# Creating a typical t-test report

A typical student t-test report can include tables with Group Statistics and Independent Samples Test results. In the following sections we will build these tables using QlikViewt-test functions applied to two independent groups of samples, Observation and Comparison. The corresponding tables for these samples would look like this:

Group Statistics

Type N Mean Standard Deviation Standard Error Mean
Comparison 20 11.95 14.61245 3.2674431
Observation 20 27.15 12.507997 2.7968933

Independent Sample Test

t df Sig. (2-tailed) Mean Difference Standard Error Difference 95% Confidence Interval of the Difference (Lower) 95% Confidence Interval of the Difference (Upper)
Equal Variance not Assumed 3.534 37.116717335823 0.001 15.2 4.30101 6.48625 23.9137
Equal Variance Assumed 3.534 38 0.001 15.2 4.30101 6.49306 23.9069

Do the following:

1. Create a new document.
2. Select Edit Script in the toolbar and enter the following to the script:

Table1:

crosstable LOAD recno() as ID, * inline [

Observation|Comparison

35|2

40|27

12|38

15|31

21|1

14|19

46|1

10|34

28|3

48|1

16|2

30|3

32|2

48|1

31|2

22|1

12|3

39|29

19|37

25|2 ] (delimiter is '|');

In this load script, recno() is included because crosstable requires three arguments. So, recno() simply provides an extra argument, in this case an ID for each row. Without it, Comparison sample values would not be loaded.

## Creating the Group Statistics table

Do the following:

1. Add a straight table on the sheet and select Type as a dimension.

3. Label Expression
N Count(Value)
Mean Avg(Value)
Standard Deviation Stdev(Value)
Standard Error Mean Sterr(Value)
4. Make sure that Type is at the top of the sorting list.
5. Result:

A Group Statistics table for these samples would look like this:

TypeNMeanStandard DeviationStandard Error Mean
Comparison2011.9514.612453.2674431
Observation2027.1512.5079972.7968933

## Creating the Two Independent Sample Student's T-test table

Do the following:

1. Add a table on the sheet.
2. Add the following calculated dimension as a dimension to the table. =ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1))

Label Expression
conf if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1)),TTest_conf(Type, Value),TTest_conf(Type, Value, 0))
t if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1)),TTest_t(Type, Value),TTest_t(Type, Value, 0))
df if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1)),TTest_df(Type, Value),TTest_df(Type, Value, 0))
Sig. (2-tailed) if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1)),TTest_sig(Type, Value),TTest_sig(Type, Value, 0))
Mean Difference TTest_dif(Type, Value)
Standard Error Difference if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1)),TTest_sterr(Type, Value),TTest_sterr(Type, Value, 0))
95% Confidence Interval of the Difference (Lower) if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1)),TTest_lower(Type, Value,(1-(95)/100)/2),TTest_lower(Type, Value,(1-(95)/100)/2, 0))
95% Confidence Interval of the Difference (Upper) if(ValueList (Dual('Equal Variance not Assumed', 0), Dual('Equal Variance Assumed', 1)),TTest_upper(Type, Value,(1-(95)/100)/2),TTest_upper(Type, Value,(1-(95)/100)/2, 0))

Result:

An Independent Sample Test table for these samples would look like this:

t df Sig. (2-tailed) Mean Difference Standard Error Difference 95% Confidence Interval of the Difference (Lower) 95% Confidence Interval of the Difference (Upper)
Equal Variance not Assumed 3.534 37.116717335823 0.001 15.2 4.30101 6.48625 23.9137
Equal Variance Assumed 3.534 38 0.001 15.2 4.30101 6.49306 23.9069