#Confidence interval minitab how to

Assumption #2: The data are independent (i.e., not correlated/related), which means that there is no relationship between the observations.

If you are unsure whether your dependent variable is continuous (i.e., measured at the interval or ratio level), see our Types of Variable guide. Examples of continuous variables include height (measured in feet and inches), temperature (measured in вЊ), salary (measured in US dollars), revision time (measured in hours), intelligence (measured using IQ score), firm size (measured in terms of the number of employees), age (measured in years), reaction time (measured in milliseconds), grip strength (measured in kg), power output (measured in watts), test performance (measured from 0 to 100), sales (measured in number of transactions per month), academic achievement (measured in terms of GMAT score), and so forth.

Assumption #1: Your dependent variable should be measured at a continuous level (i.e., it is an interval or ratio variable).

Assumptions #1 and #2 are explained below: If these assumptions are not met, there is likely to be a different statistical test that you can use instead. However, you should check whether your study meets these two assumptions before moving on. You cannot test the first two of these assumptions with Minitab because they relate to your study design and choice of variables.

Minitab AssumptionsДЄ one-sample t-test has four assumptions. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a one-sample t-test to give you a valid result.

#Confidence interval minitab how to

In this guide, we show you how to carry out a one-sample t-test using Minitab, as well as interpret and report the results from this test. The lecturer could use a one-sample t-test to compare the weekly study time of a sample of 20 students to the suggested 10 hours. Alternately, imagine that a lecturer believed her course required 10 hours of study time per week and wanted to determine whether students spent this amount of time studying. The academic could use a one-sample t-test to compare the GMAT score of the 100 participants against the national average.

This population mean is not always known, but is sometimes hypothesized.ДЇor example, imagine that an academic was conducting research on the relationship between exam performance and revision time, but wanted to first check whether his 100 participants reflected the national average in terms of their academic ability, measured in terms of their GMAT score. The one-sample t-test is used to determine whether a sample comes from a population with a specific mean. One-sample t-test using Minitab Introduction