Calculate Confidence Interval

Anonymous — Wed, 25 Nov 2020 16:16:04 GMT

I'm attempting to calculate confidence interval the same way Excel does with CONFIDENCE.NORM, which is calculating a confidence interval for a population mean using normal distribution. In this case the confidence level is 90%. Is there a formula or function that would work in such a case? I looked over the TTest functions but none really caught my eye.

Re: Calculate Confidence Interval

Frank_Hartmann — Fri, 29 Sep 2017 07:37:52 GMT

search for

TTest_lower, TTest_upper and TTest_conf:

Qlikview HELP:

TTest_lower ( [set_expression] [ total [<fld {,fld}>] group, value [, sig = 0.025 [, eq_var = true]])

returns the aggregated value for the lower end of the confidence interval for two independent series of values iterated over the chart dimension(s). See the TTest_t ([set_expression][ total [<fld {,fld}>] group, value [, eq_var = true]) and TTest_conf ([set_expression][ total [<fld {,fld}>] group, value [, sig = 0.025 [, eq_var = true]]) functions for argument descriptions.

The same limitations with regard to nested aggregation as those listed under the chi2test_p ([set_expression][ total [<fld {,fld}>] col, row, observed_value [, expected_value]) function apply. This function supports the total qualifier in the same manner as described under the chi2test_p ([set_expression][ total [<fld {,fld}>] col, row, observed_value [, expected_value]) function.

Examples:

ttest_lower( Group, Value )

ttest_lower( Group, Value, false )

TTest_conf ([set_expression][ total [<fld {,fld}>] group, value [, sig = 0.025 [, eq_var = true]])

returns the aggregated t value for two independent series of values iterated over the chart dimension(s). The two-tailed level of significance can be specified in sig. If omitted sig will be set to 0.025, resulting in a 95% confidence interval. See the TTest_t ([set_expression][ total [<fld {,fld}>] group, value [, eq_var = true]) function for descriptions of the other arguments.

Examples:

ttest_conf( Group, Value )

ttest_conf( Group, Value, false )

TTest_upper ([set_expression][ total [<fld {,fld}>] group, value [, sig = 0.025 [, eq_var = true]])

returns the aggregated value for the upper end of the confidence interval for two independent series of values iterated over the chart dimension(s). See the TTest_t ([set_expression][ total [<fld {,fld}>] group, value [, eq_var = true]) and TTest_conf ([set_expression][ total [<fld {,fld}>] group, value [, sig = 0.025 [, eq_var = true]]) functions for argument descriptions.

Examples:

ttest_upper( Group, Value )

ttest_upper( Group, Value, false )

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TTest_conf (group, value [, sig = 0.025 [, eq_var = true]])

returns the aggregated t value for two independent series of values iterated over a number of records as defined by a group by clause. The two-tailed level of significance can be specified in sig. If omitted sig will be set to 0.025, resulting in a 95% confidence interval. See the TTest_t (group, value [, eq_var = true]) function for descriptions of the other arguments.

Example:

Load Year, ttest_conf(Group, Value) as X from abc.csv group by Year;

TTest_lower (group, value [, sig = 0.025 [, eq_var = true]])

returns the aggregated value for the lower end of the confidence interval for two independent series of values iterated over a number of records as defined by a group by clause. See the TTest_t (group, value [, eq_var = true]) and TTest_conf (group, value [, sig = 0.025 [, eq_var = true]]) functions for argument descriptions.

Example:

Load Year, ttest_lower(Group, Value) as X from abc.csv group by Year;

TTest_upper (group, value [, sig = 0.025 [, eq_var = true]])

returns the aggregated value for the upper end of the confidence interval for two independent series of values iterated over a number of records as defined by a group by clause. See the TTest_t (group, value [, eq_var = true]) and TTest_conf (group, value [, sig = 0.025 [, eq_var = true]]) functions for argument descriptions.

Example:

Load Year, ttest_upper(Group, Value) as X from abc.csv group by Year;

Re: Calculate Confidence Interval

Anonymous — Mon, 30 Oct 2017 19:33:54 GMT

Thanks for the reply! In the end I found an equation that worked.

In case anyone else comes across this, I was able to apply the Z score for normal distrubtion for 90% as follows..

=1.6448536 *(stddev(range of values)/sqrt(population size))

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Calculate Confidence Interval

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