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Copy file name to clipboardExpand all lines: doc/index.qmd
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for a nominal coverage level is $1-\alpha$.
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The corresponding coverage results are highlighted according to the following color scheme:
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* <spanstyle="background-color: #00FF00; color: black; padding: 2px5px; border-radius: 3px;">Green</span> if the deviation to the nominal level is below $5\%$
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* <spanstyle="background-color: #FFFF00; color: black; padding: 2px5px; border-radius: 3px;">Yellow</span> if the deviation to the nominal level is above $5\%$ and below $10\%$
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* <spanstyle="background-color: #FF0000; color: black; padding: 2px5px; border-radius: 3px;">Red</span> if the deviation to the nominal level is above $10\%$
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```{python}
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#| echo: false
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#| output: asis
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from utils.styling import get_html_color_span
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# Generate color legend using centralized configuration
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good_span = get_html_color_span("good")
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medium_span = get_html_color_span("medium")
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poor_span = get_html_color_span("poor")
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from IPython.display import Markdown, display
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markdown_output = f"""
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* {good_span} if the deviation to the nominal level is below 5%
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* {medium_span} if the deviation to the nominal level is above 5% and below 10%
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* {poor_span} if the deviation to the nominal level is above 10%
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"""
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display(Markdown(markdown_output))
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```
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For simulations with multiple parameters of interest, usually pointwise and uniform coverage is assessed.
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