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## 统计代写|回归分析代写Regression Analysis代考|Main Effects of Categorical Variables

Categorical variables, also known as nominal variables, have values that you can put into a countable number of distinct groups based on a characteristic. For categorical variables, you have the variable name and the levels of that variable. The following table shows examples of several categorical variables and their levels.

With continuous variables, you can plot them on a scatterplot and see how one variable changes as you increase the value of the other variable. However, with categorical variables, you’re dealing with groups in your data that you cannot incrementally increase. Consequently, you interpret categorical variables differently in regression analysis. The levels of categorical variables represent groups in your data, and you can plot them using a boxplot, as shown below. Regression analysis estimates the mean differences between these groups and determines whether they are statistically significant.

These effects are main effects, which indicates that the effect sizes do not change based on the values of the other variables in the model.Including categorical variables in a regression model allows you to determine whether the differences in this type of graph are statistically significant while controlling for other variables in the model. Later in this section, we’ll analyze the data that this boxplot represents to determine whether the differences between the mean incomes of these groups are statistically significant.

## 统计代写|回归分析代写Regression Analysis代考|Coding Categorical Variables

Statistical software can’t take a categorical variable and directly analyze it. Instead, it converts categorical variables into indicator variables using a $(0,1)$ coding scheme. Indicator variables, also known as dummy variables, are columns of $1 s$ and $0 \mathrm{~s}$ that indicate the presence or absence of a characteristic. A 1 indicates the presence of a feature while a 0 represents its absence. The number of indicator variables depends on the number of categorical levels. To show you how this works, I’ll start with gender.

In the table, the Gender column represents the categorical data that you enter into the worksheet. The value depends on the gender of the subject for which the row corresponds. The Male and Female columns are the indicator variables based on the Gender column. The Male column contains 1 s for observations that correspond to males and 0 s for non-males. The opposite pattern applies to the Female column.

Notice how these two columns supply completely redundant information? One column predicts the other column perfectly. Statisticians refer to this as perfect multicollinearity, which creates an error if you include both in a regression model. For a categorical variable, you must omit one of the underlying indicator variables from the model, which becomes the reference level.

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。