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This post is continuation of the above post.
In this post, let's check if there is statistically Gender Gap difference by Year, by sector, by Indicator, by category and by obs.
I start with Year.
p-value is 0.824. Thus, Gender Gap is NOT statistically different by Year.
Next, let's check sector.
p-value is less than 23-16. Thus, Gender Gap IS statistically different by sector.
Next, let's check Indicator.
p-value is 2e-16 too. This, Gender Gap IS statistically different by Indicator.
Next, let's check catgory.
p-value is 2e-16 too. Thus, Gender Gap IS statistically different by category.
Lastly, let's check obs.
p-value is 002359. Thus, Gender Gap IS different by obs.
Now, I include sector, Indicator, categoru and obs in one linear regression.
p-value is less than 2.2e-16.
That's all.
Next post is
To read the first post of this series,
This post uses below R code.
#
# gap is statistically different by Year?
aov(gap ~ Year, data = df) |> summary()
#
# gap is statistically different by sector?
aov(gap ~ sector, data = df) |> summary()
#
# gap is statistically different by Indicator?
aov(gap ~ Indicator, data = df) |> summary()
#
# gap is statistically different by category?
aov(gap ~ category, data = df) |> summary()
#
# gap is statistically different by obs?
lm(gap ~ obs, data = df) |> summary()
#
# Linear Regression
lm(gap ~ sector + Indicator + category + obs, data = df) |>
summary()
#
