Category: Methods

Scalar Product 0

How to Calculate the Scalar Product in R

Scalar product, sometimes known as the dot product, is an algebraic operation that returns a single integer from two numbers of equal length. Let’s say we have two vectors x and y and we...

r transform 0

R transform function with Example

R transform() function is used to manipulate data. The first variable is converted to a data frame. This function is used to quickly and easily transform or modify the data frame. R transform Syntax:...

How to Calculate Cramer’s V in R 0

How to Calculate Cramer’s V in R

How to Calculate Cramer’s V in R, Cramer’s V is a statistic that ranges from 0 to 1 and is used to assess the strength of the relationship between two nominal variables. Closer values...

Standardization in Statistics 0

Standardization in Statistics with R

Standardization in statistics, when a dataset is standardized, all of the variables are scaled so that the mean is 0 and the standard deviation is 1. Standardization in Statistics In a data frame, there...

Time Series Trend Analysis 0

Time Series Trend Analysis in R

Time series trend analysis, The Mann-Kendall Pattern Test is used to detect whether or not time series data has a trend. It’s a non-parametric test, which means there’s no underlying assumption about the data’s...

How to calculate Scheffes Test in R 0

How to calculate Scheffe’s Test in R

How to calculate Scheffes Test in R, A one-way ANOVA is used to check if there is a statistically significant difference between the means of three or more independent groups. If the aggregate p-value...

Lack of fit test in R 0

How to Perform a Lack of Fit Test in R-Quick Guide

A lack of fit test is used to determine whether a full regression model fits a dataset significantly better than a reduced version of the model.

Consider the following regression model, which has four predictor variables.

Y = β0 + β1×1 + β2×2 + β3×3 + β4×4 + ε
A nested model is demonstrated by the following model, which contains only two of the original predictor variables.

Y = β0 + β1×1 + β2×2 + ε
We can use a Lack of Fit Test with the following null and alternative hypotheses to see if these two models differ significantly.
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