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What are the Nonparametric tests 0

What are the Nonparametric tests?

What are the Nonparametric tests?. Why we need nonparametric statistical analysis? Parametric statistical methods are based on particular assumptions about the population in which the samples have been drawn. Particularly probability distribution, observation accuracy,...

Contingency Table in R 0

Contingency Table in R

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...

discriminant analysis in r 6

Discriminant Analysis in R

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.

Null Hypothesis 0

Why we need a null hypothesis test?

Why we need a null hypothesis test?. Hypothesis testing is an important stage in statistics. The test evaluates two mutually exclusive statements about a population to determine which statement is better supported by the...

places to visit in ernakulam 1

Places to visit in Ernakulam District

Places to visit near Ernakulam district, Are you planning to visit Kerala?

Each of the many places you visit is of historical and colonial significance.

If you are planning to visit places in Kerala, good planning is essential and this information will help you a lot.

Let’s take a look at the best places to visit in the Ernakulam district.

Proportion test in R 1

Proportion test in R

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 2

One Sample Analysis in R

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...