Command:
getTTestBound ( |
|
What:
Retrieves the T test confidence interval for an output variable (it makes sense only for normal distributed variables) with a 95% confidence
Parameters:
value [double precision #] |
The confidence interval about the mean of the variable. It is not necessary in Matlab |
outVar [char*] |
The name of the output variable of interest |
Return:
error_code [int]: 1 if the operation has occurred without errors, 0 if an error has occurred
Examples:interval = getTTestBound('outVar')
this command retrieves in the Matlab variable interval the T test confidence bound about the mean of the output variable outvar with a 95% of confidence.
About the T test bound:
If a Y variable
is normal distributed it would be possible to use standard parametric
techniques, such as T test, to compute confidence bounds on μ, the
population mean of Y, depending on the sample size.
The hypothesis of the test are:
H0: μ = μ0 against H1: μ ≠ μ0
A confidence interval for a mean specifies a range of values within which the unknown population parameter, in this case the mean, may lie.
The width of the confidence interval gives us some idea about the precision of the estimate. The wider is the interval and the less is the precision of the estimate. We interpret the interval calculated at a 95% level as, we are 95% confident that the interval contains the true population mean. We could also say that 95% of all confidence intervals formed in this manner (from different samples of the population) will include the true population mean.
The hypothesis of the test are:
H0: μ = μ0 against H1: μ ≠ μ0
A confidence interval for a mean specifies a range of values within which the unknown population parameter, in this case the mean, may lie.
The width of the confidence interval gives us some idea about the precision of the estimate. The wider is the interval and the less is the precision of the estimate. We interpret the interval calculated at a 95% level as, we are 95% confident that the interval contains the true population mean. We could also say that 95% of all confidence intervals formed in this manner (from different samples of the population) will include the true population mean.