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Moved factors from comments to statement (#100)
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welcome.md

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@@ -25,6 +25,14 @@ This example demonstrates how to perform Bayesian inference for a linear regress
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Plant growth can be influenced by multiple factors, and understanding these relationships is crucial for optimizing agricultural practices.
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Independent Variables:
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- Sunlight Hours: Number of hours the plant is exposed to sunlight daily.
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- Water Amount: Daily water amount given to the plant (in milliliters).
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- Soil Nitrogen Content: Percentage of nitrogen content in the soil.
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Dependent Variable:
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- Plant Growth (y): Measured as the increase in plant height (in centimeters) over a certain period.
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```python
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import pymc as pm
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x_dist = pm.Normal.dist(shape=(100, 3))
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x_data = pm.draw(x_dist, random_seed=seed)
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# Independent Variables:
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# Sunlight Hours: Number of hours the plant is exposed to sunlight daily.
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# Water Amount: Daily water amount given to the plant (in milliliters).
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# Soil Nitrogen Content: Percentage of nitrogen content in the soil.
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# Dependent Variable:
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# Plant Growth (y): Measured as the increase in plant height (in centimeters) over a certain period.
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# Define coordinate values for all dimensions of the data
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coords={
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"trial": range(100),

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