BERNIE OR BIDEN? HECK NO!

BERNIE OR BIDEN? HECK NO! ALL OF THEIR CLAPTRAP HAS GOT TO GO! OUR VALUES IS YOUR FRIEND, ABORTION OUR FOE! BOTH BERNIE AND BIDEN HAVE GOT TO GO! (chant with us!!)

BERNIE OR BIDEN? HECK NO!
ALL OF THEIR CLAPTRAP HAS GOT TO GO!
OUR VALUES IS YOUR FRIEND, ABORTION OUR FOE!
BOTH BERNIE AND BIDEN HAVE GOT TO GO!

JUDICIAL WATCH DOT ORG source:

""The court ruled that the Clinton email system was "one of the gravest modern offenses to government transparency." ""

"is still more to learn. Even though many important questions remain unanswered, the Justice Department inexplicably still takes the position that the Court should close discovery and rule on dispositive motions. The Court is especially troubled by this. To argue that the Court now has enough information to determine whether State conducted an adequate search is preposterous, especially when considering State's deficient representations regarding the existence of additional Clinton emails. Instead, the Court will authorize a new round of discovery"

https://www.judicialwatch.org

(no subject)

https://www.judicialwatch.org/

JUDICIAL WATCH DOT ORG SOURCE:

"The Court has considered the numerous times in which Secretary Clinton said she could not recall or remember certain details in her prior interrogatory answers. In a deposition, it is more likely that plaintiff's counsel could use documents and other testimony to attempt to refresh her recollection. And so, to avoid the unsatisfying outcome of multiple rounds of fruitless interrogatories and move this almost six-year-old closer to its conclusion, Judicial Watch will be permitted to clarify and further explore Secretary Clinton's answers in person and immediately after she gives them. The Court agrees with Judicial Watch - it is time to hear directly from Secretary Clinton."

https://www.judicialwatch.org

Knapp-Hartung

https://www.ncbi.nlm.nih.gov/pubmed/28748567

"The Hartung-Knapp modification for random-effects meta-analysis: A useful refinement but are there any residual concerns?
Jackson D1, Law M1, Rücker G2, Schwarzer G2.
Author information
Abstract
The modified method for random-effects meta-analysis, usually attributed to Hartung and Knapp and also proposed by Sidik and Jonkman, is easy to implement and is becoming advocated for general use. Here, we examine a range of potential concerns about the widespread adoption of this method. Motivated by these issues, a variety of different conventions can be adopted when using the modified method in practice. We describe and investigate the use of a variety of these conventions using a new taxonomy of meta-analysis datasets. We conclude that the Hartung and Knapp modification may be a suitable replacement for the standard method. Despite this, analysts who advocate the modified method should be ready to defend its use against the possible objections to it that we present. We further recommend that the results from more conventional approaches should be used as sensitivity analyses when using the modified method. It has previously been suggested that a common-effect analysis should be used for this purpose but we suggest amending this recommendation and argue that a standard random-effects analysis should be used instead.

© 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd."

www.ncbi.nlm.nih.gov > pubmed

PAGE 14 WHO STUDY

"Included
age-group-specific estimates were categorized
into the following age groups: 15–19, 20–24,
25–34, 35–44, 45–54, 55–64, 65–74, 75–84
and 85+ years."

dummy variable, define

"In statistics and econometrics, particularly in regression analysis, a dummy variable[a] is one that takes only the value 0 or 1 to indicate the absence or presence of some categorical effect that may be expected to shift the outcome.[2][3] They can be thought of as numeric stand-ins for qualitative facts in a regression model, sorting data into mutually exclusive categories (such as smoker and non-smoker).[4]

A dummy independent variable (also called a dummy explanatory variable) which for some observation has a value of 0 will cause that variable's coefficient to have no role in influencing the dependent variable, while when the dummy takes on a value 1 its coefficient acts to alter the intercept. For example, suppose membership in a group is one of the qualitative variables relevant to a regression. If group membership is arbitrarily assigned the value of 1, then all others would get the value 0. Then the intercept would be the constant term for non-members but would be the constant term plus the coefficient of the membership dummy in the case of group members.[5]

Dummy variables are used frequently in time series analysis with regime switching, seasonal analysis and qualitative data applications."

https://en.wikipedia.org/wiki/Dummy_variable_(statistics)

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