Skip to content

Data Science Tutorials

  • Home
  • R
  • Statistics
  • Course
  • Machine Learning
  • Guest Blog
  • Contact
  • About Us
  • Toggle search form
  • How to Standardize Data in R
    How to Standardize Data in R? R
  • How to Implement the Sklearn Predict Approach
    How to Implement the Sklearn Predict Approach? R
  • Random Forest Machine Learning
    Random Forest Machine Learning Introduction R
  • How to Group and Summarize Data in R
    How to Group and Summarize Data in R R
  • Top Data Science Skills
    Top Data Science Skills- step by step guide Machine Learning
  • How to create a ggalluvial plot in r
    How to create a ggalluvial plot in R? R
  • How to Use the Multinomial Distribution in R
    How to Use the Multinomial Distribution in R? R
  • ggpairs in R
    ggpairs in R R
Correlation Coefficient p value in R

Correlation Coefficient p value in R

Posted on February 24February 24 By Jim No Comments on Correlation Coefficient p value in R
Tweet
Share
Share
Pin

Correlation Coefficient p value in R, The linear link between two variables can be evaluated using the Pearson correlation coefficient.

This correlation coefficient always has a value between -1 and 1, where:

-1: A perfect inverse linear correlation exists between two variables.

0: There is no linear link between the two variables.

1: A perfect linear correlation between two variables.

You can compute the corresponding t-score and p-value to see if a correlation coefficient is statistically significant.

The following formula can be used to determine a correlation coefficient’s (r) t-score:

t = r√n-2 / √1-r2

The corresponding two-sided p-value for the t-distribution with n-2 degrees of freedom is used to determine the p-value.

You can use the cor.test() function in R to determine the p-value for a Pearson correlation coefficient.

One sample proportion test in R-Complete Guide (datasciencetut.com)

cor.test(x, y)

How to actually use this function is demonstrated in the example that follows.

Compute the p-Value for the correlation coefficient

The p-value for the correlation coefficient between two variables in R may be calculated using the cor.test() function by using the following code:

Set up two variables.

x <- c(710, 718, 920, 187, 884, 486, 491, 174, 283, 585)
y <- c(590, 494, 679, 886, 484, 853, 808, 992, 756, 975)

determine the correlation coefficient and associated p-value

cor.test(x, y)
               Pearson's product-moment correlation
data:  x and y
t = -3.0109, df = 8, p-value = 0.01679
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.9311631 -0.1833776
sample estimates:
       cor
-0.7288512

From the output we can see:

The Pearson correlation coefficient is -0.7288512.

The corresponding p-value is 0.01679.

Two Sample Proportions test in R-Complete Guide – Data Science Tutorials

Given that the correlation coefficient is negative, the two variables must have a negative linear connection.

The association is statistically significant, nevertheless, because the p-value of the correlation coefficient is less than 0.05.

Be aware that we may also use the syntax cor.test(x, y)$p.value to merely retrieve the correlation coefficient’s p-value:

cor.test(x, y)$p.value
0.01678965

The correlation coefficient’s p-value is 0.01678965.

Top 7 Skills Required to Become a Data Scientist (datasciencetut.com)

This corresponds to the p-value in the earlier output.

Check your inbox or spam folder to confirm your subscription.

Tweet
Share
Share
Pin
R

Post navigation

Previous Post: Predictive Modeling and Data Science
Next Post: Is R or Python Better for Data Science in Bangalore

Related Posts

  • Select the First Row by Group in R
    Select the First Row by Group in R R
  • Credit Card Fraud detection in R
    Credit Card Fraud Detection in R R
  • How do confidence intervals work
    How do confidence intervals work? R
  • Add new calculated variables to a data frame and drop all existing variables
    Add new calculated variables to a data frame and drop all existing variables R
  • How to Turn Off Scientific Notation in R
    How to Turn Off Scientific Notation in R? R
  • Load Multiple Packages in R
    Load Multiple Packages in R R

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

  • About Us
  • Contact
  • Disclaimer
  • Guest Blog
  • Privacy Policy
  • YouTube
  • Twitter
  • Facebook
  • Defensive Programming Strategies in R
  • Plot categorical data in R
  • Top Data Modeling Tools for 2023
  • Ogive Graph in R
  • Is R or Python Better for Data Science in Bangalore

Check your inbox or spam folder to confirm your subscription.

  • Data Scientist Career Path Map in Finance
  • Is Python the ideal language for machine learning
  • Convert character string to name class object
  • How to play sound at end of R Script
  • Pattern Searching in R
  • Is Data Science a Dying Profession
    Is Data Science a Dying Profession? R
  • How to Use Spread Function in R
    How to Use Spread Function in R?-tidyr Part1 R
  • Best Books to Learn R Programming
    Best Books to Learn R Programming Course
  • Autocorrelation and Partial Autocorrelation in Time Series
    Autocorrelation and Partial Autocorrelation in Time Series Statistics
  • How to Calculate Lag by Group in R
    How to Calculate Lag by Group in R? R
  • how to create a hexbins chart in R
    How to create a hexbin chart in R R
  • How to Create Summary Tables in R
    How to Create Summary Tables in R R
  • Check whether any values of a logical vector are TRUE
    Check whether any values of a logical vector are TRUE R

Copyright © 2023 Data Science Tutorials.

Powered by PressBook News WordPress theme