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.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

Example of how I use MS Excel's VLOOKUP() function to filter my data.

Analyzing sales data is essential for any business looking to make informed decisions and optimize its operations. In this project, we will utilize Microsoft Excel and Power Query to conduct a comprehensive analysis of Superstore sales data. Our primary objectives will be to establish meaningful connections between various data sheets, ensure data quality, and calculate critical metrics such as the Cost of Goods Sold (COGS) and discount values. Below are the key steps and elements of this analysis:

1- Data Import and Transformation:

• Gather and import relevant sales data from various sources into Excel.
• Utilize Power Query to clean, transform, and structure the data for analysis.
• Merge and link different data sheets to create a cohesive dataset, ensuring that all data fields are connected logically.

2- Data Quality Assessment:

• Perform data quality checks to identify and address issues like missing values, duplicates, outliers, and data inconsistencies.
• Standardize data formats and ensure that all data is in a consistent, usable state.

3- Calculating COGS:

• Determine the Cost of Goods Sold (COGS) for each product sold by considering factors like purchase price, shipping costs, and any additional expenses.
• Apply appropriate formulas and calculations to determine COGS accurately.

4- Discount Analysis:

• Analyze the discount values offered on products to understand their impact on sales and profitability.
• Calculate the average discount percentage, identify trends, and visualize the data using charts or graphs.

5- Sales Metrics:

• Calculate and analyze various sales metrics, such as total revenue, profit margins, and sales growth.
• Utilize Excel functions to compute these metrics and create visuals for better insights.

6- Visualization:

• Create visualizations, such as charts, graphs, and pivot tables, to present the data in an understandable and actionable format.
• Visual representations can help identify trends, outliers, and patterns in the data.

7- Report Generation:

• Compile the findings and insights into a well-structured report or dashboard, making it easy for stakeholders to understand and make informed decisions.

Throughout this analysis, the goal is to provide a clear and comprehensive understanding of the Superstore's sales performance. By using Excel and Power Query, we can efficiently manage and analyze the data, ensuring that the insights gained contribute to the store's growth and success.

Time-Series Matrix (TSMx): A visualization tool for plotting multiscale temporal trends TSMx is an R script that was developed to facilitate multi-temporal-scale visualizations of time-series data. The script requires only a two-column CSV of years and values to plot the slope of the linear regression line for all possible year combinations from the supplied temporal range. The outputs include a time-series matrix showing slope direction based on the linear regression, slope values plotted with colors indicating magnitude, and results of a Mann-Kendall test. The start year is indicated on the y-axis and the end year is indicated on the x-axis. In the example below, the cell in the top-right corner is the direction of the slope for the temporal range 2001–2019. The red line corresponds with the temporal range 2010–2019 and an arrow is drawn from the cell that represents that range. One cell is highlighted with a black border to demonstrate how to read the chart—that cell represents the slope for the temporal range 2004–2014. This publication entry also includes an excel template that produces the same visualizations without a need to interact with any code, though minor modifications will need to be made to accommodate year ranges other than what is provided. TSMx for R was developed by Georgios Boumis; TSMx was originally conceptualized and created by Brad G. Peter in Microsoft Excel. Please refer to the associated publication: Peter, B.G., Messina, J.P., Breeze, V., Fung, C.Y., Kapoor, A. and Fan, P., 2024. Perspectives on modifiable spatiotemporal unit problems in remote sensing of agriculture: evaluating rice production in Vietnam and tools for analysis. Frontiers in Remote Sensing, 5, p.1042624. https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2024.1042624 TSMx sample chart from the supplied Excel template. Data represent the productivity of rice agriculture in Vietnam as measured via EVI (enhanced vegetation index) from the NASA MODIS data product (MOD13Q1.V006). TSMx R script: # import packages library(dplyr) library(readr) library(ggplot2) library(tibble) library(tidyr) library(forcats) library(Kendall) options(warn = -1) # disable warnings # read data (.csv file with "Year" and "Value" columns) data <- read_csv("EVI.csv") # prepare row/column names for output matrices years <- data %>% pull("Year") r.names <- years[-length(years)] c.names <- years[-1] years <- years[-length(years)] # initialize output matrices sign.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) pval.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) slope.matrix <- matrix(data = NA, nrow = length(years), ncol = length(years)) # function to return remaining years given a start year getRemain <- function(start.year) { years <- data %>% pull("Year") start.ind <- which(data[["Year"]] == start.year) + 1 remain <- years[start.ind:length(years)] return (remain) } # function to subset data for a start/end year combination splitData <- function(end.year, start.year) { keep <- which(data[['Year']] >= start.year & data[['Year']] <= end.year) batch <- data[keep,] return(batch) } # function to fit linear regression and return slope direction fitReg <- function(batch) { trend <- lm(Value ~ Year, data = batch) slope <- coefficients(trend)[[2]] return(sign(slope)) } # function to fit linear regression and return slope magnitude fitRegv2 <- function(batch) { trend <- lm(Value ~ Year, data = batch) slope <- coefficients(trend)[[2]] return(slope) } # function to implement Mann-Kendall (MK) trend test and return significance # the test is implemented only for n>=8 getMann <- function(batch) { if (nrow(batch) >= 8) { mk <- MannKendall(batch[['Value']]) pval <- mk[['sl']] } else { pval <- NA } return(pval) } # function to return slope direction for all combinations given a start year getSign <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) signs <- lapply(combs, fitReg) return(signs) } # function to return MK significance for all combinations given a start year getPval <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) pvals <- lapply(combs, getMann) return(pvals) } # function to return slope magnitude for all combinations given a start year getMagn <- function(start.year) { remaining <- getRemain(start.year) combs <- lapply(remaining, splitData, start.year = start.year) magns <- lapply(combs, fitRegv2) return(magns) } # retrieve slope direction, MK significance, and slope magnitude signs <- lapply(years, getSign) pvals <- lapply(years, getPval) magns <- lapply(years, getMagn) # fill-in output matrices dimension <- nrow(sign.matrix) for (i in 1:dimension) { sign.matrix[i, i:dimension] <- unlist(signs[i]) pval.matrix[i, i:dimension] <- unlist(pvals[i]) slope.matrix[i, i:dimension] <- unlist(magns[i]) } sign.matrix <-...

Introduction

We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file Short_Description_of_Data_Analysis.pdf and Statistical_formulas.pdf

The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.

The dataset was expanded on 23rd September 2024 to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.

Short Description of Data Analysis and Attached Files (datasets):

Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.

Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: https://www.ksh.hu/stadat_files/nep/hu/nep0035.html, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the Data_of_demography.xlsx file.

In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.

The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): https://www.nnk.gov.hu/index.php/efi (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: https://semmelweis.hu/egeszsegfejlesztes/en/ (n=1). (accessed 05 Dec. 2023.)

Geocoordinates were determined using Google Maps (N=108): https://www.google.com/maps. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file.

The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:

https://qgis.org/en/site/forusers/download.html. (accessed 04 Jan. 2024.)

The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the

Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file,

imported via .csv file.

The OpenStreetMap tileset is directly accessible from www.openstreetmap.org in QGIS. (accessed 04 Jan. 2024.)

The Hungarian county administrative boundaries were downloaded from the following website: https://data2.openstreetmap.hu/hatarok/index.php?admin=6 (accessed 04 Jan. 2024.)

HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.

Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).

A brief description of the statistical formulas applied is included in the Statistical_formulas.pdf.

Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.

• Aggregated number of HDOs by county: Number_of_HDOs.xlsx
• Standardised data (Number of HDOs per 100,000 residents): Standardized_data.xlsx
• Calculation of the Lorenz curve: Lorenz_curve.xlsx
• Calculation of the Gini index: Gini_Index.xlsx
• Calculation of the LQ index: LQ_Index.xlsx
• Calculation of the Herfindahl-Hirschman Index: Herfindahl_Hirschman_Index.xlsx
• Calculation of the Entropy index: Entropy_Index.xlsx
• Regression and correlation analysis calculation: Regression_correlation.xlsx

Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:

• Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv
• Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.
• Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.
• Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv
• Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv
• Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv
• Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv
• Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv
• Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv

For easier readability, the files have been provided in both SPV and PDF formats.

The translation of these supplementary files into English was completed on 23rd Sept. 2024.

If you have any further questions regarding the dataset, please contact the corresponding author: domjan.peter@phd.semmelweis.hu

The hectares of habitat protected and the number of adults and children fed in one year were calculated for each of the six crop types for Canada and United States. The calculations were based on the 50th centile of the cumulative frequency distributions of change in crop yield due to pesticide treatment for each crop type. An editable interactive table was created using Microsoft Excel that would allow individuals to determine how pesticide treatment in their selected jurisdiction (province in Canada or state in the United States) and crop translates into habitat saved, calories produced, and mouths fed. This table allows the user to choose the country (Canada or United States), whether to include the organic agriculture correction factor, their state or province of interest, crop, and whether a young child, adolescent child, adult women, or adult man is being fed. The table will then calculate the hectares of habitat saved, added number of calories produced (kcal), the number of individual fed in one day, and the number of individual fed in one year. Due to the variability in yield results between crops and studies, the Excel user form allows individuals to set whichever yield increase they anticipate observing or use the 50th centile of yield increase from the cumulative frequency distribution for each crop.

The Florida Flood Hub for Applied Research and Innovation and the U.S. Geological Survey have developed projected future change factors for precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida. The change factors were computed as the ratio of projected future to historical extreme-precipitation depths fitted to extreme-precipitation data from downscaled climate datasets using a constrained maximum likelihood (CML) approach as described in https://doi.org/10.3133/sir20225093. The change factors correspond to the periods 2020-59 (centered in the year 2040) and 2050-89 (centered in the year 2070) as compared to the 1966-2005 historical period. An R script (basin_boxplot.R) is provided as an example on how to create a wrapper function that will automate the generation of boxplots of change factors for all Florida HUC-8 basins. The wrapper script sources the file create_boxplot.R and calls the function create_boxplot() one Florida basin at a time to create a figure with boxplots of change factors for all durations (1, 3, and 7 days) and return periods (5, 10, 25, 50, 100, 200, and 500 years) evaluated as part of this project. An example is also provided in the code that shows how to generate a figure showing boxplots of change factors for a single duration and return period. A Microsoft Word file documenting code usage is also provided within this data release (Documentation_R_script_create_boxplot.docx). As described in the documentation, the R script relies on some of the Microsoft Excel spreadsheets published as part of this data release. The script uses HUC-8 basins defined in the "Florida Hydrologic Unit Code (HUC) Basins (areas)" from the Florida Department of Environmental Protection (FDEP; https://geodata.dep.state.fl.us/datasets/FDEP::florida-hydrologic-unit-code-huc-basins-areas/explore) and their names are listed in the file basins_list.txt provided with the script. County names are listed in the file counties_list.txt provided with the script. NOAA Atlas 14 stations located in each Florida basin or county are defined in the Microsoft Excel spreadsheet Datasets_station_information.xlsx which is part of this data release. Instructions are provided in code documentation (see highlighted text on page 7 of Documentation_R_script_create_boxplot.docx) so that users can modify the script to generate boxplots for basins different from the FDEP "Florida Hydrologic Unit Code (HUC) Basins (areas)."

This dataset contains the valuation template the researcher can use to retrieve real-time Excel stock price and stock price in Google Sheets. The dataset is provided by Finsheet, the leading financial data provider for spreadsheet users. To get more financial data, visit the website and explore their function. For instance, if a researcher would like to get the last 30 years of income statement for Meta Platform Inc, the syntax would be =FS_EquityFullFinancials("FB", "ic", "FY", 30) In addition, this syntax will return the latest stock price for Caterpillar Inc right in your spreadsheet. =FS_Latest("CAT") If you need assistance with any of the function, feel free to reach out to their customer support team. To get starter, install their Excel and Google Sheets add-on.

Spreadsheet from the paper entitled: Revisiting a Statistical Shortcoming when Fitting the Langmuir Model to Sorption Data by C.H. Bolster, Journal of Environmental Quality, 2008, 37:1986-1992. Spreadsheet has been modified to make a correction to the calculation of E for weighted data. (3/18/2010). Sorption models are commonly used for describing solute and metal sorption to soils. When fitting sorption models to sorption data, however, the user must be aware that certain statistical limitations exist with both linear and nonlinear versions of the models. Ongoing research at the Animal Waste Management Research Unit of the USDA-ARS addresses the effect of these statistical limitations on fitting phosphorus sorption data with various sorption models. This research was originally part of the former USDA-ARS National Program 206: Manure and By-product Utilization. Resources in this dataset:Resource Title: Modified Langmuir Equation Spreadsheet. File Name: Web Page, url: https://www.ars.usda.gov/research/software/download/?softwareid=205&modecode=50-40-05-00 download page

📊 Instagram Reach Analysis | تحليل الوصول في إنستغرام

An exploratory data analysis project using Excel to understand what influences Instagram post reach and engagement.
مشروع تحليل استكشافي لفهم العوامل المؤثرة في وصول منشورات إنستغرام وتفاعل المستخدمين، باستخدام Excel.

📁 Project Description | وصف المشروع

This project uses an Instagram dataset imported from Kaggle to explore how different factors like hashtags, saves, shares, and caption length influence impressions and engagement.
يستخدم هذا المشروع بيانات من إنستغرام تم استيرادها من منصة Kaggle لتحليل كيف تؤثر عوامل مثل الهاشتاقات، الحفظ، المشاركة، وطول التسمية التوضيحية في عدد مرات الظهور والتفاعل.

🛠️ Tools Used | الأدوات المستخدمة

• Microsoft Excel
• Pivot Tables
• TRIM, WRAP, and other Excel formulas
• مايكروسوفت إكسل
• الجداول المحورية
• دوال مثل TRIM و WRAP وغيرها في Excel

🧹 Data Cleaning | تنظيف البيانات

• Removed unnecessary spaces using TRIM
• Removed 17 duplicate rows → 103 unique rows remained

• Standardized formatting: freeze top row, wrap text, center align

• إزالة المسافات غير الضرورية باستخدام TRIM

• حذف 17 صفًا مكررًا → تبقى 103 صفوف فريدة

• تنسيق موحد: تثبيت الصف الأول، لف النص، وتوسيط المحتوى

🔍 Key Analysis Highlights | أبرز نتائج التحليل

1. Impressions by Source | مرات الظهور حسب المصدر

• Highest reach: Home > Hashtags > Explore > Other
• Some totals exceed 100% due to overlapping

2. Engagement Insights | رؤى حول التفاعل

• Saves strongly correlate with higher impressions
• Caption length is inversely related to likes
• Shares have weak correlation with impressions

3. Hashtag Patterns | تحليل الهاشتاقات

• Most used: #Thecleverprogrammer, #Amankharwal, #Python
• Repeating hashtags does not guarantee higher reach

✅ Conclusion | الخلاصة

Shorter captions and higher save counts contribute more to reach than repeated hashtags. Profile visits are often linked to new followers.
العناوين القصيرة وعدد الحفظات تلعب دورًا أكبر في الوصول من تكرار الهاشتاقات. كما أن زيارات الملف الشخصي ترتبط غالبًا بزيادة المتابعين.

👩‍💻 Author | المؤلفة

Raghad's LinkedIn

🧠 Inspiration | الإلهام

Inspired by content from TheCleverProgrammer, Aman Kharwal, and Kaggle datasets.
استُلهم المشروع من محتوى TheCleverProgrammer وأمان خروال، وبيانات من Kaggle.

💬 Feedback | الملاحظات

Feel free to open an issue or share suggestions!
يسعدنا تلقي ملاحظاتكم واقتراحاتكم عبر صفحة المشروع.

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

An excel-based sheet to calculate training zones and heart rate intensity according to the Karvonen formula (1). The user will download the heart rate data second-by-second from the device (ej.: Polar, Garmin, FitBit…). After providing the participant's maximum heart rate (maxHR) and resting heart rate (resting HR), the user will be able to set the different work zones according to their preferences using the heart rate reserve (%HRR). Once these data are entered, the excel will return the time (hh:mm:ss and % format) that the person has been working in the different zones according to Karvonen's formula. In addition, it includes Tanaka's formula (2) to calculate the maxHR in case it is not known by the user.

Excel sheets in order: The sheet entitled “Hens Original Data” contains the results of an experiment conducted to study the response of laying hens during initial phase of egg production subjected to different intakes of dietary threonine. The sheet entitled “Simulated data & fitting values” contains the 10 simulated data sets that were generated using a standard procedure of random number generator. The predicted values obtained by the new three-parameter and conventional four-parameter logistic models were also appeared in this sheet. (XLSX)

This is the Excel file for the PhD study of Jack Brimmell entitled: A longitudinal examination of executive function, visual attention, and soccer penalty performance.

Excel spreadsheet contain raw data extracted from manuscripts to calculate the infection rate (IR), stepwise dissemination rate (SDR), cumulative dissemination rate (CDR), stepwise transmission rate (STR) and cumulative transmission rate (CTR) presented in Table 5.

Background: The human ear is unique to individuals and ear prints, like fingerprints, are discrete enough to distinguish even identical twins. So, external ear can be used to identify both living and deceased individuals. So, this study was conducted to evaluate stature and sexual dimorphism of the external ear using various morphometric parameters and morphological features for forensic identification.Methods: Study was conducted with 40 participants. Various measurements were taken, and morphological features of both right and left ear were noted. Later the results were analyzed statistically.Results: In males and females most, common shape was oval, and ear lobe was free. Most common shape of ear lobe in males was arched and in females was triangular. In males and females most common form of helix was concave marginal. In males most common shape of tragus was round while in females it was long. In males and females most common shape of Darwin’s tubercle was enlarged. It was seen that the right lobe width showed perfect separation, indicating its potential as an extremely reliable predictor. It was noted that in females the strongest correlation with height is with the ear inclination angle of both sides.Conclusion: The results of this study can help the forensic anthropologist in identifying the sex and stature of a person from various ear measurements.

NaiveBayes_R.xlsx: This Excel file includes information as to how probabilities of observed features are calculated given recidivism (P(x_ij│R)) in the training data. Each cell is embedded with an Excel function to render appropriate figures. P(Xi|R): This tab contains probabilities of feature attributes among recidivated offenders. NIJ_Recoded: This tab contains re-coded NIJ recidivism challenge data following our coding schema described in Table 1. Recidivated_Train: This tab contains re-coded features of recidivated offenders. Tabs from [Gender] through [Condition_Other]: Each tab contains probabilities of feature attributes given recidivism. We use these conditional probabilities to replace the raw values of each feature in P(Xi|R) tab. NaiveBayes_NR.xlsx: This Excel file includes information as to how probabilities of observed features are calculated given non-recidivism (P(x_ij│N)) in the training data. Each cell is embedded with an Excel function to render appropriate figures. P(Xi|N): This tab contains probabilities of feature attributes among non-recidivated offenders. NIJ_Recoded: This tab contains re-coded NIJ recidivism challenge data following our coding schema described in Table 1. NonRecidivated_Train: This tab contains re-coded features of non-recidivated offenders. Tabs from [Gender] through [Condition_Other]: Each tab contains probabilities of feature attributes given non-recidivism. We use these conditional probabilities to replace the raw values of each feature in P(Xi|N) tab. Training_LnTransformed.xlsx: Figures in each cell are log-transformed ratios of probabilities in NaiveBayes_R.xlsx (P(Xi|R)) to the probabilities in NaiveBayes_NR.xlsx (P(Xi|N)). TestData.xlsx: This Excel file includes the following tabs based on the test data: P(Xi|R), P(Xi|N), NIJ_Recoded, and Test_LnTransformed (log-transformed P(Xi|R)/ P(Xi|N)). Training_LnTransformed.dta: We transform Training_LnTransformed.xlsx to Stata data set. We use Stat/Transfer 13 software package to transfer the file format. StataLog.smcl: This file includes the results of the logistic regression analysis. Both estimated intercept and coefficient estimates in this Stata log correspond to the raw weights and standardized weights in Figure 1. Brier Score_Re-Check.xlsx: This Excel file recalculates Brier scores of Relaxed Naïve Bayes Classifier in Table 3, showing evidence that results displayed in Table 3 are correct. *****Full List***** NaiveBayes_R.xlsx NaiveBayes_NR.xlsx Training_LnTransformed.xlsx TestData.xlsx Training_LnTransformed.dta StataLog.smcl Brier Score_Re-Check.xlsx Data for Weka (Training Set): Bayes_2022_NoID Data for Weka (Test Set): BayesTest_2022_NoID Weka output for machine learning models (Conventional naïve Bayes, AdaBoost, Multilayer Perceptron, Logistic Regression, and Random Forest)

Spreadsheet used to calculate Highway Site characteristics (Drainage area, slope and impervious fraction) for the Stochastic Empirical Loading Dilution Model (SELDM) . The spreadsheet was used in conjunction with the SELDM simulations used in the publication: Stonewall, A.J., and Granato, G.E., 2018, Assessing potential effects of highway and urban runoff on receiving streams in total maximum daily load watersheds in Oregon using the Stochastic Empirical Loading and Dilution Model: U.S. Geological Survey Scientific Investigations Report 2019-5053, 116 p., https://doi.org/10.3133/sir20195053.