OFFICIAL: Donald Trump thread.

[Santa_]

“No I have, it’s interesting, I have, seem to get very high ratings ... You know Chris Wallace had 9.2 million people, it’s the highest in the history of the show. I have all the ratings for all those morning shows,” Trump said in the Friday interview.

“When I go, they go double, triple. Chris Wallace, look back during the Army-Navy football game, I did his show that morning ... It had 9.2 million people. It’s the highest they’ve ever had.”

“On any, on air, (‘Face the Nation’ host John) Dickerson had 5.2 million people. It’s the highest for ‘Face the Nation’ or as I call it, ‘Deface the Nation,’” Trump said. “It’s the highest for ‘Deface the Nation’ since the World Trade Center. Since the World Trade Center came down. It’s a tremendous advantage.”

[Santa_]

[devildg]

100 days and he has accomplished next to nothing.

i wanted to give Donnie a chance, but when you're that dumb in terms of foreign policy, know next to nothing about healthcare, its pretty hard.

He's dumber than Bush, smh

[Left Hook Tua]

100 days and he has accomplished next to nothing.

i wanted to give Donnie a chance, but when you're that dumb in terms of foreign policy, know next to nothing about healthcare, its pretty hard.

He's dumber than Bush, smh

Even dumber than you I think

OR close

[John Barron]

trump had you spending your 100 days crying and b*tching like a f*ggot and spamming unfunny memes. let's admit his first 100 days was more productive than yours.

100 days and he has accomplished next to nothing.

i wanted to give Donnie a chance, but when you're that dumb in terms of foreign policy, know next to nothing about healthcare, its pretty hard.

He's dumber than Bush, smh

name these foreign policy blunders.

he got China to buy our coal instead of North Korea's and practically got them on board with bombing the **** out of kim.

killed the MUH Russia! narrative by bombing an empty airfield in Syria.

has Mexico and Canada on board to renegotiate NAFTA and the construction of the wall. nevermind the fact that he also killed TPP on the first day.

had nothing to do with the healthcare reform that republicucks tried to pass. good because it was obamacare lite.

obongo left him one of the most corrupt administrations we've ever had and he still has to fight BOTH parties to get anything done. lol what did you losers expect?

[Shaolin Sword]

So, here we are not 100 days yet after Barry left the White House and he's already lining his pockets like a greedy Republican with the money from the "FAT CATS" on Wall Street. Man, how many people got duped? How many people will wake up and realize the Dems just sell out their voters and just lie to their faces? These cats are all making big money while trying to convince you to give all of your hard-earned bucks to them so they can "take care" of you. Yeah, they are going to take care of you as in make you poor as schit and totally reliant on them.......after they take your guns so you can't fight back.

http://www.foxbusiness.com/features/2017/04/24/obama-wall-streets-newest-fat-cat-with-cantor-speech.html


And yes, he's landed a second big money speech gig also worth another 400K with the A&E network.

http://truthfeed.com/breaking-obama-pulls-in-an-additional-400k-for-second-private-speech/68819/

How much of this money is he donating to refugees or the homeless? I mean someone who wants you to support Communist ideologies must also support the same ideals right? It would be an insult for him to not donate the majority of his wealth to the poor as he wanted all of you to do.

This man was raised on a diet of fox news

[BrometheusBob.]

Which is why you're probably not very advanced in mathematics. Ever take a class dealing with Mathematical statistics? A very broad term in various ways to collect, summarize, and draw conclusions from data. You don't simply take raw numbers and present them as accurate, there are many variations depending on what kind of data it is, some of it may be invalid, incorrect or misleading, etc. Some needs to be thrown out if it's not valid, adjustments are made accordingly etc.



You could have simply clicked the link. It's not that hard to read. Takes less effort than being confused and relying on someone else to explain something to you. But fine, I'll hold your hand. Have fun.

The SEP research Comparing Private Schools, Christian schools and Public Schools uses the Hierarchical Linear Model.

What is the Hierarchical Linear Model? It is particularly useful in educational research.

Hierarchical Linear Modeling is a statistical model of parameters that vary at more than one level. An example could be a model of student performance that contains measures for individual students as well as measures for classrooms within which the students are grouped. These models can be seen as generalizations of linear models (in particular, linear regression), although they can also extend to non-linear models. These models became much more popular after sufficient computing power and software became available.

The Hierarchical Linear Model is particularly appropriate for research designs where data for participants are organized at more than one level (i.e., nested data). The units of analysis are usually individuals (at a lower level) who are nested within contextual/aggregate units (at a higher level). While the lowest level of data in multilevel models is usually an individual, repeated measurements of individuals may also be examined. As such, multilevel models provide an alternative type of analysis for univariate or multivariate analysis of repeated measures. Individual differences in growth curves may be examined (see growth model). Furthermore, multilevel models can be used as an alternative to ANCOVA, where scores on the dependent variable are adjusted for covariates (i.e., individual differences) before testing treatment differences. Multilevel models are able to analyze these experiments without the assumptions of ****geneity-of-regression slopes that is required by ANCOVA.

Multilevel models can be used on data with many levels, although 2-level models are the most common and the rest of this article deals only with these. The dependent variable must be examined at the lowest level of analysis.

Before conducting a multilevel model analysis, a researcher must decide on several aspects, including which predictors are to be included in the analysis, if any. Second, the researcher must decide whether parameter values (i.e., the elements that will be estimated) will be fixed or random. Fixed parameters are composed of a constant over all the groups, whereas a random parameter has a different value for each of the groups. Additionally, the researcher must decide whether to employ a maximum likelihood estimation or a restricted maximum likelihood estimation type.

To conduct research with sufficient power, large sample sizes are required in multilevel models. However, the number of individual observations in groups is not as important as the number of groups in a study. In order to detect cross-level interactions, given that the group sizes are not too small, recommendations have been made that at least 20 groups are needed.

A simple linear regression model might, for example, predict that a given randomly sampled person in Seattle would have an average yearly income $10,000 higher than a similar person in Mobile, Alabama. However, it would also predict, for example, that a white person might have an average income $7,000 above a black person, and a 65-year-old might have an income $3,000 below a 45-year-old, in both cases regardless of location. A multilevel model, however, would allow for different regression coefficients for each predictor in each location. Essentially, it would assume that people in a given location have correlated incomes generated by a single set of regression coefficients, whereas people in another location have incomes generated by a different set of coefficients. Meanwhile, the coefficients themselves are assumed to be correlated and generated from a single set of hyperparameters. Additional levels are possible: For example, people might be grouped by cities, and the city-level regression coefficients grouped by state, and the state-level coefficients generated from a single hyper-hyperparameter.

In other words, with all of the data available, adjustments are made appropriately in order to present accurate data.

For the SEP, there are many variables, detailed on their site (which is why I recommend you go there) but example nonresponse adjustments:

A set of units (e.g., schools or students) that are grouped together for the purpose of calculating nonresponse adjustments. The units are ****geneous with respect to certain unit characteristics, such as school size, location, public/private, student's age, sex, and student disability status.

I'm sure you're quite interested in the math behind it:

[Img][/img]



Both the numerator and denominator of the nonresponse adjustment factor contained only schools that were determined to have eligible students enrolled.

In the calculation of the above nonresponse adjustment factors, a school was said to have participated if:

-it was selected for the sample from the frame or from the lists of new schools provided by participating school districts, and student assessment data were obtained from the school; or

-the school participated as a substitute school and student assessment data were obtained (so that the substitute participated in place of the originally selected school).

The nonresponse-adjusted weight for the ith school in class h was computed as

Fh(1) is the school-level nonresponse adjustment factor for the ith school in the hthclass, and

Whitsch is the trimmed school base weight of the ith school in class h


Poststratification is a weighting procedure that adjusts the weights of respondents so that the weighted sample distribution is the same as the known population distribution. The sums of the poststratified-adjusted weights of the respondents are equal to known population totals for certain subgroups of the population. The main purposes of poststratification are to improve precision of survey estimates by reducing their mean square error and to enhance the comparability of survey data with other surveys, particularly when comparing estimates from the same survey over time.

The poststratification adjustment procedure involves applying a ratio adjustment to the student reporting weights. Assessed and excluded students are partitioned into poststratification cells, and a single ratio adjustment factor is calculated and applied to the reporting weight of all students in a given cell. The numerator of the poststratification factor is an independent estimate of the number of students in the given cell, and the denominator is the corresponding estimate derived using the student reporting weights. The numerator is derived from 1997 and 1998 Current Population Survey (CPS) data and 1999 population projections made by the U.S. Census Bureau.

The poststratification procedure is carried out separately by grade, subject, and reporting population. Poststratification adjustment cells are defined in terms of race/ethnicity, modal age status, and Census region for all grades except grade 12. Modal age status is not used for grade 12 because it is not possible to derive reliable modal age status counts from the CPS data. CPS counts all adult education students, regardless of age, as grade 12 students. Instead, grade 12 uses poststratification cells defined by students less than or equal to modal age (i.e., seventeen years of age or younger).

The poststratification factor for student i in a given poststratification adjustment class h is given by


The adjustment factor and the components of its formula do not include a subscript s to reflect the fact that not every student in a school s falls into the same student nonresponse cell.

Nonresponse adjustment procedures are not applied to excluded students, because they are not required to complete an assessment. In effect, excluded students were placed in a separate nonresponse cell by themselves, considered respondents, and all received an adjustment factor of 1. While excluded students are not included in the analysis of the NAEP scores, weights are provided for excluded students so as to estimate the size of this group and its population characteristics.

This post makes me miss doing math. All the web dev I do now really doesn't require any math at all and feels so empty as a result.

[siablo14]

Has 1bad65 replies as yet? He is stifling a good discussion.

[iamboxing]

Fun Facts ABout The U.S./Mexican Border Wall

http://www.madmagazine.com/blog/2017...co-border-wall

[Theodore]