Excel Demographic Comparison Analysis
Budget: ₹750 – ₹1,250 INR
I have a spreadsheet of patient-level data and I need the story behind our demographics to be made crystal clear. The focus is strictly on patient demographics, with gender and ethnicity as the two variables of interest. What I want is a solid comparison between those groups, not just a list of averages.
You will start with the raw Excel file and clean or restructure it only as much as is required for sound analysis. From there, create the necessary pivot tables, cross-tabulations, and significance tests (chi-square or any other test that best suits categorical data) so we can see where differences actually exist. Visual summaries—charts or dashboards built right inside Excel—are important because this work will be presented to non-statisticians.
Deliverables
• One Excel workbook containing:
– a “Data” sheet (cleaned, formulas unlocked)
– an “Analysis” sheet with tables and test outputs clearly labeled
– a “Visuals” sheet or dashboard summarizing key findings
• A brief read-me note inside the file explaining methods used and how to refresh the analysis if new data are added.
If you prefer to run the stats in R or Python that’s fine, as long as every result still lands neatly back in the Excel file. Accuracy and clarity matter more than fancy formatting, so keep formulas transparent and cells well-commented.
I’m ready to share the dataset the moment we agree on the approach and timeline.
You will start with the raw Excel file and clean or restructure it only as much as is required for sound analysis. From there, create the necessary pivot tables, cross-tabulations, and significance tests (chi-square or any other test that best suits categorical data) so we can see where differences actually exist. Visual summaries—charts or dashboards built right inside Excel—are important because this work will be presented to non-statisticians.
Deliverables
• One Excel workbook containing:
– a “Data” sheet (cleaned, formulas unlocked)
– an “Analysis” sheet with tables and test outputs clearly labeled
– a “Visuals” sheet or dashboard summarizing key findings
• A brief read-me note inside the file explaining methods used and how to refresh the analysis if new data are added.
If you prefer to run the stats in R or Python that’s fine, as long as every result still lands neatly back in the Excel file. Accuracy and clarity matter more than fancy formatting, so keep formulas transparent and cells well-commented.
I’m ready to share the dataset the moment we agree on the approach and timeline.