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Using Paired T-Tests for Controlled Analysis. Updated: August 14, 2023 by Ken Feldman. A paired t-test is a form of hypothesis testing where the subject is held constant and an analysis is done for two different conditions. This would be useful in comparing whether two machines are different by having the same operator do the test.
A two-sample t-test compares significant or random differences between two samples. It is most useful for hypothesis testing, letting users see if an assumption is correct. The two-sample t-test is one of the most powerful tools you have for. The two-sample t-test is one of the most commonly used hypothesis tests in Six Sigma work.
An industry example of a 1-sample sign test. The Six Sigma Black Belt wanted to determine whether the median sales of the Miami office was equal to the target of $25,000. Here is the data and results of her analysis. With a p-value of 0.344, you would not reject the null and therefore conclude the median sales of the sample comes from a ...
Since the T test is a hypothesis test, there must be a null (Ho) and alternative (Ha) hypothesis. The Ho is:: Ho: mu1 = mu2 (the two population means are equal) The Ha can be either two-tailed, left-one tailed, or right-one tailed: Here are the alternative hypotheses: Ha: (two-tailed): mu1 does not equal mu2 (the two population means are not equal)
Paired Data . To do a regression analysis requires that there be a paired set of data. That is, for each x value, there needs to be a corresponding y value. Use Statistical Software. Doing the calculations of the standard deviation, least squares regression, standardized residuals, and all of the residual plots by hand is not recommended.
Hypothesis testing – Autocorrelation can affect the results of hypothesis tests, such as t-tests and F-tests. By identifying and correcting for autocorrelation, we can obtain more accurate and reliable test results. An Industry Example of Autocorrelation. Suppose you had daily sales data for the last 60 days.
The Red X method is based on the key assumption there is always a dominant cause of variation. This statement is based on the application of the Pareto principle to the causes of the variation. Generally, the variation of the output is caused by the variation of several inputs. These inputs (Xs) are categorized by color, with the Red X being ...
With the Kruskal-Wallis test, multiple group samples can be tested together. 3. Box-plot Comparison. The test has the advantage that the overall levels of samples can be compared with box-plots visually. Choosing the Right Tool for the Job. Kruskal-Wallis is a useful test simply because of its flexibility and ease of implementation.
Categorical and continuous data are not mutually exclusive despite their opposing definitions. The most important difference between the terms is that “continuous data” describes the type of information collected or entered into the study. In contrast, “categorical data” describes a way of sorting and presenting the information in the ...
When comparing the average of two or more groups with the help of hypothesis tests, the assumption is that the data is a sample from a normally distributed population. That is why hypothesis tests such as the t-test, paired t-test, and analysis of variance (ANOVA) are also called parametric tests. Nonparametric tests do […] Read more »