## 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.

## Places to visit in and around Thrissur

If you want to enjoy all the glories of the cultural beauty of the state of Kerala, Thrissur is one of the important places to include in your travel list. Thrissur district is blessed...

## paired t test tabled value vs p-value

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...

## Null Hypothesis

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...

## t-test in R-How to Perform T-tests in R

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 in R, its assumptions, and...

## 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

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

Hypothesis: One sample analysis in R-In statistics, we can define the corresponding null hypothesis  (H0) as follow: 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...

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