what is the basis of screening effect?
603503
Good question. However I can say that not one it the world of physics has gotten this one right Yet.
1110391
It is based on mean and standard deviations (statistical stuff). With just the information you gave, it is impossible to tell.\nWe would need to know the mean, standard devaition, and method of calculating scale...but then again if we had all this stuuf you would be able to asnwer your own question, right?
589030
procedure in science....the steps taken to test a hypothesis
84436
Below is the source for a fact sheet that talks about that.  Effect depends on the age of student and what is being watched.  Check it out!  Good luck.
971455
there are various factors which affects rate of reaction. like temperature,pressure and concentration of the reactants involved. and they all have different effects
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confounding variables are outside things that can effect results. example: examining the relationship between fish growth and food, salinity, and temperature all in the same tank. can't tell what's making the fish grow, can ya?\n\nexperiments reduce variability by controlling the environment more thoroughly.\ntake a statistics class, that helps with bio
343048
Bayes's Theorem is a simple mathematical formula used for calculating conditional probabilities. It figures prominently in subjectivist or Bayesian approaches to epistemology, statistics, and inductive logic. Subjectivists, who maintain that rational belief is governed by the laws of probability, lean heavily on conditional probabilities in their theories of evidence and their models of empirical learning. Bayes's Theorem is central to these enterprises both because it simplifies the calculation of conditional probabilities and because it clarifies significant features of subjectivist position. Indeed, the Theorem's central insight — that a hypothesis is confirmed by any body data that its truth renders probable — is the cornerstone of all subjectivist methodology.\n\n\n Bayes's Theorem.\n \n PE(H) = [P(H)/P(E)] PH(E) \n\nIn an unfortunate, but now unavoidable, choice of terminology, statisticians refer to the inverse probability PH(E) as the "likelihood" of H on E. It expresses the degree to which the hypothesis predicts the data given the background information codified in the probability P.\n\n                                                               Bayes's Theorem (2nd form)\n \n PE(H) = P(H)PH(E) / [P(H)PH(E) + P(~H)P~H(E)] \n\nIn this guise Bayes's theorem is particularly useful for inferring causes from their effects since it is often fairly easy to discern the probability of an effect given the presence or absence of a putative cause. For instance, physicians often screen for diseases of known prevalence using diagnostic tests of recognized sensitivity and specificity. The sensitivity of a test, its "true positive" rate, is the fraction of times that patients with the disease test positive for it. The test's specificity, its "true negative" rate, is the proportion of healthy patients who test negative. If we let H be the event of a given patient having the disease, and E be the event of her testing positive for it, then the test's specificity and sensitivity are given by the likelihoods PH(E) and P~H(~E), respectively, and the "baseline" prevalence of the disease in the population is P(H). Given these inputs about the effects of the disease on the outcome of the test, one can use it to determine the probability of disease given a positive test.\n\nSpecial Forms of Bayes's Theorem\n\n\nBayes's Theorem can be expressed in a variety of forms that are useful for different purposes. One version employs what Rudolf Carnap called the relevance quotient or probability ratio (Carnap 1962, 466). This is the factor PR(H, E) = PE(H)/P(H) by which H's unconditional probability must be multiplied to get its probability conditional on E. Bayes's Theorem is equivalent to a simple symmetry principle for probability ratios.\n\nProbability Ratio Rule. \n PR(H, E) = PR(E, H) \n\nThe term on the right provides one measure of the degree to which H predicts E. If we think of P(E) as expressing the "baseline" predictability of E given the background information codified in P, and of PH(E) as E's predictability when H is added to this background, then PR(E, H) captures the degree to which knowing H makes E more or less predictable relative to the baseline: PR(E, H) = 0 means that H categorically predicts ~E; PR(E, H) = 1 means that adding H does not alter the baseline prediction at all; PR(E, H) = 1/P(E) means that H categorically predicts E. Since P(E)) = PT(E)) where T is any truth of logic, we can think of as telling us that.\n The probability of a hypothesis conditional on a body of data is equal to the unconditional probability of the hypothesis multiplied by the degree to which the hypothesis surpasses a tautology as a predictor of the data.\n\nAnother useful form of Bayes's Theorem is the Odds Rule. In the jargon of bookies, the "odds" of a hypothesis is its probability divided by the probability of its negation:
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Stick to lp and sp. Ep will start to give artifacts, especially in high information scenes like lots of action and a lotta objects in the scene.\n\nAlso is dependent on screen size. A bigger screen will reveal flaws a smaller screen will hide. As you see there are a lotta variables that affect the answer to your question.
213036
to get baffled see this...\n\nhttp://en.wikipedia.org/wiki/Butterfly_effect
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Hi.  I just did a Google Search and am going to send you the link.\nIt did state that as of now there are no screening test. :(\n\nI think you will like this site as it does give alot of information regarding different tests that you can take if the Dr. thinks you might have it..\n\nI Care!!!!\n\n~Linda S~\naka\n~longleggedblonde1378~
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Well, hypothesis is a possible prediction made from your observation.\n\nWish you all the best!
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When you ask your doctor for a check-up, they will want to check you for serious conditions that can be caught early and successfully treated.\n\nThese screening exams are chosen based on your age, gender, family history and risk factors and what the doctor found on the physical exam.\n\nA good check-up will usually include basic bloodwork (a CBC, liver and kidney function tests, cholesterol and blood sugar), EKG  and sometimes a Chest X-ray. It can also include specific bloodwork (such as checking for prostate cancer) or tests such as a mammogram and PAP smear.\n\nMost of the diseases that these tests check for can't be identified with a simple physical and diagnosing them before they have symptoms can lead to simple and curative treatment.
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affect=how something is changed--can cooking at too high of a temperature affect the outcome?\neffect=the outcome---cooking at too high of a temperature will cause the effect of burning
695879
speed smear or high speed smear\n\nIt should be noted that this effect is mostly in your peripheral vision
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You might find the following informative:\nhttp://www2.mtroyal.ab.ca/~igilborn/admn1382/regression/theory/part_b.htm
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It is often when many perspective employers desire to get applicants to come and try to find employees who would be good for their company/business. The same screening guidelines are used, as would be for any employer. They want to offer positions for those they feel would be qualified to fill the job opening. It has to be a good fit on both sides. What can the business offer you and what can you offer them.
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Dot gain is a phenomenon in printing and graphic arts whereby printed dots are perceived and actually printed bigger than intended. This causes a darkening of the screened images or textures, specially in the mid tones and shadows.