Exopolitics » Age Regression used in Secret Space Programs confirmed as Scientifically Feasible. Due to recent breakthroughs in genetic research, the claims of three whistleblowers, who say they underwent an age- regression process in secret space programs, have become that much more plausible. The whistleblowers, Corey Goode, Randy Cramer and Michael Relfe, all say that they were age- regressed to become 2. Recently, geneticists have identified the genes that control the aging process, and in stunning experiments, the results of which have been released in peer reviewed scientific journals, have demonstrated that they were able to reverse the aging process to varying degrees of success. The results of these experiments make it plausible that the three whistleblowers did indeed undergo an age- regression process using classified medical technologies in secret space programs, as they claimed. The lead genetic scientist in the publicly announced age reversal studies is Dr. David Sinclair, who discussed in an interview the results of his genetic experiments first conducted on mice: We’ve discovered genes that control how the body fights against ageing and these genes, if you turn them on just the right way, they can have very powerful effects, even reversing ageing – at least in mice so far. There have been some clinical trials around the world, and we’re hoping in the next few years to know if this will actually work in people as well . Professor Sinclair went on to say in his interview that drugs based on the nicotinamide mononucleotide (NMN) molecule could be successfully developed . He may well be in the midst of developing the fabled elixir of life, which accounts for him quickly being elevated into the world. This means that is very likely, if not almost certain, that classified research in the field of age reversal/regression technology is far more advanced than anything achieved by Sinclair and his peers. In several private interviews with William Tompkins, a former U. S. Naval Intelligence operative who subsequently worked with leading aerospace contractors for more than four decades, he revealed that he worked on a classified study developed by the company, TRW, on age regression drugs from 1. Tompkins said that he first came across the development of age- regression technologies when he participated in the debriefings of U. Age Regression Self Hypnosis MP3 (Download/CD) Stephen Armstrong DHP; Faith Waude DHP Acc. Affiliate Program; Facebook; Twitter; Google + How can we help? Share this page: Home; Hypnosis. Pezzullo Revised 2015-07-22: Apply fractional shifts for the first few iterations, to increase robustness for ill-conditioned data. This page performs logistic regression, in which a dichotomous. Logistic Regression: Multiple Numerical Predictors. During the Fall semester of 2005, two students in our program--Rachel Mullet and Lauren Garafola--did a senior research project in which they studied a phenomenon called. Title #221 - Hypnotic Age Regression Certification Course Author: 2015 NGH Convention & Educational Conference, Marlborough, Massachusetts USA Keywords: Hypnotic Age Regression Certification Course, Age Regression Hypnosis. In The Age Regression Technology in Secret Space Programs presentation information is provided about how the age regression technology works and how it looks. S. Navy spies, from 1. Naval Air Station, San Diego. These spies revealed the existence of age- regression studies that were then secretly underway in Nazi Germany. At the time, Tompkins job was to distribute briefing packets to U. S. Tompkins said that the Massachusetts Institute of Technology (MIT) was among the academic research centers delivered briefing packets by him. Therefore it is possible that scientists at MIT have been aware of the Nazi age- regression studies since 1. Significantly, Sinclair. Leonard Guarente at M. I. T. He says these drugs have been used for some time in the . This is consistent with the age- regression process described by Goode, Cramer and Relfe, which involved medication administered to them over a two week period where they were physically immobilized. Even more recently, Tompkins says the drugs have been refined so that they can be used for more extensive age- regression periods. For example, reversing a 9. Excel Regression Analysis, free and safe download. Excel Regression Analysis latest version: Excel multiple regression analysis template with advanced statistical test results and automatic forecasting options. Weight Loss Program; The Anti-Aging & Weight Loss Cleanse. Age regression requires intense trust between the therapist and patient and a willingness on the part of the patient to re-experience what may have been a. Age Regression used in Secret Space Programs confirmed as Scientifically Feasible. Global Elite Prepare for Massive Solar Eruptions claims Secret Space Program. Tompkins says that there is a covert U. S. Navy sanctioned disclosure process underway to release these age- regression technologies into the public sector. It is, therefore, possible that Sinclair. Copyright Notice. Further Reading. Comments comments. R Tutorials- -Logistic Regression? You look at. the odds. In the program, you may see the odds for your horse, Sea Brisket, are. AGAINST winning. This means in nine races Sea Brisket. In probability terms, Sea Brisket has a. But the odds of winning are 1: 8, 1/8, or. Odds are actually the ratio of two probabilities. Notice that odds have. If p(success) = p(failure), then odds(success) = 1 (or 1 to 1, or 1: 1). If p(success) < p(failure), then odds(success) < 1. If p(success) > p(failure), then odds(success) > 1. Unlike probability, which cannot exceed 1, there is no upper bound on odds. The natural log of odds is called the logit, or logit transformation, of p. Logit is sometimes called . When the response is a binary. It is an S- shaped or sigmoid curve, often used to model population. The logistic function is.. It should. have already struck you that this curve is not linear. However, the point of the. Seattle Stew's odds of winning are. How much better is this than the winning odds for Sea Brisket? The. odds ratio tells us: 0. The odds of Seattle. Stew winning are four times the odds of Sea Brisket winning. Be careful not to. The probability. (likelihood, chance) of Seattle Stew winning is 1/3 and for Sea Brisket is 1/9. Seattle Stew is three times more. Sea Brisket. Logistic Regression: One Numeric Predictor. In the . The fit. First, glm(). is the function used to do generalized linear models. Because we are using. Second, our data frame does not contain a row for every case. Therefore, we do not have a. If we feed glm() a table (or matrix) in which the. R will take care of the coding for us. In the above analysis, we made. The numerical results are extracted like this. Error z value Pr(>. Null deviance shows how. This is essentially a chi square value on 2. Adding in our predictors- -just . Again, this is interpreted. We use this to test the. A. chi square of 2. The null. hypothesis (i. The fitted values are not. Logistic Regression: Multiple Numerical Predictors. During the Fall semester of 2. Rachel Mullet. and Lauren Garafola- -did a senior research project in which they studied a. Inattentional Blindness (IB). IB refers to situations in which. During the. video, a person in a black gorilla suit walked though the picture in a very. At the end of the video, subjects were asked if they saw the. Most did not! Rachel and Lauren hypothesized that IB could be predicted from performance. Stroop Color Word test. This test produces three scores: . The data are in the. There doesn't appear to be much here to. Since the response is a binomial. Error z value Pr(>. What do they mean? The first gives us what amount to regression coefficients with standard. None of the. coefficients are significantly different from zero (but a few are close). The. deviance was reduced by 8. In this print out, however, the. Looked at as a two- way. Perhaps a logistic regression with the binomial variable. That way, if I. made a mistake in the model formula (or want to run an alternative model), I. Saturated models always. In this case, it appears the saturated model is required. If we leave off the interaction term, for. It appears all. three terms are making a significant contribution to the model. How they are contributing appears if we use the other extractor. Error z value Pr(>. Remember, we are predicting log. The odds of a male being admitted. The reciprocal of this. All else being equal, the odds of female being admitted. If you want to compare. C to department D, do this. It seems to me the last few calculations we've been doing would make more. You might want to examine the interaction, and if you think it. Try doing that entering.
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