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# Woordenboek

T distribution T distribution
A family of probability distributions that can be used to develop confidence intervals which estimate the population mean whenever the population standard deviation is unknown and the population has a normal or near-normal probability distribution.
T-test
To determine if there is a difference between two groups; the Y is continuous and the X is discrete or categorical; The three types of t-tests most commony used are; 1. To test if sample average = specified value, 2. To test if 2 sample means are equal, 3. A Paired t: to reduce variation due to stratification when comparing two sample averages in a systematic paired way.
T-test for testing the hypothesis of a s
This statistical test answers the following question: Given a hypothesized value for the population mean, does the observed data support the hypothesis or does the data imply the population is something other than the hypothesized mean. If the absolute value of the t-statistic is large, that implies the observed sample mean fell far away, in terms of standard deviations, from the hypothesized mean.
T-test for testing the hypothesis that t
This statistical procedure addresses the following situation: Given data from two populations, can we reasonable conclude that the population means for the two populations are the same or does the data support the hypothesis that the two population means are different.
T-value
The observed t-value is calculated by first subtracting the observed sample mean from the hypothesized mean and then dividing the result by the standard error. The standard error in this instance is the sample standard deviation divided by the square root of the sample size. The t-value tells us a round-a-bout measure of how many standard deviations the observed sample mean fell from the hypothesized mean. The larger this value is the more unusual the sample mean was.
Taguchi method
This method is similar to Design of Experiments (DOE). It is a technique for focusing attention on how particular combinations of variables can be used to identify the individual impact of variables. The Taguchi method is often applied to designing and executing experiments in manufacturing to create an ideal design factoring in all required functions.
Team Charter
A documented agreement on team roles and ways of working.
Test of Significance
A procedure to determine whether a quantity subjected to random variation differs from a postulated value by an amount greater than that due to random variation alone.
Tijdreeksanalyse
In de literatuur wordt vaak gesproken over time series analysis. In bijvoorbeeld de industrie als biologie veel gebruikt om trends in de tijd of seizoenseffecten te identificeren. Box & Jenkins hebben deze statistische analyse gepopulariseerd, zie eventueel Box & Jenkins, 1970 of 1976.
Time Trap
Een time trap is een onderdeel of stap in een proces dat vertraging aan een proces toevoegt. Deze kunnen ontstaan door het invoeren van te grote batches (meer dan minimaal benodigde), kwaliteitsproblemen of wachttijden en onderbrekingen in een proces.
To Be Process Mapping
The mapping of new or future processes. "To Be" refers to what the processes are meant to look like in the future contrary to "As Is" depicting the current flow.
Tollgate review checklist
All project process management tools (e.g. DMAIC, DfLSS) use a phase and tollgate approach. Each type of project is broken down into a number of phases and the project can only progress into the next phase if it passes the performance required by the phase checklist. Each question in the phase checklist is designed to stimulate the project manager and project sponsor to ask and answer the right level of questions. The questions are designed to be stretching and challenging in a positive way. The checklist for a phase should be reviewed by the team as it starts each phase because it defines what will need to be done to complete the phase. The aim of the checklists is to ensure that all key areas of a project, that can impact its success, are being addressed in parallel. No project should proceed to a new phase until the project manager has satisfied the project sponsor that the phase has been completed. This approach ensures that projects stay on track and planned financial benefits are delivered.
Type I Error (α Risk)
The probability of accepting the alternate hypotheses when, in reality, the null hypothesis is true.
Type II Error (β Risk)
The probability of accepting the null hypothesis when, in reality, the alternate hypothesis is true.