/plot=boxplot.
* Do a Scatterplot to check if weight and height
-* might be correlated. As both the weight and the
+* might be correlated. As both the weight and the
* height for males is higher than for females
* the combination of male and female data is correlated.
* Weigth increases with height.
graph
/scatterplot = height with weight.
-
+
* Within the male and female groups there is no correlation between
* weight and height. This becomes visible by marking male and female
* datapoints with different colour.
* The T-Test checks if male and female humans have
* different weight, height, beauty and iq. See that Significance for the
* weight and height variable tends to 0, while the Significance
-* for iq should not go to 0.
+* for iq should not go to 0.
* Significance in T-Test means the probablity for the assumption that the
* height (weight, beauty,iq) of the two groups (male,female) have the same
* mean value. As the data for the iq values is generated as normal distribution
* with the same mean value, the significance should not go down to 0.
t-test groups=gender(0,1)
- /variables=weight height beauty iq.
+ /variables=weight height beauty iq.
* Run the Code several times to see the effect that different data
-* is generated. Every run is like a new sample from the population.
+* is generated. Every run is like a new sample from the population.
* Change the number of samples (cases) by changing the
* loop range to see the effect on significance!