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This post is following of the above post.
In the previous post, I made scaled variables in df4, let's see correlation matrix of those variables.
The most highly correlated variable pair is s_po_to and s_ni_to. The second is s_ni_kg and s_po_kg.
s_ni_to and s_po_kg are not highly correlated, it is 0.103.
So, I will do regression analysis, one is s_ni_kg = beta_0 + beta_1 * s_po_kg + u, the other is s_ni_kg = beta_0 + beta_1 * s_po_kg + beta_2 * s_ni_to + u.
The fisrt one is simple regression and the second one is multiple regression.
Let's see scatter plot matrix for those variables.
Then, let's use lm() function for regression anaysis.
7.494e-01 means 0.794.
So, simple regression model is
s_ni_kg = 0 + 0.749 * s_po_kg + u
Next, let's add s_ni_to for explanatory variables.
So, multiple regression model formula is
s_ni_kg = 0 + 0.756 * s_po_kg - 0.065 * s_ni_to + u
let's make scatter plot and regression line.
For multiple regression, I use average value of s_ni_to.
The both scatter plot are very similar shape.
That's it. Thank you!
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