Figures in scientific publications are critically important because they often show the data supporting key findings. Our systematic review of research articles published in top physiology journals (n = 703) suggests that, as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies. Papers rarely included scatterplots, box plots, and histograms that allow readers to critically evaluate continuous data. Most papers presented continuous data in bar and line graphs. This is problematic, as many different data distributions can lead to the same bar or line graph. The full data may suggest different conclusions from the summary statistics. We recommend training investigators in data presentation, encouraging a more complete presentation of data, and changing journal editorial policies. Investigators can quickly make univariate scatterplots for small sample size studies using our Excel templates.

The various performance criteria applied in this analysis include the probability of reaching the ultimate target, the costs, elapsed times and system vulnerability resulting from any intrusion. This Excel file contains all the logical, probabilistic and statistical data entered by a user, and required for the evaluation of the criteria. It also reports the results of all the computations.

Analyzing Coffee Shop Sales: Excel Insights 📈

In my first Data Analytics Project, I Discover the secrets of a fictional coffee shop's success with my data-driven analysis. By Analyzing a 5-sheet Excel dataset, I've uncovered valuable sales trends, customer preferences, and insights that can guide future business decisions. 📊☕

DATA CLEANING 🧹

• REMOVED DUPLICATES OR IRRELEVANT ENTRIES: Thoroughly eliminated duplicate records and irrelevant data to refine the dataset for analysis.

• FIXED STRUCTURAL ERRORS: Rectified any inconsistencies or structural issues within the data to ensure uniformity and accuracy.

• CHECKED FOR DATA CONSISTENCY: Verified the integrity and coherence of the dataset by identifying and resolving any inconsistencies or discrepancies.

DATA MANIPULATION 🛠️

• UTILIZED LOOKUPS: Used Excel's lookup functions for efficient data retrieval and analysis.

• IMPLEMENTED INDEX MATCH: Leveraged the Index Match function to perform advanced data searches and matches.

• APPLIED SUMIFS FUNCTIONS: Utilized SumIFs to calculate totals based on specified criteria.

• CALCULATED PROFITS: Used relevant formulas and techniques to determine profit margins and insights from the data.

PIVOTING THE DATA 𝄜

• CREATED PIVOT TABLES: Utilized Excel's PivotTable feature to pivot the data for in-depth analysis.

• FILTERED DATA: Utilized pivot tables to filter and analyze specific subsets of data, enabling focused insights. Specially used in “PEAK HOURS” and “TOP 3 PRODUCTS” charts.

VISUALIZATION 📊

• KEY INSIGHTS: Unveiled the grand total sales revenue while also analyzing the average bill per person, offering comprehensive insights into the coffee shop's performance and customer spending habits.

• SALES TREND ANALYSIS: Used Line chart to compute total sales across various time intervals, revealing valuable insights into evolving sales trends.

• PEAK HOUR ANALYSIS: Leveraged Clustered Column chart to identify peak sales hours, shedding light on optimal operating times and potential staffing needs.

• TOP 3 PRODUCTS IDENTIFICATION: Utilized Clustered Bar chart to determine the top three coffee types, facilitating strategic decisions regarding inventory management and marketing focus.

*I also used a Timeline to visualize chronological data trends and identify key patterns over specific times.

While it's a significant milestone for me, I recognize that there's always room for growth and improvement. Your feedback and insights are invaluable to me as I continue to refine my skills and tackle future projects. I'm eager to hear your thoughts and suggestions on how I can make my next endeavor even more impactful and insightful.

THANKS TO: WsCube Tech Mo Chen Alex Freberg

TOOLS USED: Microsoft Excel

DataAnalytics #DataAnalyst #ExcelProject #DataVisualization #BusinessIntelligence #SalesAnalysis #DataAnalysis #DataDrivenDecisions

Enterprise-Value-To-Ebitda-Ratio Time Series for Excel Force MSC Bhd. Excel Force MSC Berhad, together with its subsidiaries, develops, provides, and maintains software application solutions for the financial services industry in Malaysia. The company operates through Application Solutions, Maintenance Services, Application Services Provider, and Other segments. Its product portfolio includes CyberStock BTX, a bridging trader and exchange system platform that provides trading tools classes; and CyberStock ECOS, a stock broking solution which offers real time market information, place trades, and manage orders solution. In addition, the company provides CyberStock Mobile Trader, a mobile trading system that connects users smartphones to exchanges to manage trading activities; and CyberStock EDS, an exempt dealer system that provides advanced trading infrastructure and facilities for commercial banks. Further, it offers CyberStock SMF, a share margin financing system that enables financial institutions, brokerage firms, and banks to operate and manage margin financing services; and CyberStock CNS, a custodian and nominee system, which provides value-added services, such as trade settlement, cash balances investment, income collection, corporate actions processing, recordkeeping and reporting to custodian banks for domestic services. Additionally, the company provides CyberStock BOS, a back office system to manage enormous file and data; and offers network and security services. Excel Force MSC Berhad was founded in 1994 and is based in Petaling Jaya, Malaysia.

Working-Capital-Turnover Time Series for Excel Force MSC Bhd. Excel Force MSC Berhad, together with its subsidiaries, develops, provides, and maintains software application solutions for the financial services industry in Malaysia. The company operates in four segments: Application Solutions, Maintenance Services, Application Services Provider, and Other segments. Its product portfolio includes CyberBroker Front Office for client-server, web, and mobile-based stock trading systems; CyberBroker Middle Office; CyberBroker Back Office, including custodian and nominee systems; StockBanking System comprising share margin financing systems; and fundamental analysis systems. The company also offers eForce One, a web trading platform that operates as an electronic client ordering system; Mobile Trade 3.5G, a mobile trading system; eForce Interactive X-Chart, a charting tool; eForce EmPower, a back-office system; and Cyberstock, a dealer and remisier system. In addition, it provides investment advisory services. Excel Force MSC Berhad was founded in 1994 and is based in Petaling Jaya, Malaysia.

Operating-Expenses Time Series for Excel Force MSC Bhd. Excel Force MSC Berhad, together with its subsidiaries, develops, provides, and maintains software application solutions for the financial services industry in Malaysia. The company operates in four segments: Application Solutions, Maintenance Services, Application Services Provider, and Other segments. Its product portfolio includes CyberBroker Front Office for client-server, web, and mobile-based stock trading systems; CyberBroker Middle Office; CyberBroker Back Office, including custodian and nominee systems; StockBanking System comprising share margin financing systems; and fundamental analysis systems. The company also offers eForce One, a web trading platform that operates as an electronic client ordering system; Mobile Trade 3.5G, a mobile trading system; eForce Interactive X-Chart, a charting tool; eForce EmPower, a back-office system; and Cyberstock, a dealer and remisier system. In addition, it provides investment advisory services. Excel Force MSC Berhad was founded in 1994 and is based in Petaling Jaya, Malaysia.

📌 Project Overview This project analyzes hospital admissions, patient stays, and cost trends using Excel. The dataset contains information on patient demographics, hospital names, insurance providers, and treatment costs. Key insights were derived using PivotTables, charts, and formulas.

📊 Key Insights & Visualizations ✅ Top Hospitals by Admissions → Bar Chart ✅ Insurance Provider with Most Patients → Pie Chart ✅ Cost per Day Trends → Line Chart ✅ Average Length of Stay per Hospital → Bar Chart

🛠 Excel Analysis Techniques Used PivotTables for summarizing patient data

Conditional Formatting to highlight cost trends

Bar, Pie, and Line Charts for visualization

Statistical Analysis (Average length of stay, cost trends)

📂 Files Included 📌 hospital_analysis.xlsx – The full Excel analysis file 📌 hospital_summary.pdf – Summary of key findings

Healthcare #HospitalData #ExcelAnalysis #DataVisualization #PivotTables #DataCleaning #MedicalAnalytics #PatientTrends #CostAnalysis #AdmissionsAnalysis #InsuranceData #DataAnalysis #ExcelDashboards #HealthTech

These charts provide a snapshot of the domestic and global market for rice, the primary staple for more than half the world's population. Excel files are available from the monthly Outlook reports.

This repository contains a collection of data about 454 value chains from 23 rural European areas of 16 countries. This data is obtained through a semi-automatic workflow that transforms raw textual data from an unstructured MS Excel sheet into semantic knowledge graphs.In particular, the repository contains:MS Excel sheet containing different value chains details provided by MOuntain Valorisation through INterconnectedness and Green growth (MOVING) European project;454 CSV files containing events, titles, entities and coordinates of narratives of each value chain, obtained by pre-processing the MS Excel sheet454 Web Ontology Language (OWL) files. This collection of files is the result of the semi-automatic workflow, and is organized as a semantic knowledge graph of narratives, where each narrative is a sub-graph explaining one among the 454 value chains and its territory aspects. The knowledge graph is based on the Narrative Ontology, an ontology developed by Institute of Information Science and Technologies (ISTI-CNR) as an extension of CIDOC CRM, FRBRoo, and OWL Time.Two CSV files that compile all the possible available information extracted from 454 Web Ontology Language (OWL) files.GeoPackage files with the geographic coordinates related to the narratives.The HTML files that show all the different SPARQL and GeoSPARQL queries.The HTML files that show the story maps about the 454 value chains.An image showing how the various components of the dataset interact with each other.

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

Excel spreadsheets by species (4 letter code is abbreviation for genus and species used in study, year 2010 or 2011 is year data collected, SH indicates data for Science Hub, date is date of file preparation). The data in a file are described in a read me file which is the first worksheet in each file. Each row in a species spreadsheet is for one plot (plant). The data themselves are in the data worksheet. One file includes a read me description of the column in the date set for chemical analysis. In this file one row is an herbicide treatment and sample for chemical analysis (if taken). This dataset is associated with the following publication: Olszyk , D., T. Pfleeger, T. Shiroyama, M. Blakely-Smith, E. Lee , and M. Plocher. Plant reproduction is altered by simulated herbicide drift toconstructed plant communities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(10): 2799-2813, (2017).

This article describes a free, open-source collection of templates for the popular Excel (2013, and later versions) spreadsheet program. These templates are spreadsheet files that allow easy and intuitive learning and the implementation of practical examples concerning descriptive statistics, random variables, confidence intervals, and hypothesis testing. Although they are designed to be used with Excel, they can also be employed with other free spreadsheet programs (changing some particular formulas). Moreover, we exploit some possibilities of the ActiveX controls of the Excel Developer Menu to perform interactive Gaussian density charts. Finally, it is important to note that they can be often embedded in a web page, so it is not necessary to employ Excel software for their use. These templates have been designed as a useful tool to teach basic statistics and to carry out data analysis even when the students are not familiar with Excel. Additionally, they can be used as a complement to other analytical software packages. They aim to assist students in learning statistics, within an intuitive working environment. Supplementary materials with the Excel templates are available online.

Days-of-Sales-Outstanding Time Series for Excel Force MSC Bhd. Excel Force MSC Berhad, together with its subsidiaries, develops, provides, and maintains software application solutions for the financial services industry in Malaysia. The company operates through Application Solutions, Maintenance Services, Application Services Provider, and Other segments. Its product portfolio includes CyberStock BTX, a bridging trader and exchange system platform that provides trading tools classes; and CyberStock ECOS, a stock broking solution which offers real time market information, place trades, and manage orders solution. In addition, the company provides CyberStock Mobile Trader, a mobile trading system that connects users smartphones to exchanges to manage trading activities; and CyberStock EDS, an exempt dealer system that provides advanced trading infrastructure and facilities for commercial banks. Further, it offers CyberStock SMF, a share margin financing system that enables financial institutions, brokerage firms, and banks to operate and manage margin financing services; and CyberStock CNS, a custodian and nominee system, which provides value-added services, such as trade settlement, cash balances investment, income collection, corporate actions processing, recordkeeping and reporting to custodian banks for domestic services. Additionally, the company provides CyberStock BOS, a back office system to manage enormous file and data; and offers network and security services. Excel Force MSC Berhad was founded in 1994 and is based in Petaling Jaya, Malaysia.

This data set is perfect for practicing your analytical skills for Power BI, Tableau, Excel, or transform it into a CSV to practice SQL.

This use case mimics transactions for a fictional eCommerce website named EverMart Online. The 3 tables in this data set are all logically connected together with IDs.

My Power BI Use Case Explanation - Using Microsoft Power BI, I made dynamic data visualizations for revenue reporting and customer behavior reporting.

Revenue Reporting Visuals - Data Card Visual that dynamically shows Total Products Listed, Total Unique Customers, Total Transactions, and Total Revenue by Total Sales, Product Sales, or Categorical Sales. - Line Graph Visual that shows Total Revenue by Month of the entire year. This graph also changes to calculate Total Revenue by Month for the Total Sales by Product and Total Sales by Category if selected. - Bar Graph Visual showcasing Total Sales by Product. - Donut Chart Visual showcasing Total Sales by Category of Product.

Customer Behavior Reporting Visuals - Data Card Visual that dynamically shows Total Products Listed, Total Unique Customers, Total Transactions, and Total Revenue by Total or by continent selected on the map. - Interactive Map Visual showing key statistics for the continent selected. - The key statistics are presented on the tool tip when you select a continent, and the following statistics show for that continent: - Continent Name - Customer Total - Percentage of Products Sold - Percentage of Total Customers - Percentage of Total Transactions - Percentage of Total Revenue

About Datasets:

Domain : Sales Project: McDonalds Sales Analysis Project Dataset: START-Dashboard Dataset Type: Excel Data Dataset Size: 100 records

KPI's: 1. Customer Satisfaction 2. Sales by Country 2022 3. 2021-2022 Sales Trend 4. Sales 5. Profit 6. Customers

Process: 1. Understanding the problem 2. Data Collection 3. Exploring and analyzing the data 4. Interpreting the results

This data contains dashboard, hyperlink, shapes, icons, map, radar chart, line chart, doughnut chart, KPIs, formatting.

🚘 Electric Vehicle (EV) Sales Analysis & Forecasting

The Electric Vehicle (EV) Sales Analysis & Forecasting project explores India’s EV adoption trends from 2014 to 2024, combining BigQuery for data analytics and Power BI for visualization. This end-to-end data project demonstrates expertise in SQL-based data analysis, time-series forecasting using BigQuery ML (ARIMA+), and interactive dashboard design. This repository presents an in-depth data analysis and visualization of Electric Vehicle (EV) sales trends across India using Microsoft Excel, SQL, Python and Power BI.

The Electric Vehicle Sales Dashboard is an interactive and visual analytics solution built using Power BI, focused on analyzing EV adoption trends across India. It includes KPIs, comparative charts, and advanced metrics to assist stakeholders in understanding EV sales performance from 2014 to 2024.

⚙️ Tech Stack

Google BigQuery → Data storage, cleaning, and analysis using SQL

BigQuery ML (ARIMA_PLUS) → Time series forecasting for future EV sales prediction

Power BI → Interactive dashboard for visualization and storytelling

Pyhton → For eda and visualization

Excel / CSV → Initial dataset preparation and transformation

🔹 Key Features: ✅ Total EV Sales Trends: Year-over-year and month-over-month change

✅ State-wise EV Performance: Compare EV adoption across Indian states

✅ Vehicle Category Breakdown: 2W, 3W, 4W, Buses, Others

✅ Dynamic Filtering: Use slicers to filter by Year, Month, State.

✅ KPI Cards: Total Sales, Average Sales

📊 Dashboard Overview Components:

Section Description 🔹 KPI Cards Highlight key performance indicators like Total Sales, Average Sales. 📈 Trend Charts Year-wise and month-wise sales volume (bar/line charts) 🗺️ Geo Analysis State-wise sales with ranking and contribution 🚗 Vehicle Type Pie Distribution of sales by 2W, 3W, 4W, Bus, Others 📅 Time Series Filtering and Interactive slicers for Year, Month, and State. 📊 YOY / MOM Change Dedicated visual section with % growth/dip over time

Dashboard using Power BI

https://github.com/user-attachments/assets/eca3b9c3-b446-4447-a38a-d61f5572a712" alt="DashBoards">

Dashboard using Excel

https://github.com/user-attachments/assets/f7cc0949-4581-4cb3-b3ab-ea9242bce803" alt="Dashboard excel">

🗓️ Year-on-Year EV Sales (2014–2024) - EV sales have grown from 2.4K in 2014 to 1.5M in 2023. - Peak growth observed in: - 2021 → 2022: +209% YoY growth - 2020 → 2021: +165% YoY growth - 2024 saw a drop (as of current data): -90.61% https://github.com/user-attachments/assets/02965465-d6cf-45bf-b127-40feb6fe9963" alt="yoy">

📆 Month-on-Month (MoM) Analysis - Highest sales months: November, December, and January - Noticeable drop in February followed by recovery in March https://github.com/user-attachments/assets/83bce2dd-b2ae-43b9-bdb2-def5b9dbe37d" alt="mom">

📍 State-wise Performance - Top Performing States: - 🥇 Uttar Pradesh: ~730K units - 🥈 Maharashtra: ~400K units - 🥉 Karnataka: ~320K units - Lower adoption in northeastern and union territory regions - https://github.com/user-attachments/assets/1ee491b5-9016-4a1e-850f-34e8b9a9aab3" alt="state wise ev">

EV State wise Sale correlation https://github.com/user-attachments/assets/4c5fc55d-389d-4bed-a9bb-eebe8ce31a11" alt="EV corr">

Top 5 State KDE Plot https://github.com/user-attachments/assets/1b9e2f69-9ea0-4ac4-8c26-0350fb75bd75" alt="EV kde plot">

🚗 EV Sales by Vehicle Type - 2-Wheelers: 50.3% of total sales - 3-Wheelers: 45.1% - 4-Wheelers and Buses: Small but growing segment https://github.com/user-attachments/assets/2fc64426-4e3d-4a14-b058-f7c67040663b" alt="vtype wise ev">

📅 Year-wise EV Sales Trend EV adoption has grown significantly from 2,392 units in 2014 to over 1.5 million units in 2023

Fastest growth period: 2020–2022, with YoY gains of 165% and 209%

2024 shows a decline (-90.6%) due to possible data incompleteness https://github.com/user-attachments/assets/d60567fb-fc61-463f-b0e8-34ddd14f1b8d" alt="Year wise ev">

------------------------------...

📊 Road Accident Data Analysis: Interactive Excel Dashboard 🚗

Excited to share my Kaggle project focusing on road accident data analysis. Leveraging Excel's power, I've developed an interactive dashboard offering comprehensive insights for safer roads.

Key Aspects:

Data Processing & Cleaning: Ensured data reliability through meticulous processing. KPIs: Primarily focused on Total Casualties, with detailed breakdowns for Fatal, Serious, Slight, and by Car type. Visualizations: Engaging charts - Doughnuts, Line, Bar, and Pie - offering a holistic view of accident trends. Interactivity: User-friendly features include Urban/Rural and Year filters for dynamic exploration. Unique Insights:

Monthly Trends: Line chart for a nuanced comparison of current vs. previous year casualties. Road Type Breakdown: Bar chart to showcase casualties distributed across different road types. Geospatial Analysis: Doughnut charts detailing casualties by location and area. Call for Collaboration: Seeking Kaggle community input for refinement and optimization. Let's collectively contribute to making our roads safer through data-driven insights!

DataAnalysis #RoadSafety #InteractiveDashboard #KaggleProject #Excel #DataVisualization #CollaborationOpportunity

Looking forward to your feedback and contributions! 🚀🌐

About Datasets:

Domain : Finance Project: Bank loan of customers Datasets: Finance_1.xlsx & Finance_2.xlsx Dataset Type: Excel Data Dataset Size: Each Excel file has 39k+ records

KPI's: 1. Year wise loan amount Stats 2. Grade and sub grade wise revolving balance 3. Total Payment for Verified Status Vs Total Payment for Non Verified Status 4. State wise loan status 5. Month wise loan status 6. Get more insights based on your understanding of the data

Process: 1. Understanding the problem 2. Data Collection 3. Data Cleaning 4. Exploring and analyzing the data 5. Interpreting the results

This data contains Power Query, Power Pivot, Merge data, Clustered Bar Chart, Clustered Column Chart, Line Chart, 3D Pie chart, Dashboard, slicers, timeline, formatting techniques.

Results of the heuristic evaluation performed on four different versions of the same chart created with Microsoft Excel (XSLX, DOCX, HTML and SVG) related to the paper published in: Alcaraz Martínez, Rubén; Ribera, Mireia, Roig, Jordi; Pascual, Afra. Can we create accessible charts with Microsoft MS Excel?: a review of possibilities and limits, with a special focus to users with low vision. In Interacción '24: Proceedings of the XXIV International Conference on Human Computer Interaction.

Purpose – The conversion between calibrated airspeed (CAS) and equivalent airspeed (EAS) is relatively cumbersome, because it involves the calculation of incompressible flow, for which the equations are quite long. If calculations on the computer are required, conversions with equations are necessary. In contrast, this project calculates a CAS to EAS Compressibility Correction Chart, which allows to convert CAS to EAS very quickly by reading the correction from a graph. --- Methodology – In Excel, compressibility correction is achieved through flight mechanics formulas. The correction is calculated with two distinct functions, one based on Mach Number and the other on pressure altitude. These functions are graphed individually and then integrated to produce the Compressibility Correction Chart. --- Findings – The Compressibility Correction Chart was successfully recreated as a 2-D graph. Upon comparison with other correction charts, the EAS-CAS-results demonstrate a mere 0% deviation, proving the accuracy of the findings and validating their near-perfect alignment. --- Research Limitations – Due to a limitation in Excel, which allows for 255 series for plotting, the range of input parameters had to be adjusted accordingly. The iterations of altitude span 1000 ft intervals, while those for Mach Number span 0.05 intervals. --- Practical Implications – Pilots can easily use the Compressibility Correction Chart for quick and highly accurate calculations when needed. --- Originality – CAS-EAS Compressibility Correction Charts are available in other sources. This paper represents a recreation of the 2-D Correction Chart by the combination of plots: one as function of Mach Number and the other of pressure altitude, using the Excel Software.