## 统计代写|R语言代写R language代考|looking at the diStRiBution oF data

When doing statistical analysis it is important to get a “picture” of the data. You usually want to know if the observations are clustered around some middle point (the average) and if there are observations way out on their own (outliers). This is all related to the distribution of the data. There are many distributions, but common ones are the normal distribution, Poisson, and binomial. There are also distributions relating directly to statistical tests; for example, chi-squared and Student’s $t$.

It is necessary to look at your data and be able to determine what kind of distribution is most adequately represented by them. It is also useful to be able to compare the distribution you have with one of the standard distributions to see how your sample matches up.

You already met the table ( ) command, which was a step toward gaining an insight into the distribution of a sample. It enables you to see how many observations there are in a range of categories. This section covers other general methods of looking at data and distributions.

The table () command gives you a quick look at a vector of data, but the result is still largely numeric. A graphical summary might be more useful because a picture is often easier to interpret. You could draw a frequency histogram, and indeed you do this in the following section, but you can also use a stem and leaf plot as a kind of halfway house. The stem () command produces the stem and leaf plot.

In the following activity you will use the stem () command and compare it to the table() command that you used in Chapter 4.

## 统计代写|R语言代写R language代考|density Function

You have seen in drawing a histogram with the hist () command that you can use freq = FALSE to force the $y$-axis to display the density rather than the frequency of the data. You can also call on the density function directly via the density () command. This enables you to draw your sample distribution as a line rather than a histogram. Many statisticians prefer the density plot to a regular histogram. You can also combine the two and draw a density line over the top of a regular histogram.
You use the density () command on a vector of numbers to obtain the kernel density estimate for the vector in question. The result is a series of $x$ and $y$ coordinates that you can use to plot a graph. The basic form of the command is as follows:
density $(\mathrm{x}$, bw $=$ ‘nrd0’, kernel $=$ ‘gaussian’, na.rm = FALSE $)$
You specify your data, which must be a numerical vector, followed by the bandwidth. The bandwidth defaults to the nrdo algorithm, but you have several others to choose from or you can specify a value. The kerne1 = instruction enables you to select one of several smoothing options, the default being the “gaussian” smoother. You can see the various options from the help entry for this command. By default, NA items are not removed and an error will result if they are present; you can add na.rm $=$ TRUE to ensure that you strip out any NA items.
If you use the command on a vector of numeric data you get a summary as a result like so:The result actually comprises several items that are bundled together in a list object. You can see these items using the names () or str () commands:You can extract the parts you want using $\$$as you have seen with other lists. You might, for example, use the$\$x$ and $\$ y$parts to form the basis for a plot. # R语言代考 ## 统计代写|R语言代写R language代考|looking at the diStRiBution oF data 在进行统计分析时，重要的是要获得数据的“图片”。您通常想知道观察值是否聚集在某个中间点（平均值）周围，以及是否存在独立的观察值（离群值）。这都与数据的分布有关。分布有很多种，但常见的有正态分布、泊松分布和二项分布。也有直接与统计测试相关的分布；例如，卡方和 Student’s吨. 有必要查看您的数据并能够确定它们最充分地代表了哪种分布。能够将您拥有的分布与标准分布之一进行比较以查看您的样本如何匹配也很有用。 您已经遇到了 table ( ) 命令，这是深入了解样本分布的一个步骤。它使您能够查看某个类别范围内有多少观测值。本节介绍查看数据和分布的其他一般方法。 table() 命令可让您快速查看数据向量，但结果仍然主要是数字。图形摘要可能更有用，因为图片通常更容易理解。您可以绘制频率直方图，实际上您会在下一节中这样做，但您也可以使用茎叶图作为一种中途之家。stem () 命令生成茎叶图。 在接下来的活动中，您将使用 stem () 命令并将其与您在第 4 章中使用的 table() 命令进行比较。 ## 统计代写|R语言代写R language代考|density Function 你已经看到在用 hist() 命令绘制直方图时，你可以使用 freq = FALSE 来强制是-axis 显示密度而不是数据的频率。您也可以通过 density () 命令直接调用密度函数。这使您能够将样本分布绘制为一条线而不是直方图。许多统计学家更喜欢密度图而不是常规直方图。您还可以将两者结合起来，在常规直方图的顶部绘制一条密度线。 您可以在数字向量上使用 density () 命令来获取相关向量的核密度估计。结果是一系列X和是可用于绘制图形的坐标。命令的基本形式如下： density(X, 乙=’nrd0’，内核=’高斯’，na.rm = FALSE) 您指定您的数据，它必须是一个数值向量，后跟带宽。带宽默认为 nrdo 算法，但您可以选择其他几种算法，也可以指定一个值。kerne1 = 指令使您能够选择多个平滑选项之一，默认为“高斯”平滑器。您可以从该命令的帮助条目中看到各种选项。默认情况下，NA 项目不会被删除，如果它们存在则会导致错误；你可以添加 na.rm=TRUE 以确保您去除任何 NA 项目。 如果您在数字数据向量上使用该命令，您将得到如下摘要结果： 结果实际上包含几个项目，这些项目捆绑在一个列表对象中。您可以使用 names() 或 str() 命令查看这些项目：您可以使用提取您想要的部分$正如您在其他列表中看到的那样。例如，您可以使用$X和$是部分构成情节的基础。

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