Correlation Analysis in R? In correlation analysis, we estimate a sample correlation coefficient based on experimental data, in most cases the Pearson Product Moment correlation coefficient is used to find the relationships. The sample...
Why Random Forest classification Model in R? Random forest regression model getting model accuracy is very difficult in some cases, the alternative is to increase the number of data points but in some cases...
The random forest model in R is a highly useful tool in analyzing predicted outcomes for a classification or regression model. The main idea is how explanatory variables will impact the dependent variable. In...
Contingency Table in R, In the test hypothesis, it is usually assumed sample drawn from a known distribution like binomial, Poisson, normal, etc…It is an assumption but good to check our assumption holds true...
Discrimination tests are more important in sensory analysis. The main idea behind sensory discrimination analysis is to identify any significant difference or not.
Here are the details of different types of discrimination methods and p-value calculations based on different protocols/methods.
This article will discuss different types of methods and discriminant analysis in r.
When do you use paired t-test and how to apply the same in a practical situation? In this article, we will talk about paired t-test analysis calculation based on a mathematical formula and using...
t-test in R-How to Perform, Student’s t-test is the deviation of the estimated mean from its population mean expressed in terms of standard error. In this article talking about how to perform a t-test...
How to do a proportion test in R and what are the conditions that need to meet for the proportion test? The sampling method for each population is simple random sampling. The samples are...
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...