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)

Creating a robust employee dataset for data analysis and visualization involves several key fields that capture different aspects of an employee's information. Here's a list of fields you might consider including: Employee ID: A unique identifier for each employee. Name: First name and last name of the employee. Gender: Male, female, non-binary, etc. Date of Birth: Birthdate of the employee. Email Address: Contact email of the employee. Phone Number: Contact number of the employee. Address: Home or work address of the employee. Department: The department the employee belongs to (e.g., HR, Marketing, Engineering, etc.). Job Title: The specific job title of the employee. Manager ID: ID of the employee's manager. Hire Date: Date when the employee was hired. Salary: Employee's salary or compensation. Employment Status: Full-time, part-time, contractor, etc. Employee Type: Regular, temporary, contract, etc. Education Level: Highest level of education attained by the employee. Certifications: Any relevant certifications the employee holds. Skills: Specific skills or expertise possessed by the employee. Performance Ratings: Ratings or evaluations of employee performance. Work Experience: Previous work experience of the employee. Benefits Enrollment: Information on benefits chosen by the employee (e.g., healthcare plan, retirement plan, etc.). Work Location: Physical location where the employee works. Work Hours: Regular working hours or shifts of the employee. Employee Status: Active, on leave, terminated, etc. Emergency Contact: Contact information of the employee's emergency contact person. Employee Satisfaction Survey Responses: Data from employee satisfaction surveys, if applicable.

Code Url: https://github.com/intellisenseCodez/faker-data-generator

The Monty Hall Problem (Three-Door Problem) is a well-known example for a counterintuitive problem in probability theory. This site provides a VBA-based spreadsheet implementation in Excel for an interactive and automatic simulation of the Monty Hall Problem.

In the interactive simulation mode, participants (students) are organized into pairs. Within each pair, one student assumes the role of the host, while the other takes on the role of the contestant. In this simulation mode, the game process and the associated simulation based on the Excel tool provided here are deliberately not fully automated; rather, the students in the role of hosts and contestants should carry out essential steps themselves, interact with each other, and thus become an active part of the simulation. The settings allow for different assumptions regarding, among other things, the random or conscious nature of decisions. This allows a range of different game situations to be mapped - from a purely random game (based solely on Excel’s random number generator) on the one hand to a purely conscious game (based on possibly tactical decisions and expectations of the participants) on the other. The results template can be used to aggregate the results of the interactive simulation of the groups, e.g. in combination with Moodle.

The fully automatic simulation comes in two modes and enables different speed and display options, e.g. successive chart creation during simulation.

Both the interactive and automatic simulation modes allow for different assumptions regarding the probabilities for the car location, the contestant’s first choice and the door opened by the host.

Both the interactive and automatic simulation modes can be carried out in online and face-to-face teaching. The online variant can be conducted using Zoom or any other video conferencing software that enables group rooms.

Carrying out the interactive and automatic simulation provides data in the form of absolute and relative frequencies for wins and losses depending on whether the contestant switches doors or not. The results can then be discussed.

Versions of the simulation tool: - for Windows: Monty Hall Problem Simulation 5.0 (Win) - for Mac: Monty Hall Problem Simulation 5.0 (Mac)

Please note: The simulation tool is optimized for use with Windows. Some options are not available in the Mac version of the simulation tool provided here.

Abstract

The World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California, Berkeley are conducting the Kenya COVID-19 Rapid Response Phone Survey to track the socioeconomic impacts of the COVID-19 pandemic, the recovery from it as well as other shocks to provide timely data to inform policy. This dataset contains information from eight waves of the COVID-19 RRPS, which is part of a panel survey that targets Kenyan nationals and started in May 2020. The same households were interviewed every two months for five survey rounds, in the first year of data collection and every four months thereafter, with interviews conducted using Computer Assisted Telephone Interviewing (CATI) techniques.

The data set contains information from two samples of Kenyan households. The first sample is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot and provided a phone number. The second was obtained through the Random Digit Dialing method, by which active phone numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority are randomly selected. The samples cover urban and rural areas and are designed to be representative of the population of Kenya using cell phones. Waves 1-7 of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge and vaccinations. Wave 8 focused on how households were exposed to shocks, in particular adverse weather shocks and the increase in the price of food and fuel, but also included parts of the previous modules on household background, service access, employment, food security, income loss, and subjective wellbeing.

The data is uploaded in three files. The first is the hh file, which contains household level information. The ‘hhid’, uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the ‘adult_id’. The third file is the child level file, available only for waves 3-7, which contains information for every child in the household. Each child in a household is uniquely identified by the ‘child_id’.

The duration of data collection and sample size for each completed wave was: Wave 1: May 14 to July 7, 2020; 4,061 Kenyan households Wave 2: July 16 to September 18, 2020; 4,492 Kenyan households Wave 3: September 28 to December 2, 2020; 4,979 Kenyan households Wave 4: January 15 to March 25, 2021; 4,892 Kenyan households Wave 5: March 29 to June 13, 2021; 5,854 Kenyan households Wave 6: July 14 to November 3, 2021; 5,765 Kenyan households Wave 7: November 15, 2021, to March 31, 2022; 5,633 Kenyan households Wave 8: May 31 to July 8, 2022: 4,550 Kenyan households

The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/

Geographic coverage

National coverage covering rural and urban areas

Analysis unit

Household, Individual

Sampling procedure

The COVID-19 RRPS with Kenyan households has two samples. The first sample consists of households that were part of the 2015/16 KIHBS CAPI pilot and provided a phone number. The 2015/16 KIHBS CAPI pilot is representative at the national level stratified by county and place of residence (urban and rural areas). At least one valid phone number was obtained for 9,007 households and all of them were included in the COVID-19 RRPS sample. The target respondent was the primary male or female household member from the 2015/16 KIHBS CAPI pilot. The second sample consists of households selected using the Random Digit Dialing method. A list of random mobile phone numbers was created using a random number generator from the 2020 Numbering Frame produced by the Kenya Communications Authority. The initial sampling frame therefore consisted of 92,999,970 randomly ordered phone numbers assigned to three networks: Safaricom, Airtel and Telkom. An introductory text message was sent to 5,000 randomly selected numbers to determine if numbers were in operation. Out of these, 4,075 were found to be active and formed the final sampling frame. There was no stratification and individuals that were called were asked about the households they live in. Until wave 7 sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. In wave 8 only households that had previously participated in the survey were contacted for interview. The “wave” variable represents in which wave the households were interviewed in.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire was administered in English and is provided as a resource in pdf format. Additionally, questionnaires for each wave are also provided in Excel format coded for SCTO. The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/

Review citations used for picking reviews by random (random # generator produced by excel, and number listed on citations picked based on random number generated)

The invoice dataset provided is a mock dataset generated using the Python Faker library. It has been designed to mimic the format of data collected from an online store. The dataset contains various fields, including first name, last name, email, product ID, quantity, amount, invoice date, address, city, and stock code. All of the data in the dataset is randomly generated and does not represent actual individuals or products. The dataset can be used for various purposes, including testing algorithms or models related to invoice management, e-commerce, or customer behavior analysis. The data in this dataset can be used to identify trends, patterns, or anomalies in online shopping behavior, which can help businesses to optimize their online sales strategies.

This dataset is prepared using random number generator function in excel. The data include sample bank branch key business parameters.

3 dim tables include key business parameters, dates and branch names. 3 fact tables include parameter values of branches as on 3 different dates (last FY end,last Qtr end, last day).

The dataset can be loaded into Power BI for analysis and visualizations. 3 fact tables can be appended to one table. 3 types of reports can be generated : Branch-wise Business , Trend analysis , Parameter-wise analysis

Image Credits: (Image by pch.vector on Freepik)

Essential medicines are those medicines that satisfy the primary health care needs of the citizens. Poor quality of essential medicines can have serious impact on public health. Thus, this study is aimed to assess the quality of essential medicines available in public health care facilities of Nepal. A cross sectional descriptive study was carried out in 62 health facilities across 21 districts, representing all seven provinces of Nepal and selected proportionately from all three ecological regions i.e. Terai, Hill and Mountain using lottery method. Health facilities in selected districts were chosen using random number generator. Face to face interview was taken with health facility in charge using structured questionnaire. All storage conditions information was recorded through observation checklists. Temperature and humidity were measured using a digital instrument. Similarly, 20 different generic medicines were collected for quality testing. The obtained data were entered in Epidata version 3.1, cleaned in Microsoft Excel 2007 and analyzed in SPSS version 16.0. Among 62 health facilities, only 13% of health facilities were found to follow the medicine storage guidelines, with temperature and humidity levels exceeding recommended limits. Out of 244 batches of 20 different generics of essential medicines, 37 batches were found to be substandard. These substandard medicines were- Ciprofloxacin hydrochloride eye/ear drop, Iron supplement tablets, Metformin Hydrochloric tablet, Metronidazole Tablets, Paracetamol Oral suspension, Paracetamol tablet and Povidone Iodine solution. The study recommends the urgent need for the Government of Nepal to prioritize ensuring the quality of essential medicines in the country.

Supply chain analytics is a valuable part of data-driven decision-making in various industries such as manufacturing, retail, healthcare, and logistics. It is the process of collecting, analyzing and interpreting data related to the movement of products and services from suppliers to customers.

Context

Typically e-commerce datasets are proprietary and consequently hard to find among publicly available data. However, The UCI Machine Learning Repository has made this dataset containing actual transactions from 2010 and 2011. The dataset is maintained on their site, where it can be found by the title "Online Retail".

Content

"This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers."

Acknowledgements

Per the UCI Machine Learning Repository, this data was made available by Dr Daqing Chen, Director: Public Analytics group. chend '@' lsbu.ac.uk, School of Engineering, London South Bank University, London SE1 0AA, UK.

Image from stocksnap.io.

Inspiration

Analyses for this dataset could include time series, clustering, classification and more.