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.

The dataset for the article "The current utilization status of wearable devices in clinical research".Analyses were performed by utilizing the JMP Pro 16.10, Microsoft Excel for Mac version 16 (Microsoft).The file extension "jrp" is a file of the statistical analysis software JMP, which contains both the analysis code and the data set.In case JMP is not available, a "csv" file as a data set and JMP script, the analysis code, are prepared in "rtf" format.The "xlsx" file is a Microsoft Excel file that contains the data set and the data plotted or tabulated using Microsoft Excel functions.Supplementary Figure 1. NCT number duplication frequencyIncludes Excel file used to create the figure (Supplemental Figure 1).・Sfig1_NCT number duplication frequency.xlsxSupplementary Figure 2-5　Simple and annual time series aggregationIncludes Excel file, JMP repo file, csv dataset of JMP repo file and JMP scripts used to create the figure (Supplementary Figures 2-5).・Sfig2-5 Annual time series aggregation.xlsx・Sfig2 Study Type.jrp・Sfig4device type.jrp・Sfig3 Interventions Type.jrp・Sfig5Conditions type.jrp・Sfig2, 3 ,5_database.csv・Sfig2_JMP script_Study type.rtf・Sfig3_JMP script Interventions type.rtf・Sfig5_JMP script Conditions type.rtf・Sfig4_dataset.csv・Sfig4_JMP script_device type.rtfSupplementary Figures 6-11 Mosaic diagram of intervention by conditionSupplementary tables 4-9 Analysis of contingency table for intervention by condition JMP repot files used to create the figures(Supplementary Figures 6-11 ) and tables(Supplementary Tablea 4-9) , including the csv dataset of JMP repot files and JMP scripts.・Sfig6-11 Stable4-9 Intervention devicetype_conditions.jrp・Sfig6-11_Stable4-9_dataset.csv・Sfig6-11_Stable4-9_JMP script.rtfSupplementary Figure 12. Distribution of enrollmentIncludes Excel file, JMP repo file, csv dataset of JMP repo file and JMP scripts used to create the figure (Supplementary Figures 12).・Sfig12_Distribution of enrollment.jrp・Sfig12_Distribution of enrollment.csv・Sfig12_JMP script.rtf

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.

This dataset contains various sample data files for practicing Excel functions and features, including data related to sales orders, athletes, food nutrients, insurance policies, and workplace safety.

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 <-...

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)."

Yearly citation counts for the publication titled "A non-linear regression analysis program for describing electrophysiological data with multiple functions using Microsoft Excel".

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.

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

In Julie Marcoux's Excel Workshop, she demonstrates some great tricks that will make it easier to work with DLI data, including creating macros, useful excel functions, and tools.

Graphical analysis of the toxicity testing and the potency of millet extracts in reversing the tachycardic and bradycardic conditions. The results show significant changes and it is effectively supported by the statistical data (correlation analysis) performed using the basic functions of Microsoft Excel.

Context

This notebook serves to showcase my problem solving ability, knowledge of the data analysis process, proficiency with Excel and its various tools and functions, as well as my strategic mindset and statistical prowess. This project consist of an auditing prompt provided by Hive Data, a raw Excel data set, a cleaned and audited version of the raw Excel data set, and my description of my thought process and knowledge used during completion of the project. The prompt can be found below:

Hive Data Audit Prompt

The raw data that accompanies the prompt can be found below:

Hive Annotation Job Results - Raw Data

^ These are the tools I was given to complete my task. The rest of the work is entirely my own.

To summarize broadly, my task was to audit the dataset and summarize my process and results. Specifically, I was to create a method for identifying which "jobs" - explained in the prompt above - needed to be rerun based on a set of "background facts," or criteria. The description of my extensive thought process and results can be found below in the Content section.

Content

Brendan Kelley April 23, 2021

Hive Data Audit Prompt Results

This paper explains the auditing process of the “Hive Annotation Job Results” data. It includes the preparation, analysis, visualization, and summary of the data. It is accompanied by the results of the audit in the excel file “Hive Annotation Job Results – Audited”.

Observation

The “Hive Annotation Job Results” data comes in the form of a single excel sheet. It contains 7 columns and 5,001 rows, including column headers. The data includes “file”, “object id”, and the pseudonym for five questions that each client was instructed to answer about their respective table: “tabular”, “semantic”, “definition list”, “header row”, and “header column”. The “file” column includes non-unique (that is, there are multiple instances of the same value in the column) numbers separated by a dash. The “object id” column includes non-unique numbers ranging from 5 to 487539. The columns containing the answers to the five questions include Boolean values - TRUE or FALSE – which depend upon the yes/no worker judgement.

Use of the COUNTIF() function reveals that there are no values other than TRUE or FALSE in any of the five question columns. The VLOOKUP() function reveals that the data does not include any missing values in any of the cells.

Assumptions

Based on the clean state of the data and the guidelines of the Hive Data Audit Prompt, the assumption is that duplicate values in the “file” column are acceptable and should not be removed. Similarly, duplicated values in the “object id” column are acceptable and should not be removed. The data is therefore clean and is ready for analysis/auditing.

Preparation

The purpose of the audit is to analyze the accuracy of the yes/no worker judgement of each question according to the guidelines of the background facts. The background facts are as follows:

• A table that is a definition list should automatically be tabular and also semantic • Semantic tables should automatically be tabular • If a table is NOT tabular, then it is definitely not semantic nor a definition list • A tabular table that has a header row OR header column should definitely be semantic

These background facts serve as instructions for how the answers to the five questions should interact with one another. These facts can be re-written to establish criteria for each question:

For tabular column: - If the table is a definition list, it is also tabular - If the table is semantic, it is also tabular

For semantic column: - If the table is a definition list, it is also semantic - If the table is not tabular, it is not semantic - If the table is tabular and has either a header row or a header column...

This study provides tools to test the reliability of selected statistical software: Excel, Gauss, Stata, and SST. Functions covered include non-linear optimization algorithms, distributions, and pseudo-random number generators.

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)

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)

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.

The data file entitled “Emergy analysis of maize production in Ghana” is based on an empirical study to assess the resource as well as energy use efficiency of maize production systems using the Emergy-Data Envelopment Analysis approach, which was developed within the context of the BiomassWeb Project. The study area was Bolgatanga and Bongo Districts, Ghana, sub-Saharan Africa. The approach was developed by coupling Emergy Analysis and Data Envelopment Analysis methods into a framework, and integrating the concept of eco-efficiency into the framework to assess the resource as well as energy use efficiency and sustainability of agroecosystems as a whole. In this data file, the Emergy Analysis method is applied to achieve enviromental and economic accounting of maize production systems in Ghana. The Agricultural Production Systems sIMulator (APSIM) was used to model five maize-based production scenarios as follows: 1. Extensive rainfed maize system if the external input is 0 kg/ha/yr urea, with/ without manure (Extensive0). 2. Extensive rainfed maize system if the external input is 12 kg/ha/yr NPK, with/ without manure (Extensive12). 3. Rainfed maize-legume (cowpea - Vigna unguiculata, soybean - Glycine max, or groundnut - Arachis hypogaea) intercropping system if the external input is 20 kg/ha/yr urea, with/ without manure (Intercrop20). 4. Intensive maize system if the external input is 50 kg/ha/yr urea, including supplemental irrigation (Intensive50). 5. Intensive maize system if the external input is 100 kg/ha/yr urea, including supplemental irrigation (Intensive100). The five scenarios were compared on the basis of the evaluation that was achieved using the Emergy Analysis to account for resource as well as energy use efficiency and sustainability. The data were processed using mathemathical functions in Microsoft Excel. The data file is organized in seven sheet tabs, and they are linked. Comments have been added to make the content self-explanatory. Where secondary data have been used, the sources have been cited. This data file was authored by Mwambo, Francis Molua.

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.