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Normality Test in R:-In statistics methods is classified into two like Parametric methods and Nonparametric methods. The majority of the test like correlation, regression, t-test, and analysis of variance (ANOVA) assume some certain characteristics about...
Correlation analysis is one of the most commonly used statistical techniques for measuring the strength and direction of the relationship between two continuous variables. It is widely applied in data science, finance, healthcare, marketing,...
A contingency table is one of the most widely used tools in statistics for analyzing the relationship between categorical variables. Whether you’re working in healthcare, finance, marketing, manufacturing, or social sciences, contingency tables help...
The Student’s t-test is one of the most widely used statistical methods for comparing means. It helps determine whether the observed difference between sample means is statistically significant or simply due to random sampling...
One sample analysis in R-In statistics, we can define the corresponding null hypothesis (H0) as follow: Hypothesis: 1. H0:m=μ, 2. H0:m≤μ 3. H0:m≥μ The corresponding alternative hypotheses (Ha) are as follow: 1. Ha:m≠μ (different) 2. Ha:m>μ (greater) 3. Ha:m<μ (less) Outlier Detection: Out.fun<-function{abs(x-mean(x,na.rm=TRUE))>3*sd(x,na.rm=TRUE)} ddply(data,.(sample, variable),transform,outlier.team=out.fun(value)) column heading...