This data contains functions like: Sum, Average, Max, Min, Sumif, Sumifs, Count, Countblank, Countifs, Counta, Averageif, Averageifs.

📘 Description

The Student Academic Performance Dataset contains detailed academic and lifestyle information of 250 students, created to analyze how various factors — such as study hours, sleep, attendance, stress, and social media usage — influence their overall academic outcomes and GPA.

This dataset is synthetic but realistic, carefully generated to reflect believable academic patterns and relationships. It’s perfect for learning data analysis, statistics, and visualization using Excel, Python, or R.

The data includes 12 attributes, primarily numerical, ensuring that it’s suitable for a wide range of analytical tasks — from basic descriptive statistics (mean, median, SD) to correlation and regression analysis.

📊 Key Features

🧮 250 rows and 12 columns

💡 Mostly numerical — great for Excel-based statistical functions

🔍 No missing values — ready for direct use

📈 Balanced and realistic — ideal for clear visualizations and trend analysis

🎯 Suitable for:

Descriptive statistics

Correlation & regression

Data visualization projects

Dashboard creation (Excel, Tableau, Power BI)

💡 Possible Insights to Explore

How do study hours impact GPA?

Is there a relationship between stress levels and performance?

Does social media usage reduce study efficiency?

Do students with higher attendance achieve better grades?

⚙️ Data Generation Details

Each record represents a unique student.

GPA is calculated using a weighted formula based on midterm and final scores.

Relationships are designed to be realistic — for example:

Higher study hours → higher scores and GPA

Higher stress → slightly lower sleep hours

Excessive social media time → reduced academic performance

⚠️ Disclaimer

This dataset is synthetically generated using statistical modeling techniques and does not contain any real student data. It is intended purely for educational, analytical, and research purposes.

"The Statistical Abstract of the United States, published since 1878, is the standard summary of statistics on the social, political, and economic organization of the United States. It is designed to serve as a convenient volume for statistical reference and as a guide to other statistical publications and sources. The latter function is served by the introductory text to each section, the source note appearing below each table, and Appendix I, which comprises the Guide to Sources of Statisti cs, the Guide to State Statistical Abstracts, and the Guide to Foreign Statistical Abstracts. The Statistical Abstract sections and tables are compiled into one Adobe PDF named StatAbstract2009.pdf. This PDF is bookmarked by section and by table and can be searched using the Acrobat Search feature. The Statistical Abstract on CD-ROM is best viewed using Adobe Acrobat 5, or any subsequent version of Acrobat or Acrobat Reader. The Statistical Abstract tables and the metropolitan areas tables from Appendix II are available as Excel(.xls or .xlw) spreadsheets. In most cases, these spreadsheet files offer the user direct access to more data than are shown either in the publication or Adobe Acrobat. These files usually contain more years of data, more geographic areas, and/or more categories of subjects than those shown in the Acrobat version. The extensive selection of statistics is provided for the United States, with selected data for regions, divisions, states, metropolitan areas, cities, and foreign countries from reports and records of government and private agencies. Software on the disc can be used to perform full-text searches, view official statistics, open tables as Lotus worksheets or Excel workbooks, and link directly to source agencies and organizations for supporting information. Except as indicated, figures are for the United States as presently constituted. Although emphasis in the Statistical Abstract is primarily given to national data, many tables present data for regions and individual states and a smaller number for metropolitan areas and cities.Statistics for the Commonwealth of Puerto Rico and for island areas of the United States are included in many state tables and are supplemented by information in Section 29. Additional information for states, cities, counties, metropolitan areas, and other small units, as well as more historical data are available in various supplements to the Abstract. Statistics in this edition are generally for the most recent year or period available by summer 2006. Each year over 1,400 tables and charts are reviewed and evaluated; new tables and charts of current interest are added, continuing series are updated, and less timely data are condensed or eliminated. Text notes and appendices are revised as appropriate. This year we have introduced 72 new tables covering a wide range of subject areas. These cover a variety of topics including: learning disability for children, people impacted by the hurricanes in the Gulf Coast area, employees with alternative work arrangements, adult computer and Internet users by selected characteristics, North America cruise industry, women- and minority-owned businesses, and the percentage of the adult population considered to be obese. Some of the annually surveyed topics are population; vital statistics; health and nutrition; education; law enforcement, courts and prison; geography and environment; elections; state and local government; federal government finances and employment; national defense and veterans affairs; social insurance and human services; labor force, employment, and earnings; income, expenditures, and wealth; prices; business enterprise; science and technology; agriculture; natural resources; energy; construction and housing; manufactures; domestic trade and services; transportation; information and communication; banking, finance, and insurance; arts, entertainment, and recreation; accommodation, food services, and other services; foreign commerce and aid; outlying areas; and comparative international statistics." Note to Users: This CD is part of a collection located in the Data Archive of the Odum Institute for Research in Social Science, at the University of North Carolina at Chapel Hill. The collection is located in Room 10, Manning Hall. Users may check the CDs out subscribing to the honor system. Items can be checked out for a period of two weeks. Loan forms are located adjacent to the collection.

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...

Learning where to find nutrients while at the same time avoiding toxic food is essential for survival of any animal. Using Drosophila melanogaster larvae as a study case, we investigate the role of gustatory sensory neurons expressing IR76b for associative learning of amino acids, the building blocks of proteins. We found surprising complexity in the neuronal underpinnings of sensing amino acids, and a functional division of sensory neurons. We found that the IR76b receptor is dispensable for amino acid learning, whereas the neurons expressing IR76b are specifically required for the rewarding but not the punishing effect of amino acids. This unexpected dissociation in neuronal processing of amino acids for different behavioural functions provides a study case for functional divisions of labour in gustatory systems.

Microsoft Excel sheet with QC data from [69] used in Figs 5 and C in S1 File.

An Excel sheet representing the coded data of the study population.

Excel file containing compiled primary experimental data subjected to statistical analyses.

Excel spreadsheet containing the numerical data and details of statistical analysis for Figs 1D, 1E, 1F, 1G, 2C, 2D, 2F, 2G, 2H, 3B–3D, 3F, 3G, 4B, 4C, 4D, 4E, 4G, 4H, 5C, 5D, 5E, 5F, 6C, 6D–6F, 7A, 7C, 7D, 7E, 7F, 7G, 7H, 7I, 7J, 7K, S1C, S1D, S1F, S1G, S2B, S2C, S2G, S2H, S2I, S2J, S2K, S3A, S3C, S3D, S3F, S3G, S3I, S4B, S5C, S5D, S5E, S5F, S5G and S5H.

The data are organized into separate sheets corresponding to the following figure panels: 1C, 1G, 2B, 2D, 2F, 2H, 4C, 4D, 4F, 5B, 5C, S3B, S5C, S5E, S7B, S8B, S10B, S12A, S12B, and S21B. (XLSX)

Excel spreadsheet containing, in separate sheets, the underlying numerical data and statistical analysis for Figs 1D, 2B, 2C, 3A, 3B, 5B, 6

Excel spreadsheet containing, in separate sheets, the underlying numerical data and statistical analysis for Fig panels 3A, 4B-4D, 4F-4K, 5A, 5D-5E, 5H-5J, 6A, 6D, 6F-6H, 6J, 6M, 6O-6Q, 7F, S2B-S2D, S2F-S2H, S3B-S3D, S3F-S3H, S4C-S4G, S5A-S5B, S5G-S5H, and S5J-S5L.

Microsoft Excel workbook provided source data matrices and associated statistical computations used to generate the graphical representations in Figures.

The General Practice Workforce series of Official Statistics presents a snapshot of the primary care general practice workforce. A snapshot statistic relates to the situation at a specific date, which for these workforce statistics is now the last calendar day each month. This monthly snapshot reflects the general practice workforce at 31 December 2022. These statistics present full-time equivalent (FTE) and headcount figures by four staff groups, (GPs, Nurses, Direct Patient Care (DPC) and administrative staff), with breakdowns of individual job roles within these high-level groups. For the purposes of NHS workforce statistics, we define full-time working to be 37.5 hours per week. Full-time equivalent is a standardised measure of the workload of an employed person. Using FTE, we can convert part-time and additional working hours into an equivalent number of full-time staff. For example, an individual working 37.5 hours would be classed as 1.0 FTE while a colleague working 30 hours would be 0.8 FTE. The term “headcount” relates to distinct individuals, and as the same person may hold more than one role, care should be taken when interpreting headcount figures. Please refer to the Using this Publication section for information and guidance about the contents of this publication and how it can and cannot be used. England-level time series figures for all job roles are available in the Excel bulletin tables back to September 2015 when this series of Official Statistics began. The Excel file also includes Sub-ICB Location-level FTE and headcount breakdowns for the current reporting period. CSVs containing practice-level summaries and Sub-ICB Location-level counts of individuals are also available. Please refer to the Publication content, analysis, and release schedule in the Using this publication section for more details of what’s available. We are continually working to improve our publications to ensure their contents are as useful and relevant as possible for our users. We welcome feedback from all users to PrimaryCareWorkforce@nhs.net.

Excel spreadsheet containing the numerical values used for graphs and statistical analysis for figure panels 1D, 1E, 1F, 3A, 3B, 3D, 3E, 4A, 4B, 5B, 5C, 6B, 6C, 8C, 8D, 8E, S1E, S3A, S3B, S3C, S3D and S3E.