Statistical Analysis
Pearson Correlation
Pearson Correlation Description The Pearson correlation coefficient is also known as Pearson's or simply the correlation coefficient. It is a statistical measure used to determine the degree and magnitude of the association or correlation, as well as ...
Covariance
Covariance Description Covariance is a statistical measure of the variability of two random variables with each other. Covariance between two variables hints towards a linear relationship between them. Why to use To determine the relationship between ...
Two-Way ANOVA
Two-Way ANOVA Description It analyzes the effect of independent variables on the expected outcome along with their relationship to the outcome itself. Why to use To perform analysis of variance. When to use To know how two independent variables, in ...
One-Way ANOVA
One-Way ANOVA Description It compares the mean values of three or more independent groups in order to determine the statistical evidence that the associated population means are significantly different. Why to use To perform analysis of variance. ...
Shapiro-Wilk Test
Shapiro-Wilk Test Description The Shapiro-Wilk test is a normality test in probability determination statistics. It is used to determine whether a simple random sample of a variable’s values has been derived from a normal distribution. Why to use For ...
Process Capability Analysis
Process Capability Analysis Description Process Capability Analysis is a computational method for comparing the output of a manufacturing process to its engineered specification limits. Why to use Statistical Analysis When to use To compare the ...
Parametric Distribution Fitting
Parametric Distribution Fitting Description Parametric distribution fitting is the process used to select a statistical distribution that best fits a data set. Why to use Statistical Analysis When to use To decide the distribution best suited for ...
Inverse Cumulative Distribution Function
Inverse Cumulative Distribution Function Description Inverse Cumulative Distribution Function (ICDF) is the inverse of Cumulative Distribution Function (CDF). It helps to learn about the distribution of data by calculating the value associated with a ...
Cumulative Distribution Function
Cumulative Distribution Function Description The Cumulative Distribution Function (CDF) helps to learn about the distribution of data by calculating the cumulative probability of a given variable value in a dataset. Why to use To calculate the ...
Johnson Transformation
Johnson Transformation Description The Johnson transformation is a statistical technique that transforms non-normal data into a normal distribution. It extends the Box-Cox transformation and can handle positively and negatively skewed data. Why to ...
Box-Cox Transformation
Box-Cox Transformation Description The Box-Cox transformation is a mathematical technique that transforms a non-normal or skewed dataset into a more normal distribution. Why to use Normality Homoscedasticity Linearity When to use Non-Normal data When ...
Statistical Analysis
Statistical analysis is the science of analyzing patterns and trends in collected data and deriving inferences from them. ANOVA Analysis Analysis of Variance (ANOVA) is a statistical technique that measures the difference between the means of two or ...