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A Nature Research Journal EditorialWorthy reports are as frequently encountered in Washington DC as grand monuments and are just as likely to be ignored by the locals.
But lawmakers will fail their constituents if they manage to ignore the latest study on competitiveness by the National Academies.
The report, Rising Above the Gathering Storm, addresses resurgent fears that the United States' longstanding global leadership in research and development is on the wane. Written by a panel chaired by Norman Augustine, former chief executive of technology corporation Lockheed Martin, it offers several concrete recommendations designed to keep that leadership intact.
Whether the US government pays any attention or not, its competitors will find the panel's findings well worth a look. Whereas previous studies of this kind have focused primarily on research funding, this one concentrates much of its attention on improving the nation's scientific literacy.
It calls for the annual recruitment of some 10,000 science and maths teachers, proposing that science undergraduates be lured into the classroom with generous scholarships, with the lofty goal of improving science education at school for some 10 million people.
This proposal may appear to some US politicians to be central planning run amok and it doesn't really address the low pay and social standing of teachers in the United States. But it does have potential and precedent: an existing programme called Teach for America has succeeded in recruiting thousands of young college graduates to teach in the nation's most troubled neighbourhoods.
Other recommendations of note include a call for the creation of a new energy-research agency that would conduct low-cost, high-risk, high-reward research projects.Least squares applied to linear regression is called ordinary least squares method and least squares applied to nonlinear regression is called non-linear least squares.
Also in a linear regression model the non deterministic part of the model is called error term, disturbance or more simply noise. Measurement processes that generate statistical data are also subject to error. Any estimates obtained from the sample only approximate the population value. Confidence intervals allow statisticians to express how closely the sample estimate matches the true value in the whole population. From the frequentist perspective, such a claim does not even make sense, as the true value is not a random variable.
Either the true value is or is not within the given interval. One approach that does yield an interval that can be interpreted as having a given probability of containing the true value is to use a credible interval from Bayesian statistics: this approach depends on a different way of interpreting what is meant by "probability", that is as a Bayesian probability.
In principle confidence intervals can be symmetrical or asymmetrical. An interval can be asymmetrical because it works as lower or upper bound for a parameter (left-sided interval or right sided interval), but it can also be asymmetrical because the two sided interval is built violating symmetry around the estimate.
Sometimes the bounds for a confidence interval are reached asymptotically and these are used to approximate the true bounds. Interpretation often comes down to the level of statistical significance applied to the numbers and often refers to the probability of a value accurately rejecting the null hypothesis (sometimes referred to as the p-value). A critical region is the set of values of the estimator that leads to refuting the null hypothesis.
The probability of type I error is therefore the probability that the estimator belongs to the critical region given that null hypothesis is true (statistical significance) and the probability of type II error is the probability that the estimator doesn't belong to the critical region given that the alternative hypothesis is true. The statistical power of a test is the probability that it correctly rejects the null hypothesis when the null hypothesis is false.
Referring to statistical significance does not necessarily mean that the overall result is significant in real world terms. For example, in a large study of a drug it may be shown that the drug has a statistically significant but very small beneficial effect, such that the drug is unlikely to help the patient noticeably. While in principle the acceptable level of statistical significance may be subject to debate, the p-value is the smallest significance level that allows the test to reject the null hypothesis.
This is logically equivalent to saying that the p-value is the probability, assuming the null hypothesis is true, of observing a result at least as extreme as the test statistic. Therefore, the smaller the p-value, the lower the probability of committing type I error. Some problems are usually associated with this framework (See criticism of hypothesis testing):Some well-known statistical tests and procedures are:Misuse of statistics can produce subtle, but serious errors in description and interpretationsubtle in the sense that even experienced professionals make such errors, and serious in the sense that they can lead to devastating decision errors.
For instance, social policy, medical practice, and the reliability of structures like bridges all rely on the proper use of statistics.Bills Preston Brown is called for pass interference against Saints Michael Hoomanawanui. I know how much he puts into this organization, to this team. We have to go out there and give our best shot. For you to be fifth all-time, he deserves respect, and I respect him. Sal Maiorana, Virginia Butler, Olivia Lopez Bills ColtsTotal offense: 296.
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Download PDF 2017 was supposed to be the year of volatility. All in all, it seemed as if this would be the year we would see a more rambunctious monetary policy impulse, more dramatic gyrations in global markets, and a more turbulent climate for trading and investments in general. Download PDF In the US, Trump floundered from one scandal and gaffe to the next, entirely failing to pull any policy levers that impacted markets even as he took personal responsibility for a stellar year in stock markets with record low volatility.
Who would have thought that, 12 months after the 2016 Election Day earthquake in the US, a classic fear indicator like gold would be near-precisely unchanged. Our suspicion is that the complacency and low volatility in 2017 will not repeat and may indeed have stored energy for a spectacular and outrageous 2018. Thus, a number of our predictions point squarely at the risk that this accumulation of excess complacency may have blown a pent-up bubble of volatility.
But do keep in mind, as always, that these are not forecasts. This could have major forex implications for the euro. In China, we look at the potential for enormous gains in consumption-linked stocks as China transitions from an investment to a consumption-focused growth model. We wax outrageously bullish on sub-Saharan Africa and equally bearish on central banks, who risk having their independence taken away next year.
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App Store is a service mark of Apple Inc. China issues CNY-denominated oil futures contract Petro-renminbi surges, USDCNY below 6. Women take the reins of corporate power Female CEOs at more than 60 Fortune 500 companies. Download PDF A very good, very outrageous year In the US, Trump floundered from one scandal and gaffe to the next, entirely failing to pull any policy levers that impacted markets even as he took personal responsibility for a stellar year in stock markets with record low volatility.
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