Examples: Added t-Test example code

I added a simple t-Test example code with some plots as a
kind of beginners tutorial.

diff --git a/examples/t-test.sps b/examples/t-test.sps
new file mode 100644 (file)
index 0000000..aa84016
--- /dev/null
+++ b/examples/t-test.sps
@@ -0,0 +1,78 @@
+* T-TEST example pspp code
+
+* Generate an example dataset for male and female humans
+* with weight, height, beauty and iq data
+* Weight and Height data are generated as normal distributions with
+* different mean values. iq is generated with the same mean value (100).
+* Beauty is only slightly different.
+* Every run of the program will produce new data
+input program.
+
+* Females have gender 0
+* Create 10 female cases
+loop #i = 1 to 8.
+ compute weight  = rv.normal (65, 10).
+ compute height = rv.normal(170.7,6.3).
+ compute beauty = rv.normal (10,4).
+ compute iq = rv.normal(100,15).
+ compute gender = 0.
+ end case.
+end loop.
+
+* Males have gender 1
+loop #i = 1 to 8.
+ compute weight  = rv.normal (83, 13).
+ compute height = rv.normal(183.8,7.1).
+ compute beauty = rv.normal(11,4).
+ compute iq = rv.normal(100,15).
+ compute gender = 1.
+ end case.
+end loop.
+
+end file.
+end input program.
+
+* Add a label to the gender values to have descriptive names
+value labels
+  /gender 0 female 1 male.
+
+* Plot the data as boxplot and histogram
+examine
+  /variables=weight height beauty iq by gender
+  /plot=boxplot.
+
+* Do a Scatterplot to check if weight and height
+* 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.
+graph
+  /scatterplot = height with weight by gender.
+
+* 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. 
+* 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.  
+
+* Run the Code several times to see the effect that different data
+* 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!
+* With increasing number of cases the sample size increases and
+* the estimation of mean values and standard deviation becomes better.
+* The difference in beauty becomes visible only with larger sample sizes.
+
+