### 统计代写|数据可视化作业代写data visualization代考|Line Charts, Column Charts, and Bar Charts

statistics-lab™ 为您的留学生涯保驾护航 在代写数据可视化data visualization方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写数据可视化data visualization方面经验极为丰富，各种代写数据可视化data visualization相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|数据可视化作业代写data visualization代考|Line Charts

A line chart uses a point to represent a pair of quantitative variable values, one value along the horizontal axis and the other on the vertical axis, with a line connecting the points. Line charts are very useful for time series data (data collected over a period of time: minutes, hours, days, years, etc.). As an example, let us consider Cheetah Sports. Cheetah sells running shoes and has retail stores in shopping malls throughout the United States. The file Cheetah contains the last ten years of sales for Cheetah Sports, measured in millions of dollars. These data are shown in Figure 2.11. Figure $2.12$ displays a scatter chart and a line chart created in Excel for these sales data.

The following steps create the line chart of the Cheetah Sports sales data shown in Figure $2.12 \mathrm{~b}$.
Step 1. Select cells A1:B 11
Step 2. Click the Insert tab on the Ribbon
Step 3. In the Charts group, click the Insert Scatter (X,Y) or Bubble Chart button :”
Select Scatter with Straight Lines and Markers
Edit the chart as described in Section 2-2
Comparing Figure $2.12 \mathrm{~b}$ with Figure $2.12 \mathrm{a}$, the addition of lines between the points suggests continuity and makes it is easier for the reader to see and interpret changes that have occurred over time.

## 统计代写|数据可视化作业代写data visualization代考|Column Charts

A column chart displays a quantitative variable by category or time period using vertical bars to display the magnitude of a quantitative variable. We have seen an example of a column chart in the zoo attendance data, where the categories are months of the year and the quantitative variable is zoo attendance. Let us elaborate more about when to use a column chart by continuing the Cheetah Sports annual sales example shown in Figure $2.11$.

The following steps create the column chart of the Cheetah Sports sales data shown in Figure 2.15.
Step 1. Select cells A1:B11
Step 2. Click the Insert tab on the Ribbon
Step 3. In the Charts group, click the Insert Column or Bar Chart button InSelect Clustered Column $\sharp$ HIl

Excel displays the year as if it is a quantitative variable. To correct this, we need the following steps:
Step 4. Right click the chart and select Change Chart Type…
Step 5. When the Change Chart Type task pane appears, select the Cluster Column type that plots the appropriate number of variables (in this case, the single variable Sales plotted with ten monochromatic columns) and click OK Edit the chart as outlined in Section 2-2
The next step adds data labels to the bars.
Step 6. Click the Chart Elements button $+$ and select Data Labels.

The line chart in Figure $2.12 \mathrm{~b}$, and the column chart in Figure 2.15, are both good displays of the Cheetah Sports annual sales. The line chart, with its connected lines, makes it easier to see how the sales are changing over time. The column chart, with its data labels, is preferred if it is important for the audience to know the values of sales in each year. Moreover, adding data labels to a line chart generally makes the chart too cluttered. On the other hand, if there are numerous categories or time periods, the line chart (without data labels) would be preferred over the column chart with data labels because the column chart would appear too cluttered and labels would not be readable.

## 统计代写|数据可视化作业代写data visualization代考|Bar Charts

A bar chart shows a summary of categorical data using the length of horizontal bars to display the magnitude of a quantitative variable. That is, a bar chart is a column chart turned on its side. Like column charts, bar charts are useful for comparing categorical variables and are most effective when you do not have too many categories. Figure $2.2$ in the Data Visualization Makeover of the Allocation of Funds in New York City is a good example. As shown in that example, a bar chart can be a good substitute for a pie chart when showing composition. Sorting the data as in Figure $2.2$ makes the rank order of the components by the magnitude of the quantitative variable more obvious. A bar chart is preferred over a column chart if there are lengthy category names because it is easier to display the names horizontally (for improved legibility). However, for time series data, a column chart is better as it is more natural to display the passage of time from left to right horizontally.

A clustered bar chart displays multiple quantitative variables for categories or time periods using the length of horizontal bars to denote the magnitude of the quantitative variables and separate bars and colors to denote the different variables. Like a stacked column chart, a stacked bar chart is a bar chart that uses color to denote the contribution of each subcategory to the total. As with column charts, clustered and stacked bar charts are available in Excel by clicking on the Insert Column or Bar button in the Charts group and then selecting either Clustered Bar $\equiv$ or Stacked Bar $\square$

## 统计代写|数据可视化作业代写data visualization代考|Column Charts

Excel 将年份显示为一个定量变量。为了纠正这个问题，我们需要以下步骤：

## 广义线性模型代考

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## 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。