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The VO2max factor analysis does a multivariate linear regression to identify which factors and tags do influence the effective VO2max estimate. In detail, this means that for each activity, the difference to the former shape is considered as a weighted combination of all possible influencing factors. For each factor - for example, activity characteristics such as length, temperature, training type, tags or equipment - it is determined whether this factor tends to increase or decrease the effective VO2max of an activity (and by how much).

What does the effect size mean?
The effect size shows by how much the given factor increases or decreases the effective VO2max of an activity. A result of +1.5 for example means that activities with this factor tend to have a higher effective VO2max of 1.5 (as absolute value).

What does the data column show?
The data column shows a histogram of the data for this factor. For binary factors (those that can only be true or false, e.g. activity types, tags and equipment) this is a grey bar for 'false' and a (normally very small) green bar for 'true'. For non-binary factors (e.g. temperature, duration, ...) this is a common histogram from total minimum to maximum. The exact numbers (as well as min/max) are shown in a tooltip.

What does the significance mean?
The significance is based on the p-value (shown in the tooltip), which measures the probability of obtaining the observed results, assuming that the effect size is indeed not zero. A p-value of 0.05 or lower is generally considered statistically significant.

• ***: p <= 0.001
• **: p <= 0.01
• *: p <= 0.05 (significant)
• -: p > 0.05 (not significant)

More details and an application example
You can find more details and an application example in our blog article.