Normal distribution is one of the most common probability distributions in data science and statistics. It is a symmetric distribution, where most of the data is concentrated near the mean. However, creating a Normal distribution map and judging whether the data conforms to the Normal distribution may require some skills and professional tools. In this paper, we will take Minitab software as an example to introduce how to draw a Normal distribution map and judge whether the data conforms to Normal distribution.

一、How to make minitab Normal distribution map

Minitab is a professional data analysis software with powerful functions, including drawing various Chart, carrying out descriptive statistical analysis, inferential statistical analysis, etc. Next, we will introduce how to use Minitab to draw a Normal distribution map.

1、Data preparation: Firstly, you need to prepare a set of data, which can be any numerical value you want to analyze. Enter this set of data into Minitab's worksheet.

2、Create a chart: Select the "Chart" option in the menu bar, and then select "Probability Distribution Chart".

3、Select Normal distribution: in the pop-up window, select "Normal distribution" and click "OK".

4、Select Data Column: In the next window, select the data column you want to analyze, and then click "OK".

5、View the results: Minitab will generate a Normal distribution map for your data. The curve in the figure represents the theoretical normal distribution, and the points represent your data. By comparing the distribution of points and curves, you can preliminarily judge whether the data conforms to the Normal distribution.

二、Determination of minitab Normal distribution

When using Minitab to judge the Normal distribution, the main concern is the results of the normality test and the Normal distribution map.

First, we can judge whether the data conforms to the Normal distribution according to the results of the normality test. In normality testing, p-value is an important indicator. Generally, if p-value is greater than the set significance level (such as 0.05), we can accept that the data conform to the assumption of Normal distribution. In addition, the Shapiro Wilk statistic is also one of the indicators for judging normality. If the value of Shapiro Wilk statistic is close to 1, it means that the data is more likely to conform to Normal distribution.

Secondly, the Normal distribution map also provides an intuitive way to judge. In the Normal distribution chart, if the data points are roughly distributed along a straight line and form a smooth curve, then the data can be considered to conform to the Normal distribution. However, if the data points show obvious deviation or bending, it may indicate that the data does not conform to the Normal distribution.

In addition to the normality test and Normal distribution map, we can also use other statistical indicators and methods to judge the Normal distribution. For example, indicators such as skewness and kurtosis can be used to describe the skewness and kurtosis of data. The skewness of the Normal distribution should be close to 0, and the kurtosis should be close to 3. By comparing these indicators with the theoretical values of Normal distribution, we can further judge the normality of the data.

Summarize

Using Minitab to make Normal distribution map and judge Normal distribution can help us better understand and analyze the distribution characteristics of data. Through the generation of the Normal distribution map and the results of the normality test, we can judge whether the data conforms to the Normal distribution. At the same time, combined with skewness, kurtosis and other indicators, a more comprehensive and comprehensive Normal distribution judgment can be made.

It should be noted that Minitab, as a professional statistical data software, has rich functions and wide applications, especially suitable for quality management, engineering, and production fields. By proficiently mastering the usage methods of Minitab, we can better conduct statistical analysis and quality improvement, and improve the success rate of our business and projects.