# About the Summary view

The Summary view provides an overview of all columns in a table with charts and descriptive statistics. It is also where you can create computed columns and perform simple statistical tests.

The Summary view is accessible by clicking Summary in the toolbar with a table of interest selected in the navigator. The left part of the Summary view displays a list of columns in the table along with summary statistics.

The right part of the Summary view is divided into a column summary view (top) and covariate view (bottom). The column summary view displays clickable charts of the column selected on the left. In the covariate view, you can select a second column of interest and see a summary of the interaction between the column selected on the left and the column selected on the right.

The types of charts and tabulations displayed depends on the data types of the selected columns, and whether they are being treated as categories or survival times:

- If a category column is selected on the left, the column summary will display pie charts
  - If a column is selected on the right, the covariate summary will display the sample or estimated proportions of the left-hand categories broken down by values of the right-hand column
  - If no column is selected on the right, the covariate summary will display a bar chart of the number of times each category appears in the left-hand column
- If a numeric column is selected on the left, the column summary will display a histogram and a visual t-test
  - If a column is selected on the right, the covariate summary will display a histogram, scatterplot, t-test, or box-plot for each group of values of the right-hand column, or a scatterplot of the left-hand column against the right-hand column
  - If no column is selected on the right, the covariate summary will display a Q-Q plot of the quantiles of the left-hand column against the quantiles of a uniform or normal distribution
- If a survival-times column is selected on the left, the column summary will display survival curves
  - If a column is selected on the right, the covariate summary will display survival curves of the left-hand column broken down by values of the right-hand column
  - If no column is selected on the right, the covariate summary will display a survival curve of the left-hand column

Finally, the bottom of the Summary view contains a feature called The Bottom Line, which provides an intuitive interpretation of the statistical test currently being performed.

General tasks:

- Treating a column as categorical data
- Treating a column as survival times
- Partitioning a non-category covariate into discrete groups in the Summary view
- About row multipliers (frequency weights)

Single-column summaries:

- Viewing a histogram of a numeric column
- Viewing a pie chart of a category column
- Viewing a survival curve of a numeric or date column
- Viewing a Q-Q plot of a numeric column against a normal or uniform distribution

Two-column summaries:

- Viewing a scatterplot of two numeric columns
- Viewing the correlation coefficient between two numeric columns
- Viewing the estimated population of a category column broken down by values of another column
- Viewing histograms of a numeric column broken down by values of another column
- Viewing box plots of a numeric column broken down by values of another column
- Viewing survival curves of a date or numeric column broken down by values of another column

Statistical tests:

- t-tests
  - Performing a t-test of a numeric column’s mean across two categories
  - Performing an unpaired t-test of two numeric columns’ means
  - Performing a paired t-test of two numeric columns’ means
- Analysis of variance (ANOVA)
  - Performing an ANOVA test of a numeric column’s mean across values of another column
  - Performing an ANOVA test of three or more numeric columns’ means
  - Performing a repeated measures ANOVA test of three or more numeric columns’ means
  - Performing a two-way ANOVA test of a numeric column’s mean across values of two other columns
- Median tests
  - Performing a Mann-Whitney test of a numeric column’s median across two categories
  - Performing a Mann-Whitney test of two numeric columns’ medians
  - Performing a Kruskal-Wallis test of a numeric column’s median across values of another columns
  - Performing a Kruskal-Wallis test of three or more numeric columns’ medians
- Non-parametric tests
  - Performing a Wilcoxon signed-rank test of the median difference between two numeric columns
  - Performing a Friedman test of the relative values of three or more numeric columns
- Distribution tests
  - Performing a Shapiro-Wilk test of normality on a numeric column
  - Performing a Shapiro-Wilk test on the normality of differences between two columns
  - Performing a Kolmogorov-Smirnov test of uniformity on a numeric column
  - Performing a Kolmogorov-Smirnov test of a numeric column’s distribution across values of another column
  - Performing a Kolmogorov-Smirnov test of two or more numeric columns’ distributions
- Contingency table analysis
  - Performing a chi-square test of a category column’s independence from another category column
  - Performing a chi-square test of two or more category columns’ distributions
  - Performing a McNemar test for the marginal homogeneity of two category columns that are matched or related
  - Performing a Cochran’s Q test for the marginal homogeneity of three or more binary columns that are matched or related
- Survival analysis
  - Performing a one-sample log-rank test for constant hazards on a date or numeric column
  - Performing a log-rank test for the equality of hazard rates across values of another column
  - Performing a log-rank test for the equality of hazard rates of two or more columns

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