The "Properties" dialog will open and a number of fit lines will be selectable. Right click the image in this window and select "Add Fit Line at Total." To add a regression line that reflects the regression performed in the "General Statistics and Hypothesis Testing" section right-click on the chart in the output viewer and select "Edit Content." A new window will be opened, called the Chart Editor, that allows further interactions with the graphic. Select "bachelors_or_higher" as the X axis and "median_income" as the Y then click "OK." The scatterplot will appear in the Output Viewer. To obtain a scatterplot, select "Graphs"->"Legacy Dialogs"->"Scatter/Dot" to open the dialog below: The example here uses a scatterplot, but SPSS includes dialogs for many chart types, from basic bar and line graphs to more statistics-oriented visualizations like histograms, scatterplots, and boxplots. In addition to the Chart Builder, SPSS includes what it terms "Legacy Dialogs" that each create a certain chart type. Hit "OK" and the boxplot appears in the Output Viewer. Then select "median_income" from the "Variables" box and drag it to the dotted-line border area labeled "Y-Axis" in the Chart Preview box. Select the icon showing a single plot and drag it to the Chart Previews box above. The Chartbuilder works using a drag and drop interface. You'll see three icons appear in the box to the right of the menu. Select "Boxplot" in the "Choose from" menu. To demonstrate the Chart Builder's abilities, we'll create a boxplot for the "median_income" variable. This will open up the Chartbuilder dialog. To access the Chart Builder select "Graphs"->"Chart Builder" from the menu. The newest is the Chart Builder, a dialog which consolidates many of the functions available through the Legacy Dialogs. SPSS provides a number of dialogs for creating graphics. Diversity, Equity, Inclusion, & Accessibility.Notably, the PCA identifies that Si% plots away from most variables, suggesting it is a redundant variable. Based on this plot, rock fabric plotting in the NW quadrant are more brittle as they have higher Si/Al, HLD, and Ca% and are positively related to each other, but negatively related to Al% as it plots on the opposite quadrant (large angle). For example, PCA can be used to understand the relationship between elemental composition (Si/Al, Si, Al, and Ca%), mechanical strength (HLD), and coloured based on different mudstone rock fabric (blue hues) as these are important parameters that help predict geomechanical properties. To interpret PCAs, variables and data points are related based on their closeness (direction and angle) closer angles indicating positive relationships. The linear combinations of these PCs are then used to transform all measurements into a single point per sample to understand the spatial relationship between measurement and variables, where the PCs become the axes on the new plot. Each PC corresponds to a linear vector that explains a degree of variance (spread of data) with the first two PCs (PC1 and PC2) commonly responsible for most of the variance. This technique uses linear algebra to transform a dataset onto a new coordinate system referred to as principal components (PCs). PCA is a dimensionality-reduction technique that can be written in any programming language or using an excel data analysis add-on.
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