How to Conduct Descriptive Statistics
Use descriptive statistics when you want to measure means, percentages, and other summary statistics for your variables.
Step 1: Access the Descriptive Statistics Tool
- Click Descriptive Statistics in the sidebar menu to go to the Descriptives page.
Step 2: Select Variables for Analysis
- In the descriptive setup panel, choose one or more variables (columns) you want to summarize.
- For numeric variables (e.g., satisfaction, burnout, brand loyalty), select those columns.
- For grouping variables with categories (e.g., churn, fraud, quitting, location), select the appropriate option.
- For Scatter X, select predictors (e.g., satisfaction, brand loyalty, burnout, address mismatch).
- For Scatter Y, select the outcome (e.g., Revenue, Performance).
- For the pie chart, select grouping variables with categories (e.g., churn, fraud, quitting, location).
Step 3: Run Descriptive Statistics
- Click Apply variables.
- The platform will compute summary statistics for the selected variables.
Step 4: Interpret the Output
- For continuous variables, review:
- Mean, median, standard deviation, minimum, maximum, quartiles.
- For categorical variables, review:
- Frequency counts and percentages for each category.
Step 5: Visualize Results (Optional)
- Use built-in visualization tools to create histograms, boxplots, or bar charts for your variables.
Step 6: Export or Report Results
- Use the platform’s export function to download summary tables or figures.
- Include these summaries in your reports or presentations as needed.
Step 7: Consider Limitations
- Note any missing data or outliers that may influence interpretation.
- For small sample sizes, interpret results with caution.
Step 8: Plan Follow-up Analyses
- If you identify interesting patterns or group differences, consider running further analyses (e.g., group comparisons, correlations) using the platform’s additional tools.
Example
- To summarize age and sex:
- Select “age_years” and “sex” in the descriptives panel.
- Run descriptives to obtain mean age, age range, and counts of each sex category.
Tip
- Always check the data preview and summary tables for accuracy before reporting results.
If something does not work as described
Screens are updated as the platform changes, so a button may sit in a slightly different place than the wording above suggests. When that happens, the AI assistant inside the platform is the fastest way forward: it can see the page being worked on and answer in context, at any hour. Ask it what a message means, why a file was rejected, or which step comes next.
Most problems in this part of the workflow come from the data rather than the platform. Column headers that repeat, numbers stored as text, blank rows at the bottom of a file, and inconsistent category labels are the usual causes. Checking those four things resolves the majority of failed uploads and unexpected results before any further troubleshooting is needed.
For anything that still does not resolve, email support@averisysanalytics.com with the guide title, the step reached, and what appeared instead of the expected result. Including the file name and a screenshot usually means the issue is answered in the first reply. Related instructions are listed in the full platform guide, and common questions are collected in the support FAQ.