What You Can Explore

Visualizing body weight from 30 to 100 kg against several calorie-expenditure estimates makes it easy to see which values rise with weight and where the illustrative male and female series differ.

• X axis: Body weight [kg]
• Y axis: 24-hour basal metabolism, one hour of walking or running, or basal metabolism plus eight hours of desk or standing work
• LEVEL: sex (male or female)

This CSV is a simplified model created for chart practice. It is not a personal calorie calculator or a medical estimate. Age, height, body composition, pace, and other factors would be required for a realistic calculation.

Copy the Wide-Format Sample CSV

The dataset contains one weight column, five metric columns, and a sex column. The column labels and category values remain in Japanese so that both language versions use exactly the same source data.

Recommended Sakura Chart Settings

• Chart type: Line chart or bar chart
• X axis: 体重[kg]
• Y axis: begin with 基礎代謝24時間[kcal], then compare the other metrics
• LEVEL: 男女

A useful sequence is to start with a line chart for the trend, switch to bars to emphasize differences at each weight, and then use a box plot to summarize the distributions.

Example Charts

Line Charts: Weight vs. Calorie Expenditure

Bar Charts: Side-by-Side Comparisons at Each Weight

Box Plots: Comparing the Overall Distributions

Observation 1: Exercise Values Show Little Difference Between the Sexes

In this model, the male and female lines for one hour of walking or running nearly overlap. That is intentional: the calculation assumes that weight, intensity, and duration dominate exercise expenditure when those inputs are the same.

• Exercise expenditure is modeled mainly from body weight × intensity × time.
• Sex-related differences are not modeled as strongly as they are for basal metabolism.

The overlap is therefore a property of the assumptions, not proof that real individuals expend identical amounts.

Observation 2: Basal Metabolism Shows a Larger Gap

The 24-hour basal-metabolism series are separated because the model uses different coefficients. In real estimation formulas, age, height, lean mass, and body composition can all affect resting energy expenditure.

• The male series uses a somewhat higher coefficient in this sample.
• The female series uses a somewhat lower coefficient in this sample.

Treat the gap as a visualization example, not as an individual prediction.

Observation 3: Eight Hours of Daily Activity Can Outweigh One Hour of Exercise

The desk-work and standing-work totals can add more energy than a single hour of exercise because the activity continues for much longer. The chart highlights how intensity and duration work together.

• Low-intensity activity can accumulate over many hours.
• Standing work creates a clearer increase when its intensity-times-duration value is larger.

How to Make the Model More Realistic

The current model favors clarity. To create a richer practice dataset, you could add:

• an age column to model changes in basal metabolism,
• a height column for a standard BMR formula,
• an exercise-intensity or speed column, or
• small individual variation so equal weights do not always produce identical values.