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lines changed Original file line number Diff line number Diff line change @@ -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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+
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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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@@ -33,16 +41,6 @@ seed = 42
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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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-
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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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-
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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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