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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

How to Calculate the Coefficient of Variation in Excel & Google Sheets

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 07 Jun, 2024
5 min read

Introduction

The Coefficient of Variation (CV) is a valuable tool for comparing the variability of different datasets. It is most useful in instances where the units of the data are different. This article will explain what the CV is and how it’s applied in various industries. Moreover, it will give you step-by-step instructions on how to calculate a CV in Microsoft Excel and Google Sheets.

Overview

  • Learn what is a coefficient of variation.
  • Learn how to calculate the coefficient of variation in Microsoft Excel and Google Sheets.
  • Understand the real-life applications of this formula in data analysis.

Table of Contents

What is a Coefficient of Variation?

A coefficient of variation (CV) measures the data point dispersion around a mean. It allows us to compare the variability of two or more datasets that may have different units or different means, providing a standardized measure of dispersion.

The coefficient of variation shows how much the data in a sample varies in comparison to the mean of the data. A lower CV means that the data points are clustered closer to the mean, which indicates lower variability. Whereas, a higher CV shows a wider spread of data points around the mean, indicating higher variability.

For example, let’s consider data analysis in the financial and investment sector. Here, the volatility of prices can influence potential returns, and knowing the CV helps in making better market predictions. A lower CV means a lower risk-to-return ratio. This indicates a better trade-off between the potential risks and expected returns of a particular investment.

How to Calculate the Coefficient of Variation in Excel and Sheets

CV is calculated by dividing the dataset’s standard deviation by the mean and then multiplying the result by 100 to express it as a percentage.

Mathematically, the formula for CV is:

Coefficient of Variation (CV) formula

When we come to Excel, however, we don’t have a single built-in function to calculate the CV. The same applies to Google Sheets. Therefore, we need to follow a 3-step process to calculate it. For this tutorial, we have used a sample table of the average electricity consumption (in kWh) of houses in a block, on different days of the week.

Sample dataset

Here’s the step-by-step process to find the coefficient of variation in Microsoft Excel and Google Sheets:

  1. Calculate the Standard Deviation

    In an empty cell, we enter the formula =STDEV.P(B3:B9) to calculate the standard deviation of the energy consumption of House 1. We then calculate the same for all the houses. You can replace B3:B9 with the actual range of your data.Calculating Standard Deviation in Excel and Google Sheets

  2. Calculate the Mean

    In another empty cell, we enter the formula =AVERAGE(B3:B9) to calculate the mean energy consumption of House 1. We will then do the same for the other columns. You can again use the same data range used in the previous step.Calculate Mean or Average in Excel and Google Sheets

  3. Calculate the Coefficient of Variation

    In a final cell, we enter the formula =STDEV.P(B3:B9)/AVERAGE(B3:B9)*100. Alternatively, you can simply enter the formula =DIVIDE(A13,A12)*100 or the two respective cells from the previous steps. This divides the standard deviation by the mean and multiplies it by 100, resulting in the coefficient of variation.Calculate CV in Excel and Google Sheets

Practical Considerations in Calculating CV

  • Zero or Near-Zero Means: Be cautious when the mean is zero or close to zero, as CV can become misleading. In such cases, you can consider alternative measures like standard deviation or interquartile range.
  • Distribution Assumptions: CV is most effective with normally distributed data. Skewed distributions or outliers can distort the CV, so always examine the data distribution before relying solely on the CV.
  • Data Scales: Ensure your data aligns with CV assumptions. CV works best with ratio-scale data (e.g., prices, weights). Applying it to interval-scale data (e.g., temperatures) may lead to incorrect interpretations.

Real-Life Applications of the Coefficient of Variation

Data analysts in fields ranging from finance and risk management to manufacturing and medicine, calculate CVs to better understand the data. Let’s explore some of these real-life applications:

Finance and Risk Assessment

In the financial world, investors use CV values to identify assets that offer higher returns in relation to their inherent risk. This helps to build diversified portfolios and manage risks better. For instance, an investor can compare the standard deviation of returns from different stocks to their mean returns, to get an insight into their risk-return profiles.

Quality Control in Manufacturing

The CV is crucial in ensuring consistent product quality across different production batches. A lower CV indicates less variation in product characteristics, which is essential for maintaining brand standards and customer satisfaction. Industries where product reliability is critical, like pharmaceuticals and automotive manufacturing, heavily rely on CV analysis.

Weather Analysis

Meteorologists use the CV to analyze the consistency of weather patterns, such as rainfall variations across regions and time periods. This helps improve the accuracy of weather forecasts and plan weather-dependent activities such as agriculture.

Biology and Medicine

CV is employed to assess the repeatability and reliability of experimental results, ensuring consistency in scientific research. It is an essential part of understanding the effectiveness of new drugs that are in clinical trials.

Conclusion

The coefficient of variation is a powerful tool for comparing the relative variability of datasets. Calculating CV in Excel or Sheets can give you insights into the consistency of various data points. It is mostly used in analyzing financial returns, product quality, and the results of scientific research. With this step-by-step guide, you can easily apply this measure in your data analyses to make informed decisions based on data variability.

For more advanced data analysis techniques in Excel, you can explore our courses on Excel fundamentals and data visualization. Happy analyzing!

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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