UnsplashのMatteo Vellaが撮影した写真
This post is following of the above post.
In the above post, I did simple linear regression analysis. This time, I will for multiple linear regression (MLR).
I make a new variable, factor of TIME.
I made "year" which is factor type variable representing "TIME". I see 2010 has 28 observations and 2014 has 29 observations.
Let's do MLR (Multiple Linear Regression).
First, I make an interaction model.
Interaction model has two intercepts and two slopes.
If year == 2014,
SELFEMPLOYED = 39.2674 - 7.9415 + (-0.6425 + 0.3842) * EMPLOYEE + u
= 31.3529 - 0.2583 * EMPLOYEE + u
If year == 2010,
SELFEMPLOYED = 39.2674 - 0.6425 * EMPLOYEE + u
Let's make a visualization.
Next, I make a parallel slope model.
If year == 2014, parallel slope model is
SELFEMPLOYEE = 36.7298 - 3.0376 - 0.4478 * EMPLOYEE
= 33.6922 - 0.4478 * EMPLOYEE
If year == 2010,
SELFEMPLOYEE = 36.7298 - 0.4478 * EMPLOYEE.
Let's make a visualization graph.
solid lines are parallel slope model line, dashed lines are interation model line.
I don't see large difference between parallel slope model and interaction model.
Let's see ragresstion result table with get_regression_table() function of moderndive package.
Interaction term, EMPLOYEE:year2014 p_value is 0.623 so, it is not significant.
But I found other variables, EMPLOYEE and year: 2014 are also not statistically significant.
So, the both model are not statistically significant.
Let's see regression summary with get_regression_summaries() function.
p_value of interaction model is 0.594 and one of parallel slope model is 0.434.
So, the both MLR models are not statisticaly significant.
That's it! Thank you!
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