# 统计代写|线性回归分析代写linear regression analysis代考|STA321

## 统计代写|线性回归分析代写linear regression analysis代考|The problem

Jay Leno, in one of his Tonight Show monologues several years ago, mentioned a study that found that $50 \%$ of all academic research is wrong. His punchline: there’s a $50 \%$ chance this study itself is wrong.
The study Leno referred to may actually understate the true percentage of studies that are inaccurate. The major causes of all these errors in research are likely faulty research designs and improper interpretations of the results. These accuracy issues bring into doubt the value of academic research.
Most quantitative academic research, particularly in the social sciences, business, and medicine, rely on regression analyses. The primary objective of regressions is to quantify cause-effect relationships. These cause-effect relationships are part of the knowledge that should guide society to develop good public policies and good strategies for conducting business, educating people, promoting health and general welfare, and more. Regressions are useful for estimating such relationships because they are able to “hold constant” other factors that may confound the cause-effect relationship in question. That is, regressions, if done well, can rule out reasons for two variables to be related, other than the causal-effect reason.
Here are some examples of how regressions can be used to estimate the causal effect of one factor (or a set of factors) on some outcome:

• How does some new cancer drug affect the probability of a patient surviving 10 years after diagnosis?
• How does a parental divorce affect children’s test scores?
• What factors make teachers more effective?
• What encourages people to save more for retirement?
• What factors contribute to religious extremism and violence?
• How does parental cell phone use affect children’s safety?
• How does oat-bran consumption affect bad cholesterol levels?
• Do vaccines affect the probability of a child becoming autistic?
• How much does one more year of schooling increase a person’s earnings?
• Does smiling when dropping my shirt off at the cleaners affect the probability that my shirt will be ready by Thursday?

A regression is a remarkable tool in its ability to measure how certain variables move together, while holding certain factors constant. A natural human reaction is to be mesmerized by things people do not understand, such as how regressions can produce these numbers. And so, in the roughly 10 times that I have used regression results in briefings to somewhat-high-level officials at the Department of Defense (mostly as a junior researcher, with a senior researcher tagging along to make sure I didn’t say anything dumb), the people I was briefing never asked me whether there were any empirical issues with the regression analysis I had used or how confident I was with the findings. Most of the time, based on the leading official’s response to the research, they would act as if I had just given them the absolute truth on an important problem based on these “magic boxes” called “regressions.” Unfortunately, I was caught up in the excitement of the positive response from these officials, and I wasn’t as forthright as I should have been about the potential pitfalls (and uncertainty) in my findings. And so, I usually let them believe the magic.

## 统计代写|线性回归分析代写linear regression analysis代考|The purpose of research

To understand where research goes wrong, we first have to understand the overall purpose of research. We conduct research to improve knowledge, which often involves trying to get us closer to understanding cause-effect and other empirical relationships. To demonstrate, let’s start with the highly contentious issue of global warming. You may have some belief on the probability that the following statement is true:
Human activity is contributing to global warming.
And, hopefully that probability of yours lies somewhere between $0.3 \%$ and $99.7 \%-$ that is, you may have your beliefs, but you recognize that you probably are not an expert on the topic and so there is a possibility that you are wrong. I’m guessing that most people would be below $10 \%$ or above $90 \%$ (or, even $5 \%$ and $95 \%$ ). But, for the sake of the argument, let’s say that you have a subjective probability of the statement being true $45 \%$ of the time.

Suppose a study comes out that has new evidence that humans are causing global warming. This may shift your probability upwards. If the new research were reported on cable news channel MSNBC (which leans toward the liberal side of politics) and you tended to watch MSNBC, then let’s say that it would shift your probability up by 7 percentage points (to $52 \%$ ). If you tended to watch Fox News (a more conservative channel) instead, then the news from MSNBC may shift your probability up by some negligible amount, say $0.2$ percentage points (up to $45.2 \%$ ). But, ideally, the amount that your subjective probability of the statement above would shift upwards should depend on:

• How the study contrasts with prior research on the issue;
• The validity and extensiveness of the prior research;
• The extent to which any viable alternative explanations to the current findings can be ruled out – i.e., how valid the methods of the study are.

With regression analysis, it should be the same thinking of shifting beliefs. People have some prior beliefs about some issue, say on whether class size is important for student achievement. Using regression analysis, a new study finds no evidence that class size has an effect on student achievement. This finding should not necessarily be taken as concrete evidence for that side of the issue. Rather, the evidence has to be judged based on the strength of the study relative to the strength of other studies, or the three criteria listed above. And, people would then shift their subjective probability appropriately. The more convincing the analysis, the more it should swing a person’s belief in the direction of the study’s conclusions.

This is where it is up to researchers, the media, and the public to properly scrutinize research to assess how convincing it is. As I will describe below, you cannot always rely on the peer-review process that determines what research gets published in journals.

# 线性回归分析代考

## 统计代写|线性回归分析代写linear regression analysis代考|The problem

Leno 提到的研究实际上可能低估了不准确研究的真实百分比。所有这些研究错误的主要原因可能是错误的研究设计和对结果的不正确解释。这些准确性问题使学术研究的价值受到质疑。

• 一些新的抗癌药物如何影响患者在诊断后 10 年存活的可能性？
• 父母离婚如何影响孩子的考试成绩？
• 哪些因素使教师更有效率？
• 是什么鼓励人们为退休储蓄更多？
• 哪些因素助长了宗教极端主义和暴力？
• 父母使用手机如何影响孩子的安全？
• 食用燕麦麸如何影响坏胆固醇水平？
• 疫苗会影响孩子患自闭症的概率吗？
• 一个人多上一年学能增加多少收入？
• 把我的衬衫丢在洗衣店时微笑会影响我的衬衫在星期四之前准备好的可能性吗？

## 统计代写|线性回归分析代写linear regression analysis代考|The purpose of research

• 该研究如何与之前对该问题的研究进行对比；
• 先前研究的有效性和广泛性；
• 可以排除对当前发现的任何可行替代解释的程度——即研究方法的有效性。

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