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Unit1: Exploring One-Variable Data (15-23%)
Categorical Data
Categorical Data 分类变量 is shown in two-way tables and bar graphs, analyzing proportions.
- Data represents groups or categories
- Measured in proportions
- Example: eye color
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The association between two categorical variables
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We say that there is an association between two variables if knowing the value of one variable helps predict the value of the other. If knowing the value of one variable does not help you predict the value of the other, then there is no association between the variables.
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Bar Chart
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The columns are positioned over a label that represents a categorical variable.
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Segmented Bar Chart - Bivariate Categorical Data Visualization
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No Association means (Independent):
The conditional distributions of opinion about becoming rich would be the same for males and females. The segmented bar graphs for the two genders would look the same, too.
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Side by Side Bar Chart - Bivariate Categorical Data Visualization
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Mosaic Plot - Bivariate Categorical Data Visualization
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Graphic Two Way Table with percentages
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We can use mosaic plots to draw conclusions about relationships between two categorical variables.
Quantitative Data
Quantitative Data 数值变量 is displayed in histograms, dotplots, box plots, stem and leaf plots, and scatter plots.
- Measured or counted variables
- Measure in means
- Example: height, age
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Histogram
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The columns are positioned over a label that represents a quantitative variable.
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For a histogram -> make sure you approximate the mean (e.g.10%-12.5%) and use words like “no more” / “approximately” when describing range.