- To upload data files, preview data set, and check the correctness of data input
- To pre-process some variables (when necessary) for building the model
- To achieve the basic descriptive statistics and plots of the variables

- Your data need to have more rows than columns
- Your data need to be all numeric

**Data Preview**

**1. Numeric variable information list**

**2. Categorical variable information list**

**Linear fitting plot**: to roughly show the linear relation between any two numeric variable.
Grey area is 95% confidence interval.

**3. Change the labels of X and Y axes**

**Histogram**: to roughly show the probability distribution of a variable by depicting the frequencies of observations occurring in certain ranges of values.

**Density plot**: to show the distribution of a variable

**Histogram and Density Plot**

When the number of bins is 0, plot will use the default number of bins

**Density plot**

- From
to estimate the number of components - To achieve a correlation matrix and draw plots
- To achieve the principal components and loadings result tables
- To gachieve the principal components and loadings distribution plots in 2D and 3D

- All the data for analysis are numeric
- More samples size than the number of independent variables, that is, the number of rows is greater than the number of columns

Please edit data in Data tab

- This plot graphs the components relations from two components, you can use the score plot to assess the data structure and detect clusters, outliers, and trends
- Groupings of data on the plot may indicate two or more separate distributions in the data
- If the data follow a normal distribution and no outliers are present, the points are randomly distributed around zero

**2. When A >=2, choose 2 components to show component and loading 2D plot**

- This plot show the contributions from the variables to the PCs (choose PC in the left panel)
- Red indicates negative and blue indicates positive effects
- Use the cumulative proportion of variance (in the variance table) to determine the amount of variance that the factors explain.
- For descriptive purposes, you may need only 80% (0.8) of the variance explained.
- If you want to perform other analyses on the data, you may want to have at least 90% of the variance explained by the factors.

**Loadings**

**Variance table**

- This plot (biplots) overlays the components and the loadings (choose PC in the left panel)
- If the data follow a normal distribution and no outliers are present, the points are randomly distributed around zero
- Loadings identify which variables have the largest effect on each component.
- Loadings can range from -1 to 1. Loadings close to -1 or 1 indicate that the variable strongly influences the component. Loadings close to 0 indicate that the variable has a weak influence on the component.

**When A >=2, choose 2 components to show component and loading 2D plot**

- This is the extension for 2D plot. This plot overlays the components and the loadings for 3 PCs (choose PCs and the length of lines in the left panel)
- We can find the outliers in the plot.
- If the data follow a normal distribution and no outliers are present, the points are randomly distributed around zero
- Loadings identify which variables have the largest effect on each component
- Loadings can range from -1 to 1. Loadings close to -1 or 1 indicate that the variable strongly influences the component. Loadings close to 0 indicate that the variable has a weak influence on the component.

**This plot needs some time to load for the first time**

**When A >=3, choose 3 components to show component and loading 3D plot**

*The default is to show the first 3 PC in the 3D plot*

**Trace legend**

- From
**parallel analysis**to estimate the number of components - To achieve a correlation matrix and plots
- To achieve the factors and loadings result tables and
- To achieve the factors and loadings distribution plots in 2D and 3D

- All the data for analysis are numeric
- More samples size than the number of independent variables, that is, the number of rows is greater than the number of columns

Please edit data in Data tab

- This plot graphs the factor relations to the variables
- Results in the window show the statistical test for the sufficiency of factors.

- This plot graphs the relations from two factors, you can use the score plot to assess the data structure and detect clusters, outliers, and trends
- Groupings of data on the plot may indicate two or more separate distributions in the data

**2. When A >=2, choose 2 factors to show component and loading 2D plot**

- This plot show the contributions from the variables to the PCs (choose PC in the left panel)
- Red indicates negative and blue indicates positive effects
- Use the proportion of variance (in the variance table) to determine the amount of variance that the factors explain.
- For descriptive purposes, you may need only 80% (0.8) of the variance explained.
- If you want to perform other analyses on the data, you may want to have at least 90% of the variance explained by the factors.

**Loadings**

**Variance table**

- This plot (biplots) overlays the factors and the loadings (choose PC in the left panel)
- Loadings identify which variables have the largest effect on each component
- Loadings can range from -1 to 1. Loadings close to -1 or 1 indicate that the variable strongly influences the component. Loadings close to 0 indicate that the variable has a weak influence on the component.

**When A >=2, choose 2 factors to show factors and loading 2D plot**

- This is the extension for 2D plot. This plot overlays the factors and the loadings for 3 PCs (choose PCs and the length of lines in the left panel)
- We can find the outliers in the plot.
- Loadings identify which variables have the largest effect on each component

**This plot needs some time to load for the first time**

**When A >=3, choose 3 factors to show factors and loading 3D plot**

*The default is to show the first 3 factors in the 3D plot*

**Trace legend**