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统计代写| Double Blind Desig抽样理论代考

统计代写

Thanks to our computing capabilities it is easy to have huge data sets at one’s fingertips. It may be tempting to exhaustively test multiple hypotheses based on the data. Such practice is called data dredging (or data fishing) and frequently leads to false conclusions. Typically one looks for a P value of $0.05$ or less. If enough tests are performed, then about $5 \%$ of the tests will lead to erroneously reject the null hypothesis.
To avoid drawing false conclusions, if we use a data set to formulate a hypothesis, then we should not use that same data set to test this hypothesis. We should instead collect fresh data to test that hypothesis.
Problems

1. I want to estimate the number of high school math courses a typical UCCS freshman has taken. To do my estimate I use the following methods.
(a) I pick the first 100 students in alphabetical order in the incoming students list. I count their math courses. Is this acceptable?
(b) I check the transcript of every student in my Calculus 1 class. Is this acceptable?
2. In 1936 the presidential election was F.D.Roosevelt against A. Landon. The Literary Digest magazine sampled $2.4$ million individuals and predicted the victory of Landon by $57 \%$ to $43 \%$. However, Roosevelt won by $62 \%$ to $38 \%$ !
How can such a large sample be so wrong?
3. In a poll it was found out that $19 \%$ of biology teachers believe that humans and dinosaurs lived at the same time.
(a) What is the significance of this survey if it was sent to 20,000 teachers and there were 200 responses?
(b) What if it was sent to 400 teachers picked at random and there were 200 responses?
4. I perform 100 independent statistical tests at the $5 \%$ level. What is the probability that I will draw at least one wrong conclusion?
5. The vast majority of $P$ values reported in the medical literature are very close to $5 \%$. Why is this suspicious? What type of problem does this reveal?
6. In the early $1990 \mathrm{~s}$ it was recommended that all men 50 years old or older undergo regular prostate cancer screening in the USA. In the UK on the other hand there was no such screening program. The 5 year survival rate for prostate cancer was $40 \%$ in the UK and $90 \%$ in the USA. So prostate cancer screening saves lives. USA and in the UK! What was going on?
Problems
157
7. The two tables below list graduate admissions data for majors $A$ through $F$ at the U.C. Berkeley. The first table is for men, the second is for women.
\begin{tabular}{lll}
& \multicolumn{2}{l}{ Number of applicants }
\end{tabular}
$\begin{array}{lll}\text { A } & 108 & 82 \ \text { B } & 25 & 68 \ \text { C } & 593 & 34 \ \text { D } & 375 & 35 \ \text { E } & 393 & 24 \ \text { F } & 341 & 7\end{array}$
(a) Compare the overall admission rates for men and women. Does it seem like there is sex bias?

1. 我想估计一个典型的 UCCS 新生上过的高中数学课程的数量。为了做我的估计，我使用以下方法。
(a) 我在新生名单中按字母顺序挑选前 100 名学生。我数了数他们的数学课程。这可以接受吗？
(b) 我检查了微积分 1 课上每个学生的成绩单。这可以接受吗？
2. 1936 年的总统选举是 F.D.Roosevelt 对 A. Landon。 Literary Digest 杂志对 240 万美元的个人进行了抽样调查，并预测兰登的胜利将达到 57 美元到 43 美元。然而，罗斯福以 62 美元 \%$对 38 美元 \%$ 获胜！
这么大的样本怎么会出错？
3. 在一项民意调查中发现，$19\%$ 的生物教师认为人类和恐龙同时生活。
(a) 如果这项调查发给 20,000 名教师并有 200 份回复，那么这项调查的意义何在？
(b) 如果它被发送给随机挑选的 400 位教师并且有 200 条回复怎么办？
4. 我在 $5\%$ 的水平上进行了 100 次独立的统计测试。我至少得出一个错误结论的概率是多少？
5.医学文献中报道的绝大多数$P$值都非常接近$5\%$。为什么这很可疑？这揭示了什么类型的问题？
5. 在 1990 年初的 $mathrm{~s}$ 中，建议所有 50 岁或以上的男性在美国接受定期前列腺癌筛查。另一方面，在英国没有这样的筛选计划。前列腺癌的 5 年生存率在英国为 40 美元，在美国为 90 美元。所以前列腺癌筛查可以挽救生命。美国和英国！发生了什么事？
问题
157
6. 下面的两个表格列出了美国大学 $A$ 到 $F$ 专业的研究生招生数据。伯克利。第一张桌子是男性用的，第二张是女性用的。
\开始{表格}{lll}
& \multicolumn{2}{l}{ 申请人数 }
\end{表格}
申请人数 录取百分比
$\begin{array}{lll}\text { A } & 108 & 82 \ \text { B } & 25 & 68 \ \text { C } & 593 & 34 \ \text { D } & 375 & 35 \ \text { E } & 393 & 24 \ \text { F } & 341 & 7\end{数组}$
(a) 比较男性和女性的总体录取率。好像有性别偏见？

统计代考

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编码理论代写

1. 数据压缩（或信源编码
2. 前向错误更正（或信道编码
3. 加密编码
4. 线路码

复分析代考

(1) 提到复变函数 ，首先需要了解复数的基本性左和四则运算规则。怎么样计算复数的平方根， 极坐标与 $x y$ 坐标的转换，复数的模之类的。这些在高中的时候囸本上都会学过。
(2) 复变函数自然是在复平面上来研究问题，此时数学分析里面的求导数之尖的运算就会很自然的 引入到复平面里面，从而引出解析函数的定义。那/研究解析函数的性贡就是关楗所在。最关键的 地方就是所谓的Cauchy一Riemann公式，这个是判断一个函数是否是解析函数的关键所在。
(3) 明白解析函数的定义以及性质之后，就会把数学分析里面的曲线积分 $a$ 的概念引入复分析中， 定义几乎是一致的。在引入了闭曲线和曲线积分之后，就会有出现复分析中的重要的定理: Cauchy 积分公式。 这个是易分析的第一个重要定理。