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This paper will test your abilities to write an argumentative paper about a controversial topic in way that fairly considers both sides of the issue: Usethe concept...

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This paper will test your abilities to write an argumentative paper about a controversial topic in way that fairly considers both sides of the issue: Usethe concepts of Rogerian Argument--try to acknowledge the good points of the opposition = and, ifpossible; establish common groundIopic On July 2,.2017. President Trump tweeted the following:My use of social media is not Presidential_it $ MODERN DAY PRESIDENTIAL Make America Great Again!This followed criticism ofhis use of Twitter t0 speak dire

This paper will test your abilities to write an argumentative paper about a controversial topic in way that fairly considers both sides of the issue: Usethe concepts of Rogerian Argument--try to acknowledge the good points of the opposition = and, ifpossible; establish common ground Iopic On July 2,.2017. President Trump tweeted the following: My use of social media is not Presidential_it $ MODERN DAY PRESIDENTIAL Make America Great Again! This followed criticism ofhis use of Twitter t0 speak directly to his followers &nd bypass the usual means (he would say "constraints'") of addressing the public through the media Research this controversy: Why do Peopk object to bis use of Twitter? What has been the norm in the past for the way presidents address the American people? What are the requirements for archiving statements by the President; and what are the problems, then; when those statements are made Via communication platform that allows the President to delete his statements? After you research the issue: You will wite 2 paper about whether or not you believe a president should use Twiter. You are not writing an argument about whether President Trump should use Twitter;, but rather whether anV president should use Twitter You will need to include in-text citations and 2 Works Cited page (which does not count as one ofyour pages for length) . Consult the MLA section inyour Handbook Details: You must use at Ieast two reputable sources: Anything Jou state as a fact (sukchl = ~Young people don tuse Twitter" must & e supported by evidence Your paper must be +5 pages in length double-spaced in a Times New Roman 12 font Your paper must be subriltted to



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Write a summary of the following opinion. The opinion was posted at abcnews.com. Include the type of study conducted, possible lurking variables, and conclusions. What is the message of the author of the article? New Research May Couse More Fear Than Warranted. One Physician Explains OPINION by JOSEPH MOORE, M.D. A recent study out of Switzerland indicates there might be an increased risk of certain blood cancers in people with prolonged exposure to electromagnetic fields, like those generated from high-voltage power lines. If you live in a house near one of these high-voltage power lines. a study like this one might make you wonder whether you should move. But based on what we know now, I don't think that's necessary. We can never say there is no risk, but we can say that the risk appears to be extremely small. -Scare Science" The results of studies like this add a bit more to our knowledge of potential harmful environmental exposures, but they should also be seen in conjunction with the results of hundreds of studies that have gone before. It cannot be seen as a definitive call to action in and of itself. The current study followed more than 20,000 Swiss railway workers over a period of 30 years. True, that represents a lot of people over a long period of time. However, the problem with many epidemiological studies, like this one, is that it is difficult to have an absolute control group of people to compare results with. The researchers compared the incidence of different cancers of workers with a high amount of electromagnetic field exposure to those workers with lower exposures. These studies aren't like those that have identified definitive links between an exposure and a disease Ike those involving smoking and lung cancer. In those studies. we can actually measure the damage done to lung tissue as a direct result of smoking. But usually it's very difficult for the conclusions of an epidemiological study to rise to the level of controlled studies in determining public policy. Remember the recent scare about coffee and increased risk of pancreatic cancer? Or the always simmering issue of cell phone use and brain tumors? As far as I can tell, none of us have turned in our cell phones. In our own minds, we've decided that any links to cell phone use and brain cancer have not been proven definitively. While we can't say that there is absolutely no risk in using cell phones. individuals have determined on their own that the potential risks appear to be quite small and are outweighed by the benefits. Findings Shouldn't Lead to Fear As a society, we should continue to investigate these and other related exposures to try to prove one way or another whether they are disease-causing. If we don't continue to study. we won't find out. It's that simple. When findings like these come out, and l'm sure there will be more in the future. I would advise people not to lose their heads. Remain calm. You should take the results as we scientists do - as intriguing pieces of data about a problem we will eventually learn more about, either positively or negatively, in the future. It should not necessarly alter what we do right now. What we can do is take actions that we know will reduce our chances of developing cancer. Stop smoking and avoid passive smoke. It is the leading cause of cancer that individuals have control over. Whenever you go outside, put on sunscreen or cover up. Eat a heaithy diet and stay physically active. Make sure you get tested or screened. Procedures like colonoscopies. mammograms. pap smears and prostate exams can catch the early signs of cancer. when the chances of successfully treating them are the best. Taking the actions above will go much farther in reducing your risks for cancer than moving away from power lines or throwing away your cell phone.
Dr. Joseph Moore is a medical oncologist at Duke University Comprehensive Cancer Center. Source: Reprinted with the permission of the author.

Okay, Now, this is a big paragraph that will have to go through. And what we want to do is we want to summarize this rate. We want to summarize this. So what? How can we do that? Well, this is a prospective study. This is a cohort observational study. The first thing that we see is that this is a cohort observational study cohort, observational study. Which means what? That this is prospective in nature. This is for us, effective in nature. Some plop. You know, some possible lurking variables can be smoking habits, eating habits, exercise, family history. So there are habits. These can be some lurking variables. And then there is also a history of the family, right? Do you have any family history? Okay. Some family history of cancer. The study concluded that there might be an increased risk off certain blood cancers in people with prolonged exposure toe electromagnetic fields. The author of the article also reminds us that this is an observation study. So there is no control off variables that may affect the likelihood of getting certain cancers. In addition, the author also states that we should do things in our lives that promote health. So what we need to do is we have toe work on good habits in order to promote our health. And these would be you know, this is how we would summarize this.

Yeah. All right guys. So if you read the passage and problem number 25 Mark Twain is kind of being silly here, but he was referring to the a couple important facts. Number one, linear regression model is not always appropriate. It might have been appropriate. Um from like the time of what he was referring to like 1722-1840, It might have been appropriate for that time period, but it might not be appropriate for the entire time period. Because he said, Yeah, if you go way back in time, it would have been really, really long and in the in the future it'll be really, really um it will not be very, very long. So, um yes, so in some cases least card aggressions line are not appropriate. Also guys, it's also important to remember that linear regression models and predictions should only be done for data within our data set. Not data outsider dataset. Because we try to use outsider data set. We could come into silly examples such as this where the Mississippi would be super super long in the distant past and also be super super short in the distant future. Right?

This is a problem. # 34, we are given a quote from Mark Twain's book Life on the Mississippi. In this, he makes an observation about the statistical tendencies of some people to take information at face value without really thinking of the broader scope of things. In this case, he says that in the space of a 176 years, the Mississippi has shortened itself by 242 miles, which making the observation there gives an average of one mile, one and a third mile per year. So any sensible person would see that based on this data. If you take it at face value, the lower Mississippi was over a million miles long over a million years ago and in about 700,000 years from now will be only about a mile long, both of which seem absolutely observed. This is because a specific model can only be representative of a short period of time and cannot be extended to any infinite amount. Again, this refers back to the scope of the model, where we can only take a certain model within the realm of data that we have already acquired, and shouldn't really use it to predict too far ahead of in the future, as things may be different.

In this video, we're going to look at log arrhythmic transformations and the power law model. We can use a law algorithmic transformation to determine if the power law is a good power law model is actually a good fit for our data. Now, the power law formula is right here, Y equals alpha X to the beta. And in our particular example, X represents the time in microseconds and Y equals the amps built up in the circuit at time X. So here are exes and here's our why. First thing we're asked to do is we're asked to find ex prime and why prime these are transformations. So ex prime will be log of X and Y. Prime will be log of Y. And then we're asked to graph. Actually we're asked to find the regression model for the graph of ex prime Y. Prime. So I would find a log of two. This would be my first ex and then I'm going to round it, so Log of two again on the calculator log too. And then I would also need to find log 1.81 and I can do that for each of my values. And then I would graph that information. But there's actually a faster way to do it. So I'll go back to my calculator but this time I'm going to go to my lists. So stat number one. Edit notice I already have the information in L. One and L. Two. What I'm gonna do on L. three and L four is figure out the logs of those values. So I'm going to type log to so log of my original X. Value and press enter and you can see this automatically calculates it for me. So I don't have to go through and do it all by hand, log for log six, log eight, log 10. Remember the log of 10 is one because those are in verses. All right. I'm going to pause the video and then I'm going to enter in the logs of the Y. Values. And when I come back or in a second you will see the results of that. So, now I have the log of my Y. Each Y value entered into al. For so to find the regression model, select stat cursor over to couch, choose number eight. The linear regression model in the form of A plus B. X. But I don't want L one, L two. I want L. Three and L. Four. So second three will put L. Three under my ex list. Second four puts L four under my wild list, cursor down to calculate and now I have the X. Value or the A. Value, the B. Value. And my correlation coefficient so is 0.128 B is 0.492 depending again on how you need around. And rs 0.987 So my model will be Why prime is approximately 0.128 plus 0.492 Ex prime. So there's my linear regression model. So that is part B. So part C. Then is to find my model the my power model. And I can do that algebraic lee by using this Y. Prime equation. But we're actually just going to use our calculator. And then if you'd like to see how you find the model drive it from the linear regression model you can but I figured most of you will just want the answer. So we'll go through and I go back to my calculator stat calc But this time I want to calculate power regression. If you cursor down you'll see here that it is at letter A. When I select it. I don't want L. three and L. four because remember those weren't my original Values. So I'm gonna do l. 1 2nd 1 second to. So all one L two years might already be default to L. One L. Two, Calculate enter and you can see now I have alpha or in this case a 1.34 37 Or 1.3438 And then .4919. Now this will depend on again how how your instructor wants you around or how the textbook wants you around it. So it may be a little bit different. Okay so there is your power model and again rounding some texts might have it like this. There would be another model again rounded differently. Okay so if you're done, pause the video. If you want to find out how we take our linear regression model and make it into a power model, then stick around. So recall that the UAE prime and the ex prime are actually logs of those values. Now I'm gonna exponentially eight both sides. Bye. 10 And 10 log of Y will become why. And now I need to remember some of my exponential rules or exponents rules. So when I'm adding exponents, that means in my original equation I was multiplying like bases. So this actually will end up being our alpha and then this one I have to do a little more work. I have to rearrange it a little bit more. So I'm going to rearrange it because remember with our power roll you copy down the base and you multiply the exponents. So I'm going to reverse the power rule. Can remember this is multiplication. So 10 log of x becomes x. So this will be alpha, here's beta and when I use my calculator, second quit. So 10 carrot .128 for at the decimal point, You can see there's our 1.343 When we round it or 1.34-7. So again, it's a little different from when we use our calculator. And again, that has to do with how we end up a rounding our information.


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