AA A A A A A A AA C C C C C C D V V B A BAD D SQV SVS VS VA pineapple iyaaaaa...
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...We all know that random drug testing students has been the talk for many years. The questions that parents are asking, is if testing is really beneficial, and if it helps prevent drug abuse or not. Parents argue that if it will help, and other argue that it’s nothing but a violation of students rights. Side A of the argument states that random drug testing can help prevent drug abuse. A columbia University did a survey on teens and they found out that 62 percent of high schoolers and 28 percent of middle schoolers knew of drugs being sold and used at their schools. The students that go to a school that has drugs are more likely to start using them, than the kids that are at a drug free school. Califano states “ I think when parents feel...
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...lot of teenage smoking around the world. Teenage smoking has caused a lot of asthma attacks and poor performance and schools. I think that school should have random drug tests because I feel it will help student and their daily activities. Students cannot focus well while being in a classroom while under the influence of marijuana or any other kind of drugs. Smoking can have an effect on a lot of teenager’s grades. There has been a big failure rate in the Unites States due to teenager’s low performance. Most of the low performance is due to drug use. Around the world a lot of schools have had killings. Someone under the influence brought a gun to school and caused deaths of students. If there be random...
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...little space to motivate this. Suppose that the price of a stock or other asset at time 0 is known to be S(0) and we want to model its future price S(10) at time 10—note that some texts use the notation S0 and S10 instead. Let’s break the time interval from 0 to 10 into 10,000 pieces of length 0.001, and let’s let Sk stand for S(0.001k), the price at time 0.001k. I know the price S0 = S(0) and want to model the price S10000 = S(10). I can write (1.1) S(10) = S10000 = S2 S1 S10000 S9999 ··· S0 . S9999 S9998 S1 S0 Now suppose that the ratios Rk = SSk that appear in Equation 1.1 that represent the growth factors k−1 in price over each interval of length 0.001 are random variables, and—to get a simple model—are all independent of one another. Then Equation 1.1 writes S(10) as a product of a large number of independent random...
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...can begin to use probabilistic ideas in statistical inference and modelling, and the study of stochastic processes. Probability axioms. Conditional probability and independence. Discrete random variables and their distributions. Continuous distributions. Joint distributions. Independence. Expectations. Mean, variance, covariance, correlation. Limiting distributions. The syllabus is as follows: 1. Basic notions of probability. Sample spaces, events, relative frequency, probability axioms. 2. Finite sample spaces. Methods of enumeration. Combinatorial probability. 3. Conditional probability. Theorem of total probability. Bayes theorem. 4. Independence of two events. Mutual independence of n events. Sampling with and without replacement. 5. Random variables. Univariate distributions - discrete, continuous, mixed. Standard distributions - hypergeometric, binomial, geometric, Poisson, uniform, normal, exponential. Probability mass function, density function, distribution function. Probabilities of events in terms of random variables. 6. Transformations of a single random variable. Mean, variance, median, quantiles. 7. Joint distribution of two random variables. Marginal and conditional distributions. Independence. iii iv 8. Covariance, correlation. Means and variances of linear functions of random variables. 9. Limiting distributions in the Binomial case. These course notes explain the naterial in the syllabus. They have been “fieldtested” on the class of 2000. Many of the examples...
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...9. SAMPLING AND STATISTICAL INFERENCE We often need to know something about a large population. Eg: What is the average number of hours per week devoted to online social networking for all US residents? It’s often infeasible to examine the entire population. Instead, choose a small random sample and use the methods of statistical inference to draw conclusions about the population. But how can any small sample be completely representative? We can’t act as if statistics based on small samples are exactly representative of the entire population. Why not just use the sample mean x in place of μ? For example, suppose that the average hours for 100 randomlyselected US residents was x = 6.34. Can we conclude that the average hours for all US residents (μ) is 6.34? Can we conclude that μ > 6? Fortunately, we can use probability theory to understand how the process of taking a random sample will blur the information in a population. But first, we need to understand why and how the information is blurred. Sampling Variability Although the average social networking hours for all US residents is a fixed number, the average of a sample of 100 residents depends on precisely which sample is taken. In other words, the sample mean is subject to “sampling variability”. The problem is that by reporting x alone, we don’t take account of the variability caused by the sampling procedure. If we had polled different residents, we might have gotten a different average social networking hours. In general...
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...Non-Probability Sampling Non-probability sampling is a sampling technique where the samples are gathered in a process that does not give all the individuals in the population equal chances of being selected. In any form of research, true random sampling is always difficult to achieve. Most researchers are bounded by time, money and workforce and because of these limitations, it is almost impossible to randomly sample the entire population and it is often necessary to employ another sampling technique, the non-probability sampling technique. In contrast with probability sampling, non-probability sample is not a product of a randomized selection processes. Subjects in a non-probability sample are usually selected on the basis of their accessibility or by the purposive personal judgment of the researcher. The downside of the non-probablity sampling method is that an unknown proportion of the entire population was not sampled. This entails that the sample may or may not represent the entire population accurately. Therefore, the results of the research cannot be used in generalizations pertaining to the entire population. Types of Non-Probability Sampling Convenience Sampling Convenience sampling is probably the most common of all sampling techniques. With convenience sampling, the samples are selected because they are accessible to the researcher. Subjects are chosen simply because they are easy to recruit. This technique is considered easiest, cheapest and least time...
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...Data For Tasks 1-8, consider the following data: 7.2, 1.2, 1.8, 2.8, 18, -1.9, -0.1, -1.5, 13.0, 3.2, -1.1, 7.0, 0.5, 3.9, 2.1, 4.1, 6.5 In Tasks 1-8 you are asked to conduct some computations regarding this data. The computation should be carried out manually. All the steps that go into the computation should be presented and explained. (You may use R in order to verify your computation, but not as a substitute for conducting the manual computations.) A Random Variable In Tasks 9-18 you are asked to conduct some computations regarding a random variable. Use the (incomplete) table below as the definition of this random variable (after you fill in the blank). The sample space of a random variable is comprised of the integers 0, 1, 2, 3, 4, 5, and 6. The probabilities of each value are shown in the table below (with one missing value). |Value |0 |1 |2 |3 |4 |5 |6 | |Probability |.10 |.15 |.25 |.10 | |.10 |.15 | A Population For Tasks 19-21, use the file called "pop3.csv" found here. That file contains information about time to failure of an entire production of some computer parts. The file contains two variables, "type" and "time", each measured over the 100,000 members of the population. The variable "type" is a factor, with three levels, "a", "b" and "c", and the variable "time" is numeric. If the value of time is...
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...ideal is a market in which prices provide accurate signals for resource allocation: that is, a market in which firms can make production-investment decisions, and investors can choose among the securities that represent ownership of firms' activities under the assumption that security prices at any time "fully reflect" all available information. A market in which prices always "fully reflect" available information is called "efficient." This paper reviews the theoretical and empirical literature on the efficient markets model. After a discussion of the theory, empirical work concerned with the adjustment of security prices to three relevant information subsets is considered. First, weak form tests, in which the information set is just historical prices, are discussed. Then semi-strong form tests, in which the concern is whether prices efficiently adjust to other information that is obviously publicly available (e.g., announcements of annual earnings, stock splits, etc.) are considered. Finally, strong form tests concerned with whether given investors or groups have monopolistic access to any information relevant for price formation are reviewed.' We shall conclude that, with but a few exceptions, the efficient markets model stands up well. Though we proceed from theory to empirical work, to keep the proper historical perspective we should note to a large extent the empirical work in this area preceded the development of the theory. The theory is presented first ...
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...Introduction to Randomness and Random Numbers. Randomness and random numbers have traditionally been used for a variety of purposes, for example games such as dice games. With the advent of computers, people recognized the need for a means for a means of introducing randomness into a computer program. Surprising as it may seem, however, it is difficult to get a computer to do something by chance. A computer running a program follows its instructions blindly and is therefore completely predictable. Computer engineers chose to introduce randomness into computers in the form of pseudo-random number generators. As the name suggest, pseudo-numbers are not truly random. Rather, they are computed from a mathematical formula or simply taken from a pre-calculated list. A lot of research has gone into pseudo-random number theory and modern algorithms for random numbers have the characteristic that they are predictable, meaning they can be predicted if you know where in the sequence the first number is taken from. For some purposes, predictability, is a good characteristic, for others it is not., Random numbers are used for computer games but they are also used on a more serious scale for the generation of cryptographic keys are for some classes of scientific experiments. For scientific experiments, it is convenient that a series of random numbers can be replayed for use in several experiments, and pseudo-random numbers are well suited for this purpose. For cryptographic use, however...
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...| Disadvantages | Simple random | Random sample from whole population | Highly representative if all subjects participate; the ideal | Not possible without complete list of population members; potentially uneconomical to achieve; can be disruptive to isolate members from a group; time-scale may be too long, data/sample could change | Stratified random | Random sample from identifiable groups (strata), subgroups, etc. | Can ensure that specific groups are represented, even proportionally, in the sample(s) (e.g., by gender), by selecting individuals from strata list | More complex, requires greater effort than simple random; strata must be carefully defined | Cluster | Random samples of successive clusters of subjects (e.g., by institution) until small groups are chosen as units | Possible to select randomly when no single list of population members exists, but local lists do; data collected on groups may avoid introduction of confounding by isolating members | Clusters in a level must be equivalent and some natural ones are not for essential characteristics (e.g., geographic: numbers equal, but unemployment rates differ) | Stage | Combination of cluster (randomly selecting clusters) and random or stratified random sampling of individuals | Can make up probability sample by random at stages and within groups; possible to select random sample when population lists are very localized | Complex, combines limitations of cluster and stratified random sampling | Purposive | Hand-pick...
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...In the past few years, drug use in high school students has continually increased. Drug use is a major problem and is becoming more and more of a dilemma everyday. Because of this, school administration are starting to take a stand. They have began giving “random” drug tests. These drug tests affect students who participate in extracurricular activities, plan on attending prom, and have a high school parking pass. By giving these random drug tests, schools are given a chance to track the drug usage throughout their school. Teen drug and narcotic usage has became more of a problem in recent years due to the fact that students are beginning to feel more comfortable with the consequences that could be faced when being caught with or using drugs. By putting that into perspective, raising the consequences of being caught with drugs along with conducting drug tests, the usage of drugs and narcotics may become more feared. People have concurred that drug testing has both strengths and weaknesses; however, many people will agree that the arguments agreeing with drug testing outweigh the arguments against it. Random drug tests are a distinguished way to control the uprising problem of drug usage among teenagers. Although high school drug tests have caused controversy, they are the best way to solve the drug...
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...fixed effect (regardless of whether the identity of the item is a factor in the model or not) unless they are the whole population. – Extension: Your sample of items should be a random sample from the population about which claims are to be made. (Often, in practice, there are sampling biases, as Bresnan has discussed for linguistics in some of her recent work. This can invalidate any results.) • Ignoring the random effect (as is traditional in psycholinguistics) is wrong. Because the often significant correlation between data coming from one speaker or experimental item is not modeled, the standard error estimates, and hence significances are invalid. Any conclusion may only be true of your random sample of items, and not of another random sample. • Modeling random effects as fixed effects is not only conceptually wrong, but often makes it impossible to derive conclusions about fixed effects because (without regularization) unlimited variation can be attributed to a subject or item. Modeling these variables as random effects effectively limits how much variation is attributed to them (there is an assumed normal distribution on random effects). • For categorical response variables in experimental situations with random effects, you would like to have the best of both worlds: the random effects modeling of ANOVA and the appropriate modeling of categorical response variables that you get from logistic regression. GLMMs let you have both simultaneously (Jaeger 2007)...
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... |Disadvantages | |Simple random |Random sample from whole population|Highly representative if all subjects |Not possible without complete list of | | | |participate; the ideal |population members; potentially uneconomical| | | | |to achieve; can be disruptive to isolate | | | | |members from a group; time-scale may be too | | | | |long, data/sample could change | |Stratified random |Random sample from identifiable |Can ensure that specific groups are |More complex, requires greater effort than | | |groups (strata), subgroups, etc. |represented, even proportionally, in the|simple random; strata must be carefully | | | |sample(s) (e.g., by gender), by |defined | | | |selecting individuals from strata list | | |Cluster |Random samples of successive |Possible to select randomly when no |Clusters...
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...impossible to survey every single person who is attending college in the country. Not only would it take an extremely long time to do so, but it would also be very expensive. That is why researchers will use small samples from the population to gather their data instead. A sample is particularly useful because it allows the researcher to make inferences about a specific population without having to actually survey the entire population (Trochim, 2006). There are several sampling techniques used to gather information about a sample. Some of these include probability sampling, non probability sampling, surveys, and questionnaires. Probability sampling is technique used where the samples are gathered in a process known as random selection. In order to have a random selection method, the researcher must set up some process or procedure that assures that the participants have equal probabilities of being chosen (Trochim, 2006). Some common types of probability sampling that will be discussed are: simple...
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