# 线性代数网课代修|最小二乘法代写least squares method辅导|SVY2105

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• 数值分析
• 高等线性代数
• 矩阵论
• 优化理论
• 线性规划
• 逼近论

## 线性代数作业代写linear algebra代考|Statistical Distributions

In nature most quantities that are observed are subject to a statistical distribution. The distribution is often inherent in the quantity being observed but might also be the result of errors introduced in the method of observation. An example of an inherent distribution can be seen in a study in which the percentage of smokers is to be determined. Let us say that one thousand people above the age of 18 are tested to see if they are smokers. The percentage is determined from the number of positive responses. It is obvious that if 1000 different people are tested the result will be different. If many groups of 1000 were tested we would be in a position to say some-thing about the distribution of this percentage. But do we really need to test many groups? Knowledge of statistics can help us estimate the standard deviation of the distribution by just considering the first group!

As an example of a distribution caused by a measuring instrument, consider the measurement of temperature using a thermometer. Uncertainty can be introduced in several ways: 1) The persons observing the result of the thermometer can introduce uncertainty. If, for example, a nurse observes a temperature of a patient as $37.4^{\circ} \mathrm{C}$, a second nurse might record the same measurement as $37.5^{\circ} \mathrm{C}$. (Modern thermometers with digital outputs can eliminate this source of uncertainty.)

## 线性代数作业代写linear algebra代考|The normal distribution

When $\mathrm{x}$ is a continuous variable the normal distribution is often applicable. The normal distribution assumes that the range of $x$ is from $-\infty$ to $\infty$ and that the distribution is symmetric about the mean value $\boldsymbol{\mu}$. These assumptions are often reasonable even for distributions of discrete variables, and thus the normal distribution can be used for some distributions of discrete variables. The equation for a normal distribution is: $$\Phi(x)=\frac{1}{\sigma(2 \pi)^{1 / 2}} \exp \left(-\frac{(x-\mu)^{2}}{2 \sigma^{2}}\right)$$ The normal distribution is shown in Figure 1.3.1 for various values of the standard deviation $\boldsymbol{\sigma}$. We often use the term standard normal distribution to characterize one particular distribution: a normal distribution with mean $\boldsymbol{\mu}=0$ and standard deviation $\boldsymbol{\sigma}=1$. The symbol $\boldsymbol{u}$ is usually used to denote this distribution. Any normal distribution can be transformed into a standard normal distribution by subtracting $\boldsymbol{\mu}$ from the values of $\boldsymbol{x}$ and then dividing this difference by $\boldsymbol{\sigma}$.

## 在这种情况下，如何学好线性代数？如何保证线性代数能获得高分呢？

1.1 mark on book

【重点的误解】划重点不是书上粗体，更不是每个定义，线代概念这么多，很多朋友强迫症似的把每个定义整整齐齐用荧光笔标出来，然后整本书都是重点，那期末怎么复习呀。我认为需要标出的重点为

A. 不懂，或是生涩，或是不熟悉的部分。这点很重要，有的定义浅显，但证明方法很奇怪。我会将晦涩的定义，证明方法标出。在看书时，所有例题将答案遮住，自己做，卡住了就说明不熟悉这个例题的方法，也标出。

B. 老师课上总结或强调的部分。这个没啥好讲的，跟着老师走就对了

C. 你自己做题过程中，发现模糊的知识点

1.2 take note

1.3 understand the relation between definitions