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

Independent Sample Test

Loading the sample data

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.

3. Save the script and click Reload to load the data.

Creating the Group Statistics table

Do the following:

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

2. Add the following expressions:

   Expressions to add

   Label	Expression
   N	Count(Value)
   Mean	Avg(Value)
   Standard Deviation	Stdev(Value)
   Standard Error Mean	Sterr(Value)

3. Make sure that Type is at the top of the sorting list.

Result:

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

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))

3. Add the following expressions:

   Expressions to add

   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: