With a step-by-step approach, learn to prepare Excel files, data worksheets, and individual data columns for data analysis; practice conditional formatting and creating pivot tables/charts; go over basic principles of Research Data Management as they might apply to an Excel project. Avec une approche étape par étape, apprenez à préparer pour l’analyse des données des fichiers Excel, des feuilles de calcul de données et des colonnes de données individuelles; pratiquez la mise en forme conditionnelle et la création de tableaux croisés dynamiques ou de graphiques; passez en revue les principes de base de la gestion des données de recherche tels qu’ils pourraient s’appliquer à un projet Excel.

This project includes a series of Excel files demonstrating key Excel functionalities, including:

• Conditional Formatting for data visualization.
• Pivot Tables for summarizing and analyzing data.
• Excel Charts for visual representation of key insights.
• Use of Formulas and XLOOKUP to automate calculations and data lookup.
• Data Cleaning techniques to prepare the dataset for analysis.
• Additionally, the project includes a final Excel file with bike sales data and an interactive dashboard.

You can download the original Excel file with all formatting here: https://www.kaggle.com/datasets/carinacruz/excel-project

This dataset is about book series. It has 1 row and is filtered where the books is Excel at problem solving. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Linear trend analysis of time series is standard procedure in many scientific disciplines. If the number of data is large, a trend may be statistically significant even if data are scattered far from the trend line. This study introduces and tests a quality criterion for time trends referred to as statistical meaningfulness, which is a stricter quality criterion for trends than high statistical significance. The time series is divided into intervals and interval mean values are calculated. Thereafter, r2 and p values are calculated from regressions concerning time and interval mean values. If r2≥0.65 at p≤0.05 in any of these regressions, then the trend is regarded as statistically meaningful. Out of ten investigated time series from different scientific disciplines, five displayed statistically meaningful trends. A Microsoft Excel application (add-in) was developed which can perform statistical meaningfulness tests and which may increase the operationality of the test. The presented method for distinguishing statistically meaningful trends should be reasonably uncomplicated for researchers with basic statistics skills and may thus be useful for determining which trends are worth analysing further, for instance with respect to causal factors. The method can also be used for determining which segments of a time trend may be particularly worthwhile to focus on.

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

Dataset

This dataset was created by Pinky Verma

Contents

This database contains one hour oxygen saturation (SpO2) measurements of 36 patients, used for the analysis of oxygen saturation variability. The Ascii (.txt) files contain the raw data of SpO2 recorded with a sampling rate of 1/s. The attached excel file "Participant characteristics" contains anonymised participant information. Detailed analysis of this data is published on Frontiers Physiology.

A set of Excel© spreadsheets, TNTSBI 1.0c.xlsx, has been developed to display the physical properties of blast waves produced by the explosion of a hemispherical TNT charge resting on a plane rigid surface. The spreadsheets have been developed using an extensive database of experimentally derived measurements of the hydrostatic pressure, density and particle velocity from a large number of TNT explosions ranging in size from 4 kg to 500 t, as described by Dewey and McMillin (1987, 1989). The results of these analyses were made available via an interface known as AirBlast (Dewey and McMillin, 1990). The AirBlast interface was licensed to more than 30 international agencies, but is no longer compatible with modern operating systems. However, the original databases of experimental measurements remain accessible and are being incorporated into a series of Excel© files, the first of which was TNTFFI 1.1c (Dewey, 2021). It is anticipated that files providing the blast wave properties for height-of-burst explosions, TNTHOBI, will also be developed. Excel© has been chosen as the interface platform because it is a component of most computer systems and has proved to be compatible with all operating systems as they have been upgraded over several decades. Some of the functions used in the interface may not be included with all versions of Excel©, but if missing, they can be downloaded from Microsoft. TNTSBI 1.0c consists of four spreadsheets. The first, Input Values, allows the user to input: the charge mass in kilograms; the TNT equivalence as a fraction, e.g. 0.8, if an explosive other than TNT is being used; the ambient atmospheric pressure in kiloPascals, and the ambient temperature in Celsius. The input values are used to calculate the Hopkinson (1915) and Sachs (1944), (Dewey 2000, 2016) scaling factors for radius and time. These scaling factors are used in the subsequent spreadsheets to determine the physical properties of the blast wave as functions of radial distance and time without further application by the user. The second spreadsheet, Peak Values, displays the physical properties immediately behind the primary shock at a series of radial distances from the centre of the blast wave defined by the input values. The range of distances covers those for which reliable experimental measurements of the physical properties are available. The third spreadsheet, Time Histories, displays the physical properties of the blast wave at a series of times after the arrival of the primary shock at the radial distance input by the user. The integrals of the physical properties during their positive phases are also displayed. The fourth and final spreadsheet, Wave Profiles, displays the physical properties of the blast wave at a series of radial distances behind the primary shock at the time after detonation input by the user. The integrals of the physical properties during their positive phases are also displayed. The spreadsheets can be downloaded as TNTSBI 1.0c.xlsx and a Users’ Guide, TNTSBI Users’ Guide.pdf, which describes the spreadsheets in more detail and how they may be used.

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

Distribution of doses of a volatile organic compound from inhalation of one consumer product, other near -field sources, far-field sources, and aggregate (total) exposure. In this instance, far-field scenarios account for several orders of magnitude of less of the predicted dose compared to near-field scenarios. This dataset is associated with the following publication: Vallero, D. Air Pollution Monitoring Changes to Accompany the Transition from a Control to a Systems Focus. Sustainability. MDPI AG, Basel, SWITZERLAND, 8(12): 1216, (2016).

A range of quarterly Excel spreadsheets and SuperTABLE datacubes. The spreadsheets contain broad level data covering all the major items of the Labour Force Survey in time series format, including …Show full descriptionA range of quarterly Excel spreadsheets and SuperTABLE datacubes. The spreadsheets contain broad level data covering all the major items of the Labour Force Survey in time series format, including seasonally adjusted and trend estimates. The datacubes contain more detailed and cross classified original data than the spreadsheets.

The Nation is the series of basic data from the 1991 Census, providing national coverage. This series covers characteristics of the population, including demographic, social, cultural, labour force and income variables as well as details on dwellings, households and families. Generally the data are represented for Canada, provinces, territories and census metropolitan areas. Some tables include comparisons with data from earlier censuses. The aggregate data tables are presented in Beyond 20/20 Format (.ivt).

The series consists of a list of files registered on the computer-based Records and Correspondence Management System (RCMS), under Registry 01 Corporate Management Division. It was created by exporting file data from the RCMS system into a Microsoft Excel spreadsheet. It is an artificial series, created by the Department of Justice at the request of PROV, to provide access to VPRS 12607 General Correspondence Files, Registry 01 Corporate Management Division.

The list captured the file number, key-term classification, file title, and certain additional information for each file.

Organisation of the Data:

The data is organised into 13 columns, or fields, presumably corresponding to discrete fields within the RCMS database.

The columns, from left to right, are as follows:

1. FILE.YEAR - The year the file was raised.

2. REGISTRY - The number of the registry in which the file has been registered on the RCMS system. The files referred to by this series were registered under Registry 01 Corporate Management Division.

3. FILE SEQUENCE - The sequential number allocated to each file as it is raised. Numbers start again from one each year.

4. FILE PART - The part number of the file.

The FILE.YEAR, REGISTRY, FILE SEQUENCE, and FILE PART fields, taken together, provide the file number.

5. KEY TERM - In theory, this is term used to describe the principle subject area of the file.

6. DESCRIPTOR.1, DESCRIPTOR.2 and DESCRIPTOR.3 (Columns 6 to 8) - In theory, these are narrower terms used to break the general subject area into smaller, more specific areas.

7. KWOC.1, KWOC.2, KWOC.3, and KWOC.4 (Key Word Out of Context) (Columns 9 to 12) - Provide for free text description of the file.

The KEY-TERM, DESCRIPTOR, and KWOC fields, taken together, provide the file title.

In practice, many different terms have been used in the key-term and descriptor fields. There appears to have been little control over the creation of new terms and the way in which the terms are used.

8. ADD.FILE.INFO (Additional File Information) - This field contains useful information about previous and subsequent files, related files, file closure, and so forth.

Identifying Top-numbered Files:

This series also records the original file numbers for files that have been top-numbered into VPRS 12607 from other correspondence registries that operated in the Law Department in the 1980's. The details are as follows:

Files top-numbered from the Central Correspondence Registry (VPRS 266 Inward Registered Correspondence 1857-1986) - the original file number is recorded in the field "ADD.FILE.INFO".

Files top-numbered from the Courts Management Division Registry (VPRS 12705 General Correspondence Files, Courts Management Division) - the original file number is recorded in the fields "KWOC 3" and "KWOC 4".

Files top-numbered from the Buildings and Property Registry - the original file number is recorded in the field "KWOC 4".

Files top-numbered from the Human Resource Management Registry - the original file number is recorded in the field "KWOC 4".

Files top-numbered from RCMS Registry 02 Courts and Tribunals Division - the original file number is recorded in the fields "KWOC 3" and "KWOC 4".

Researchers should not discount the possibility that file numbers may be recorded in fields other than those specified above.

Data organization for the figures in the document: Figure 3A LineOutWithSun_SSAzi_135to225_green_Correct_ROI5_INFO.xls Figure 3b LineOutWithSun_SSAzi_m45to45_green_Correct_ROI5_INFO.xls Figure 4 fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Correct_ROI5_INFO.xls fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Sim_Correct_ROI5_INFO.xls Figure 5a LineOut_Camera_Elevation_SqAzi_m180to0_green_Sim_Correct_ROI5_INFO.xls LineOut_Camera_Elevation_SqAzi_m180to0_green_Correct_ROI5_INFO.xls Figure 5b LineOut_Camera_Elevation_SqAzi_0to180_green_Correct_ROI5_INFO.xls LineOut_Camera_Elevation_SqAzi_0to180_green_Sim_Correct_ROI5_INFO.xls Figure 6a LineOutColor_SqAzi_m180to0_CP_20to50_Correct_ROI5_INFO.xls Figure 6b LineOutROI_SqAzi_m180to0_CP_20to50_green_Correct_INFO.xls Figure 7 fulllinear_inDic_SqAzi_m180to0_CP_20to50_green_Correct_ROI5_INFO.xls LineOut_MeshAoPDif_Camera_Elevation_SqAzi_0to180_green_Correct_ROI5_INFO.xls LineOut_MeshAoPDif_Camera_Elevation_SqAzi_m180to0_green_Correct_ROI5_INFO.xls

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

This dataset in an Excel file presents our data-base on world trade from 1800 to 1938. We have collected or estimated series of imports and exports, at current and constant (1913) prices and at current and at constant (1913) borders, for 149 polities. After a short review of the available series, we describe the methods for the construction of the data-base. We then deal with the criteria for the inclusion of polities, the representativeness of our series, the main types of sources, the procedures of deflation and, when necessary, of adjustments to 1913 borders. We discuss the details of the estimation of our polity series in Appendix B. Following Feinstein and Thomas (2001), we assess the reliability of our polity estimates. In the last two sections we present our trade series at current and 1913 borders and compare them with other available series. This dataset is related to the working paper "World trade, 1800-1938 : a new data-set" by Giovanni Federico and Antonio Tena Junguito, available on: http://hdl.handle.net/10016/22222.

Interprovincial Trade Flows (15F0002XDB) The interprovincial and international trade flows for goods and services by province and territory are available at the S-level of commodity aggregation in EXCEL files. National Input - Output Tables (15F0041XDB) The Input-Output accounting system consists of three tables. The input tables (USE tables) detail the commodities that are consumed by various industries. Output tables (MAKE tables) detail the commodities that are prod uced by various industries. Final demand tables detail the commodities bought by many categories of buyers (consumers, industries and government) for both consumption and investment purposes. These tables allow users to track intersectional exchanges of goods and services between industries and final demand categories such as personal expenditures, capital expenditures and public sector expenditures. There are four levels of detail: the "W" or Worksheet level with 303 industries, 727 commodities and 170 final demand categories, the "L" or Link level (the most detailed level that allows the construction of consistent time series of annual data from 1961 to 2002) with 117 industries, 469 commodities and 123 final demand categories, the "M" or Medium level with 62 industries, 111 commodities and 39 final demand categories, and the "S" or Small level with 25 industries, 59 commodities a nd 16 final demand categories. In 2009, several changes were made to the accounting system: there is a new level "D" that is the Detailed level, there are no "M" or "W" level tables, and there are two "L" level tables representing 1961 and 1997 aggregations. Provincial Input-Output Tables (15F0042XDB) The provincial input-output tables are constructed every year. The tables are available at the "S" level only. National and Provincial Multipliers (15F0046XDB) These are a series of Input-Output multipliers and ratios that allow users to quickly estimate the direct, indirect and total impacts of increases in industrial output or increases in an industry's labour force. These are the GDP, labour income, employment and gross output multipliers and ratios. Capital income multipliers and ratios can be calculated by subtracting the labour income figures from the GDP figures. National Symmetric Input-Output Tables - Aggregation Level S (15-207-XC B) The Industry Accounts Division of Statistics Canada publishes annual supply and use input-output (I-O) tables. While these rectangular, industry by commodity closely reflect actual economic transactions, certain analytical and modeling purposes, however, require symmetric industry-by-industry I-O tables. The symmetric industry by industry table shows the inter-industry transactions, that is, all purchases of an industry from all other industries including expenditures on imports and i nventory withdrawals as well as all expenditures on primary inputs. Similarly, the symmetric final demand table shows all purchases by a final demand category from all other industries, including expenditures on imports and inventory withdrawals as well as all expenditures on indirect taxes. National Symmetric Input-Output Tables - Aggregation Level L (15-208-XCB). The Industry Accounts Division of Statistics Canada publishes annual symmetric industry-by-industry I-O tables at the L level. The symmetric industry by industry table shows the inter-industry transactions, that is, all purchases of an industry from all other industries including expenditures on imports and inventory withdrawals as well as all expenditures on primary inputs. Similarly, the symmetric final demand table shows all purchases by a final demand category from all other industries, including expenditures on imports and inventory withdrawals as well as all expenditures on indirect taxes. Provincial GDP by Industry and Sector, at Basic Prices (15-209-XCB). This product presents estimates of Gross Domestic Product (GDP) by industry, in current dollars, evaluated at basic price for all provinces and territories. These estimates are derived from the provincial Input-Output tables. GDP measures the unduplicated value of production. The GDP by industry estimates are derived using a "value added" approach, that is, the value that a producer adds to their intermediate inputs before generating their own output. This allows not only for the computation of total economic production but also the industrial composition and origin of the economic production. When evaluated at basic prices, an industry's GDP is the sum of its factor incomes (wages and salaries, supplementary labour income, mixed income and other operating surplus) plus taxes less subsidies on production (labour and capital). Provincial Gross Output by Industry and Sector (15-210-XCB). This product presents estimates of gross output by industry, in current dollars, evaluated at modified basic price for all provinces and territories. These estimates are derived from the provincial Input-Output tables. Gross output...

This dataset is composed of 176 Excel files, downloaded via the Google Trends tool between June 11 and 15, 2025. The files include time series data on the relative frequency (in percentages) of Google searches conducted in European Union member states on four topics: corruption, immigration, security, and transexuality. They also include data related to right-wing, center-right, and center-left (used as a control group) political parties in each country. Each file contains one column with the date (on a weekly basis) and another with the series value (a percentage normalized by Google Trends). The time span covered in each file is five years.

About this dataset

Context

The dataset illustrates the median household income in Excel, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Excel increased by $13,784 (25.63%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 7 years and declined for 4 years.

https://i.neilsberg.com/ch/excel-al-median-household-income-trend.jpeg" alt="Excel, AL median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Excel median household income. You can refer the same here

The dataset included with this article contains three files describing and defining the sample and variables for VAT impact, and Excel file 1 consists of all raw and filtered data for the variables for the panel data sample. Excel file 2 depicts time-series and cross-sectional data for nonfinancial firms listed on the Saudi market for the second and third quarters of 2019 and the third and fourth quarters of 2020. Excel file 3 presents the raw material of variables used in measuring the company's profitability of the panel data sample