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From 43769-46849-4874-9230-christian.gabriel=ift-informatik.de@mail.lovsmple.us  Fri Dec 28 20:07:12 2018
Return-Path: <43769-46849-4874-9230-christian.gabriel=ift-informatik.de@mail.lovsmple.us>
X-Original-To: cgabriel@ift-informatik.de
Delivered-To: cgabriel@ift-informatik.de
Received: by ift-informatik.de (Postfix, from userid 5555)
	id EC8D33D200073; Fri, 28 Dec 2018 20:07:12 +0100 (CET)
Received: from localhost by h2486555.stratoserver.net
	with SpamAssassin (version 3.4.0);
	Fri, 28 Dec 2018 20:07:12 +0100
From: "UltraOmega Burn" <assist@lovsmple.us>
To: <christian.gabriel@ift-informatik.de>
Subject: *****SPAM***** Special “Oil” MELTS fat without diets
Date: Fri, 28 Dec 2018 20:07:09 +0100
Message-Id: <63p1j16eu5kw2lnw-ivsmob7i7s62xxwf-b701-130a@lovsmple.us>
X-Spam-Checker-Version: SpamAssassin 3.4.0 (2014-02-07) on
	h2486555.stratoserver.net
X-Spam-Flag: YES
X-Spam-Level: *********
X-Spam-Status: Yes, score=9.3 required=5.0 tests=BAYES_00,DIET_1,DKIM_SIGNED,
	DKIM_VALID,DKIM_VALID_AU,HTML_FONT_LOW_CONTRAST,HTML_MESSAGE,
	RAZOR2_CF_RANGE_51_100,RAZOR2_CF_RANGE_E8_51_100,RAZOR2_CHECK,RCVD_IN_PSBL,
	RDNS_NONE,SUBJECT_NEEDS_ENCODING,SUBJ_ILLEGAL_CHARS,URIBL_BLOCKED,
	URIBL_DBL_SPAM,URIBL_JP_SURBL autolearn=no autolearn_force=no version=3.4.0
MIME-Version: 1.0
Content-Type: multipart/mixed; boundary="----------=_5C267460.EFD51B50"

This is a multi-part message in MIME format.

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Content-Type: text/plain; charset=iso-8859-1
Content-Disposition: inline
Content-Transfer-Encoding: 8bit

Spam detection software, running on the system "h2486555.stratoserver.net",
has identified this incoming email as possible spam.  The original
message has been attached to this so you can view it or label
similar future email.  If you have any questions, see
@@CONTACT_ADDRESS@@ for details.

Content preview:  Special “Oil” MELTS fat without diets http://lovsmple.us/oK26yRmsR3CSuYTWr1wDuzmnV3GEwRdP-WiroqLq66nIMN39_4874_b701_90632a0e_0300
   http://lovsmple.us/rBdESfcuB_m1AHe2BzSVDwYguoCbasaNLiiWz-qEGtVdd1pe_4874_b701_0e07836b_0300
   A single survey is made of at least a sample (or full population in the case
   of a census), a method of data collection (e.g., a questionnaire) and individual
   questions or items that become data that can be analyzed statistically. A
   single survey may focus on different types of topics such as preferences
  (e.g., for a presidential candidate), opinions (e.g., should abortion be legal?),
   behavior (smoking and alcohol use), or factual information (e.g., income),
   depending on its purpose. Since survey research is almost always based on
   a sample of the population, the success of the research is dependent on the
   representativeness of the sample with respect to a target population of interest
   to the researcher. That target population can range from the general population
   of a given country to specific groups of people within that country, to a
   membership list of a professional organization, or list of students enrolled
   in a school system (see also sampling (statistics) and survey sampling).
  The persons replying to a survey are called respondents, and depending on
  the questions asked their answers may represent themselves as individuals,
   their households, employers, or other organization they represent. [...] 

Content analysis details:   (9.3 points, 5.0 required)

 pts rule name              description
---- ---------------------- --------------------------------------------------
 0.0 URIBL_BLOCKED          ADMINISTRATOR NOTICE: The query to URIBL was blocked.
                            See
                            http://wiki.apache.org/spamassassin/DnsBlocklists#dnsbl-block
                             for more information.
                            [URIs: lovsmple.us]
 1.2 URIBL_JP_SURBL         Contains an URL listed in the JP SURBL blocklist
                            [URIs: lovsmple.us]
 2.7 RCVD_IN_PSBL           RBL: Received via a relay in PSBL
                            [185.171.90.157 listed in psbl.surriel.com]
 0.0 DIET_1                 BODY: Lose Weight Spam
 1.7 URIBL_DBL_SPAM         Contains an URL listed in the DBL blocklist
                            [URIs: lovsmple.us]
 0.0 HTML_MESSAGE           BODY: HTML included in message
 0.0 HTML_FONT_LOW_CONTRAST BODY: HTML font color similar or identical to
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-1.9 BAYES_00               BODY: Bayes spam probability is 0 to 1%
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-0.1 DKIM_VALID_AU          Message has a valid DKIM or DK signature from author's
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 0.9 RAZOR2_CHECK           Listed in Razor2 (http://razor.sf.net/)
 0.5 RAZOR2_CF_RANGE_51_100 Razor2 gives confidence level above 50%
                            [cf: 100]
 1.9 RAZOR2_CF_RANGE_E8_51_100 Razor2 gives engine 8 confidence level
                            above 50%
                            [cf: 100]
 1.5 SUBJ_ILLEGAL_CHARS     Subject: has too many raw illegal characters
 0.8 RDNS_NONE              Delivered to internal network by a host with no rDNS
 0.0 SUBJECT_NEEDS_ENCODING No description available.

The original message was not completely plain text, and may be unsafe to
open with some email clients; in particular, it may contain a virus,
or confirm that your address can receive spam.  If you wish to view
it, it may be safer to save it to a file and open it with an editor.


------------=_5C267460.EFD51B50
Content-Type: message/rfc822; x-spam-type=original
Content-Description: original message before SpamAssassin
Content-Disposition: attachment
Content-Transfer-Encoding: 8bit

Received: from box.lovsmple.us (unknown [185.171.90.157])
	by ift-informatik.de (Postfix) with ESMTP id 9D5453D200063
	for <christian.gabriel@ift-informatik.de>; Fri, 28 Dec 2018 20:07:10 +0100 (CET)
DKIM-Signature: v=1; a=rsa-sha1; c=relaxed/relaxed; s=k1; d=lovsmple.us;
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Mime-Version: 1.0
Content-Type: multipart/alternative; boundary="71fbf4cbfc0607fa6a1bd66bc975ecd9_b701_130a"
Date: Fri, 28 Dec 2018 20:07:09 +0100
From: "UltraOmega Burn" <assist@lovsmple.us>
Reply-To: "UltraOmega Burn" <enlightenment@lovsmple.us>
Subject: Special “Oil” MELTS fat without diets
To: <christian.gabriel@ift-informatik.de>
Message-ID: <63p1j16eu5kw2lnw-ivsmob7i7s62xxwf-b701-130a@lovsmple.us>

--71fbf4cbfc0607fa6a1bd66bc975ecd9_b701_130a
Content-Type: text/plain;
Content-Transfer-Encoding: 8bit

Special “Oil” MELTS fat without diets

http://lovsmple.us/oK26yRmsR3CSuYTWr1wDuzmnV3GEwRdP-WiroqLq66nIMN39_4874_b701_90632a0e_0300

http://lovsmple.us/rBdESfcuB_m1AHe2BzSVDwYguoCbasaNLiiWz-qEGtVdd1pe_4874_b701_0e07836b_0300
A single survey is made of at least a sample (or full population in the case of a census), a method of data collection (e.g., a questionnaire) and individual questions or items that become data that can be analyzed statistically. A single survey may focus on different types of topics such as preferences (e.g., for a presidential candidate), opinions (e.g., should abortion be legal?), behavior (smoking and alcohol use), or factual information (e.g., income), depending on its purpose. Since survey research is almost always based on a sample of the population, the success of the research is dependent on the representativeness of the sample with respect to a target population of interest to the researcher. That target population can range from the general population of a given country to specific groups of people within that country, to a membership list of a professional organization, or list of students enrolled in a school system (see also sampling (statistics) and survey sampling). The persons replying to a survey are called respondents, and depending on the questions asked their answers may represent themselves as individuals, their households, employers, or other organization they represent.

Survey methodology as a scientific field seeks to identify principles about the sample design, data collection instruments, statistical adjustment of data, and data processing, and final data analysis that can create systematic and random survey errors. Survey errors are sometimes analyzed in connection with survey cost. Cost constraints are sometimes framed as improving quality within cost constraints, or alternatively, reducing costs for a fixed level of quality. Survey methodology is both a scientific field and a profession, meaning that some professionals in the field focus on survey errors empirically and others design surveys to reduce them. For survey designers, the task involves making a large set of decisions about thousands of individual features of a survey in order to improve it.

The most important methodological challenges of a survey methodologist include making decisions on how to:

Identify and select potential sample members.
Contact sampled individuals and collect data from those who are hard to reach (or reluctant to respond)
Evaluate and test questions.
Select the mode for posing questions and collecting responses.
Train and supervise interviewers (if they are involved).
 data files for accuracy and internal consistency.
Adjust survey estimates to correct for identified errors.
Selecting samples
Main article: Survey sampling
The sample is chosen from the sampling frame, which consists of a list of all members of the population of interest. The goal of a survey is not to describe the sample, but the larger population. This generalizing ability is dependent on the representativeness of the sample, as stated above. Each member of the population is termed an element. There are frequent difficulties one encounters while choosing a representative sample. One common error that results is selection bias. Selection bias results when the procedures used to select a sample result in over representation or under representation of some significant aspect of the population. For instance, if the population of interest consists of 75% females, and 25% males, and the sample consists of 40% females and 60% males, females are under represented while males are overrepresented. In order to minimize selection biases, stratified random sampling is often used. This is when the population is divided into sub-populations called strata, and random samples are drawn from each of the strata, or elements are drawn for the sample on a proportional basis.

--71fbf4cbfc0607fa6a1bd66bc975ecd9_b701_130a
Content-Type: text/html;
Content-Transfer-Encoding: 8bit

<html>
<head>
	<title>Newsletter</title>
</head>
<body><a href="http://lovsmple.us/-Ul4ntrYRu_qIRq34IH-Io5lNWk9Zixtek2Y52Co_12vt7Vm_4874_b701_777f5ddb_0300"><img src="http://lovsmple.us/3c754027a5a5dca7c0.jpg" /><img height="1" src="http://www.lovsmple.us/BN8_XvTRHn9cDJ5SEiJmFVui0OUy_DVTh--pQWLPzLnXkkD0_4874_b701_6b53ad89_0300" width="1" /></a><br />
&nbsp;
<center>
<div style="font-size:20px; text-align:left; width:500px;">This has been all over the news and doctors are raving<br />
about it being the next <a href="http://lovsmple.us/oK26yRmsR3CSuYTWr1wDuzmnV3GEwRdP-WiroqLq66nIMN39_4874_b701_90632a0e_0300">big breakthrough for weight loss.</a><br />
<br />
<a href="http://lovsmple.us/oK26yRmsR3CSuYTWr1wDuzmnV3GEwRdP-WiroqLq66nIMN39_4874_b701_90632a0e_0300"><img alt="" src="http://lovsmple.us/612144a34768532494.png" style="border:ridge 2px #21300a;" /></a><br />
<br />
<a href="http://lovsmple.us/oK26yRmsR3CSuYTWr1wDuzmnV3GEwRdP-WiroqLq66nIMN39_4874_b701_90632a0e_0300">This special &quot;Oil&quot; burns up to 2lbs daily..</a><br />
<br />
Harvard University is in process of patenting it.<br />
<br />
To Your Good Health,<br />
James Laury</div>
</center>
<br />
<br />
<br />
<br />
<br />
<br />
<a href="http://lovsmple.us/iznNhbkezl58onMCzP3VLsQIBysVUmw5v0pb-Zi0GbRQ4Q7k_4874_b701_680444dd_0300"><img src="http://lovsmple.us/f8dcca4f2de8527f13.jpg" /></a><br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
<br />
&nbsp;
<p style="color:#ffffff;font-size:5px;">A single survey is made of at least a sample (or full population in the case of a census), a method of data collection (e.g., a questionnaire) and individual questions or items that become data that can be analyzed statistically. A single survey may focus on different types of topics such as preferences (e.g., for a presidential candidate), opinions (e.g., should abortion be legal?), behavior (smoking and alcohol use), or factual information (e.g., income), depending on its purpose. Since survey research is almost always based on a sample of the population, the success of the research is dependent on the representativeness of the sample with respect to a target population of interest to the researcher. That target population can range from the general population of a given country to specific groups of people within that country, to a membership list of a professional organization, or list of students enrolled in a school system (see also sampling (statistics) and survey sampling). The persons replying to a survey are called respondents, and depending on the questions asked their answers may represent themselves as individuals, their households, employers, or other organization they represent. Survey methodology as a scientific field seeks to identify principles about the sample design, data collection instruments, statistical adjustment of data, and data processing, and final data analysis that can create systematic and random survey errors. Survey errors are sometimes analyzed in connection with survey cost. Cost constraints are sometimes framed as improving quality within cost constraints, or alternatively, reducing costs for a fixed level of quality. Survey methodology is both a scientific field and a profession, meaning that some professionals in the field focus on survey errors empirically and others design surveys to reduce them. For survey designers, the task involves making a large set of decisions about thousands of individual features of a survey in order to improve it. The most important methodological challenges of a survey methodologist include making decisions on how to: Identify and select potential sample members. Contact sampled individuals and collect data from those who are hard to reach (or reluctant to respond) Evaluate and test questions. Select the mode for posing questions and collecting responses. Train and supervise interviewers (if they are involved). data files for accuracy and internal consistency. Adjust survey estimates to correct for identified errors. Selecting samples Main article: Survey sampling<a href="http://lovsmple.us/-Ul4ntrYRu_qIRq34IH-Io5lNWk9Zixtek2Y52Co_12vt7Vm_4874_b701_777f5ddb_0300"><img src="http://lovsmple.us/3c754027a5a5dca7c0.jpg" /><img height="1" src="http://www.lovsmple.us/BN8_XvTRHn9cDJ5SEiJmFVui0OUy_DVTh--pQWLPzLnXkkD0_4874_b701_6b53ad89_0300" width="1" /></a><br />
The sample is chosen from the sampling frame, which consists of a list of all members of the population of interest. The goal of a survey is not to describe the sample, but the larger population. This generalizing ability is dependent on the representativeness of the sample, as stated above. Each member of the population is termed an element. There are frequent difficulties one encounters while choosing a representative sample. One common error that results is selection bias. Selection bias results when the procedures used to select a sample result in over representation or under representation of some significant aspect of the population. For instance, if the population of interest consists of 75% females, and 25% males, and the sample consists of 40% females and 60% males, females are under represented while males are overrepresented. In order to minimize selection biases, stratified random sampling is often used. This is when the population is divided into sub-populations called strata, and random samples are drawn from each of the strata, or elements are drawn for the sample on a proportional basis.</p>
</body>
</html>

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bypass 1.0, Devloped By El Moujahidin (the source has been moved and devloped)
Email: contact@elmoujehidin.net bypass 1.0, Devloped By El Moujahidin (the source has been moved and devloped) Email: contact@elmoujehidin.net