This excel file will do a statistical tests of whether two ROC curves are different from each other based on the Area Under the Curve. You'll need the coefficient from the presented table in the following article to enter the correct AUC value for the comparison: Hanley JA, McNeil BJ (1983) A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 148:839-843.

Excel files containing the data for the paper titled: "Diffuse blue vs. structural silver—comparing alternative strategies for pelagic background matching between two coral reef fishes." See Data for creole wrasse vs bar jack.docx for more details

National Rivers and Streams Assessment (NRSA) physical habitat and ancillary GIS data from NRSA 2008-2009, and 2013-2014. Portions of this dataset are inaccessible because: Data is available in the attached excel file. They can be accessed through the following means: Data is available in the attached excel file. Format: Data is available in the attached excel file. This dataset is associated with the following publication: Saraiva, S.O., I. Rutherfurd, P. Kaufmann, C.G. Leal, D.R. Macedo, and P.S. Pompeu. WOOD STOCK IN NEOTROPICAL STREAMS: QUANTIFYING AND COMPARING INSTREAM WOOD AMONG BIOMES AND REGIONS. PLOS ONE. Public Library of Science, San Francisco, CA, USA, 0275464, (2022).

This Excel template is an example taken from the GEO web site (http://www.ncbi.nlm.nih.gov/geo/info/spreadsheet.html#GAtemplates) which has been modified to conform to the SysMO JERM (Just Enough Results Model). Using templates helps with searching and comparing data as well as making it easier to submit data to public repositories for publications.

Spreadsheets targeted at the analysis of GHS safety fingerprints.AbstractOver a 20-year period, the UN developed the Globally Harmonized System (GHS) to address international variation in chemical safety information standards. By 2014, the GHS became widely accepted internationally and has become the cornerstone of OSHA’s Hazard Communication Standard. Despite this progress, today we observe that there are inconsistent results when different sources apply the GHS to specific chemicals, in terms of the GHS pictograms, hazard statements, precautionary statements, and signal words assigned to those chemicals. In order to assess the magnitude of this problem, this research uses an extension of the “chemical fingerprints” used in 2D chemical structure similarity analysis to GHS classifications. By generating a chemical safety fingerprint, the consistency of the GHS information for specific chemicals can be assessed. The problem is the sources for GHS information can differ. For example, the SDS for sodium hydroxide pellets found on Fisher Scientific’s website displays two pictograms, while the GHS information for sodium hydroxide pellets on Sigma Aldrich’s website has only one pictogram. A chemical information tool, which identifies such discrepancies within a specific chemical inventory, can assist in maintaining the quality of the safety information needed to support safe work in the laboratory. The tools for this analysis will be scaled to the size of a moderate large research lab or small chemistry department as a whole (between 1000 and 3000 chemical entities) so that labelling expectations within these universes can be established as consistently as possible.Most chemists are familiar with programs such as excel and google sheets which are spreadsheet programs that are used by many chemists daily. Though a monadal programming approach with these tools, the analysis of GHS information can be made possible for non-programmers. This monadal approach employs single spreadsheet functions to analyze the data collected rather than long programs, which can be difficult to debug and maintain. Another advantage of this approach is that the single monadal functions can be mixed and matched to meet new goals as information needs about the chemical inventory evolve over time. These monadal functions will be used to converts GHS information into binary strings of data called “bitstrings”. This approach is also used when comparing chemical structures. The binary approach make data analysis more manageable, as GHS information comes in a variety of formats such as pictures or alphanumeric strings which are difficult to compare on their face. Bitstrings generated using the GHS information can be compared using an operator such as the tanimoto coefficent to yield values from 0 for strings that have no similarity to 1 for strings that are the same. Once a particular set of information is analyzed the hope is the same techniques could be extended to more information. For example, if GHS hazard statements are analyzed through a spreadsheet approach the same techniques with minor modifications could be used to tackle more GHS information such as pictograms.Intellectual Merit. This research indicates that the use of the cheminformatic technique of structural fingerprints can be used to create safety fingerprints. Structural fingerprints are binary bit strings that are obtained from the non-numeric entity of 2D structure. This structural fingerprint allows comparison of 2D structure through the use of the tanimoto coefficient. The use of this structural fingerprint can be extended to safety fingerprints, which can be created by converting a non-numeric entity such as GHS information into a binary bit string and comparing data through the use of the tanimoto coefficient.Broader Impact. Extension of this research can be applied to many aspects of GHS information. This research focused on comparing GHS hazard statements, but could be further applied to other bits of GHS information such as pictograms and GHS precautionary statements. Another facet of this research is allowing the chemist who uses the data to be able to compare large dataset using spreadsheet programs such as excel and not need a large programming background. Development of this technique will also benefit the Chemical Health and Safety community and Chemical Information communities by better defining the quality of GHS information available and providing a scalable and transferable tool to manipulate this information to meet a variety of other organizational needs.

Dataset for "Comparing Transaction Logs to ILL requests to Determine the Persistence of Library Patrons In Obtaining Materials" article. Excel file contains all data in four worksheets Zip file contains four csv files, one for each worksheet: - Comparing Transaction Logs to ILL - 2016 ILL Raw ...Data.csv - Comparing Transaction Logs to ILL - 2015 ILL Raw Data.csv - Comparing Transaction Logs to ILL - 2016 Zero Search Raw Data.csv - Comparing Transaction Logs to ILL - 2015 Zero Search Raw Data.csv [more]

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Excel: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 2(1.12%) households where the householder is under 25 years old, 72(40.45%) households with a householder aged between 25 and 44 years, 38(21.35%) households with a householder aged between 45 and 64 years, and 66(37.08%) households where the householder is over 65 years old.
• In Excel, the age group of 25 to 44 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 by age. You can refer the same here

About this dataset

Context

The dataset tabulates the Excel township population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Excel township across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Excel township was 300, a 0.99% decrease year-by-year from 2022. Previously, in 2022, Excel township population was 303, a decline of 0.98% compared to a population of 306 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Excel township increased by 17. In this period, the peak population was 308 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Excel township is shown in this column.
• Year on Year Change: This column displays the change in Excel township population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 township Population by Year. You can refer the same here

Sheet 1 (Raw-Data): The raw data of the study is provided, presenting the tagging results for the used measures described in the paper. For each subject, it includes multiple columns: A. a sequential student ID B an ID that defines a random group label and the notation C. the used notation: user Story or use Cases D. the case they were assigned to: IFA, Sim, or Hos E. the subject's exam grade (total points out of 100). Empty cells mean that the subject did not take the first exam F. a categorical representation of the grade L/M/H, where H is greater or equal to 80, M is between 65 included and 80 excluded, L otherwise G. the total number of classes in the student's conceptual model H. the total number of relationships in the student's conceptual model I. the total number of classes in the expert's conceptual model J. the total number of relationships in the expert's conceptual model K-O. the total number of encountered situations of alignment, wrong representation, system-oriented, omitted, missing (see tagging scheme below) P. the researchers' judgement on how well the derivation process explanation was explained by the student: well explained (a systematic mapping that can be easily reproduced), partially explained (vague indication of the mapping ), or not present.

Tagging scheme:
Aligned (AL) - A concept is represented as a class in both models, either

with the same name or using synonyms or clearly linkable names; Wrongly represented (WR) - A class in the domain expert model is incorrectly represented in the student model, either (i) via an attribute, method, or relationship rather than class, or (ii) using a generic term (e.g., user'' instead ofurban planner''); System-oriented (SO) - A class in CM-Stud that denotes a technical implementation aspect, e.g., access control. Classes that represent legacy system or the system under design (portal, simulator) are legitimate; Omitted (OM) - A class in CM-Expert that does not appear in any way in CM-Stud; Missing (MI) - A class in CM-Stud that does not appear in any way in CM-Expert.

All the calculations and information provided in the following sheets

originate from that raw data.

Sheet 2 (Descriptive-Stats): Shows a summary of statistics from the data collection,

including the number of subjects per case, per notation, per process derivation rigor category, and per exam grade category.

Sheet 3 (Size-Ratio):

The number of classes within the student model divided by the number of classes within the expert model is calculated (describing the size ratio). We provide box plots to allow a visual comparison of the shape of the distribution, its central value, and its variability for each group (by case, notation, process, and exam grade) . The primary focus in this study is on the number of classes. However, we also provided the size ratio for the number of relationships between student and expert model.

Sheet 4 (Overall):

Provides an overview of all subjects regarding the encountered situations, completeness, and correctness, respectively. Correctness is defined as the ratio of classes in a student model that is fully aligned with the classes in the corresponding expert model. It is calculated by dividing the number of aligned concepts (AL) by the sum of the number of aligned concepts (AL), omitted concepts (OM), system-oriented concepts (SO), and wrong representations (WR). Completeness on the other hand, is defined as the ratio of classes in a student model that are correctly or incorrectly represented over the number of classes in the expert model. Completeness is calculated by dividing the sum of aligned concepts (AL) and wrong representations (WR) by the sum of the number of aligned concepts (AL), wrong representations (WR) and omitted concepts (OM). The overview is complemented with general diverging stacked bar charts that illustrate correctness and completeness.

For sheet 4 as well as for the following four sheets, diverging stacked bar

charts are provided to visualize the effect of each of the independent and mediated variables. The charts are based on the relative numbers of encountered situations for each student. In addition, a "Buffer" is calculated witch solely serves the purpose of constructing the diverging stacked bar charts in Excel. Finally, at the bottom of each sheet, the significance (T-test) and effect size (Hedges' g) for both completeness and correctness are provided. Hedges' g was calculated with an online tool: https://www.psychometrica.de/effect_size.html. The independent and moderating variables can be found as follows:

Sheet 5 (By-Notation):

Model correctness and model completeness is compared by notation - UC, US.

Sheet 6 (By-Case):

Model correctness and model completeness is compared by case - SIM, HOS, IFA.

Sheet 7 (By-Process):

Model correctness and model completeness is compared by how well the derivation process is explained - well explained, partially explained, not present.

Sheet 8 (By-Grade):

Model correctness and model completeness is compared by the exam grades, converted to categorical values High, Low , and Medium.

Vrinda Store: Interactive Ms Excel dashboardVrinda Store: Interactive Ms Excel dashboard Feb 2024 - Mar 2024Feb 2024 - Mar 2024 The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022?

And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel The owner of Vrinda store wants to create an annual sales report for 2022. So that their employees can understand their customers and grow more sales further. Questions asked by Owner of Vrinda store are as follows:- 1) Compare the sales and orders using single chart. 2) Which month got the highest sales and orders? 3) Who purchased more - women per men in 2022? 4) What are different order status in 2022? And some other questions related to business. The owner of Vrinda store wanted a visual story of their data. Which can depict all the real time progress and sales insight of the store. This project is a Ms Excel dashboard which presents an interactive visual story to help the Owner and employees in increasing their sales. Task performed : Data cleaning, Data processing, Data analysis, Data visualization, Report. Tool used : Ms Excel Skills: Data Analysis · Data Analytics · ms excel · Pivot Tables

This dataset from the VIRTA Publication Information Service consists of the metadata of 241,575 publications of Finnish universities (publication years 2016–2021) merged from yearly datasets downloaded from https://wiki.eduuni.fi/display/cscvirtajtp/Vuositasoiset+Excel-tiedostot.

The dataset contains following information:

• Organisation: name of the university
• Publication year: the year of publication
• Subfield: one of 66 fields of science based on Statistics Finland field of science classification (in Finnish), see the classification in English: https://www2.stat.fi/en/luokitukset/tieteenala/
• Peer-reviewed: 1=peer-reviewed publications, 0=not peer-reviewed publications
• Science communication: 1=publications aimed at professional and general audiences, 0=peer-reviewed and not peer-reviewed publications aimed at academic audience.
• Bibliodiversity: 1=peer-reviewed book publications (chapters, monographs and edited volumes) and conference articles, 0=peer-reviewed journal articles.
• Multilingualism: share of peer-reviewed publications in languages other than English (Finnish, Swedish and other languages).
• Domestic publishing: 1=peer-reviewed publications in journals and books published in Finland, 0=peer-reviewed publications in journals and books published outside Finland.
• Domestic collaboration: 1=peer-reviewed publications with co-authors from more than one Finnish university, 0=peer-reviewed publications without co-authors from more than one Finnish university.
• International collaboration: 1=share of peer-reviewed publications with co-authors affiliated with foreign institutions, 0=share of peer-reviewed publications without co-authors affiliated with foreign institutions.
• Research performance: 1=peer-reviewed outputs in JUFO levels 2 (“leading”) and 3 (“top”) publication channels, 0=peer-reviewed outputs in JUFO levels 1 (“basic”) and 0 (“other”) publication channels.
• Open access: 1=peer-reviewed open access publications, including gold, hybrid and green OA, 0=peer-reviewed closed publications.

About this dataset

Context

The dataset tabulates the Excel population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Excel across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Excel was 539, a 1.46% decrease year-by-year from 2021. Previously, in 2021, Excel population was 547, a decline of 1.08% compared to a population of 553 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Excel decreased by 36. In this period, the peak population was 713 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Excel is shown in this column.
• Year on Year Change: This column displays the change in Excel population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Population by Year. You can refer the same here

Parrillo’s Article “Administrative Law as a Choice of Business Strategy” documents variation across industries in how frequently companies and their trade associations sue their federal health-and-safety regulators. This dataset page contains the Article’s Methodological Appendix (in PDF), which explains how the author and research team searched for relevant lawsuits using the Bloomberg Law dockets database and how they identified industry challengers, agency actions under challenge, and challenger companies’ parent companies—as well as how the author conducted interviews. This dataset page also contains Excel files with the data on which the Article relies. Most of the Excel files consist of the results of Bloomberg Law dockets database searches for lawsuits, plus information about individual lawsuits and challengers gathered by the author and research team; each of these files includes a tab titled “Lawsuits” that includes a row for each lawsuit, plus a tab titled “Sources and Ordering” that explains how the lawsuit results were obtained from Bloomberg and ordered. The remaining Excel files consist of other relevant data on which the Article relies, especially information about companies or agency operations in certain of the areas studied. Citations in the Article are to the Dataset by File number and then (often) by Row number; each Excel file’s filename begins with the File number referenced in the Article.

Directory of Files: A. Filename: Combine_CCTDI.zip
Short description: Quantitative Data. The zip files contain 6 Excel files which store students' raw data. This raw data set consists of student's input on each CCTDI item. The pre-data were collected through an online survey, while post-data were collected through pen and paper. The data will be analysed by ANOVA to compare the effectiveness of the intervention. (California Critical Thinking Disposition Inventory (CCTDI) has been widely employed in the field of education to investigate the changes in students’ Critical Thinking (CT) attitudes resulting from teaching interventions by comparing the pre- and post-tests. This 6-point scale self-reporting instrument requires respondents to rate themselves, ranging from “rating 1” for not describing them at all to “rating 6” for extremely well. The instrument has 40 questions categorized in seven subsets covering various CT dispositions dimensions, namely: i) truth-seeking, ii) open-mindedness, iii) analyticity, iv) systematicity, v) inquisitiveness, vi) maturity, and vii) self-confidence.

B. Filename: Combine_TCTSPS.zip
Short description: Quantitative Data. The zip files contains 6 excel files which stores students' raw data. consists of student's input on each TCTSPS item. The pre-data were collected through an online survey, while post-data were collected through pen and paper. The data will be analysed by ANOVA to compare the effectiveness of the intervention. (Test of Critical Thinking Skills for Primary and Secondary School Students (TCTS-PS) consists of 24 items divided into five subscales measuring distinct yet correlated aspects of CT skills, namely: (I) differentiating theory from assumptions, (II) deciding evidence, (III) inference, (IV) finding an alternative theory, and (V) evaluation of arguments. The instrument yields a possible total score of 72. The instrument is intended for use in measuring gains in CT skills resulting from instruction, predicting success in programs where CT is crucial, and examining relationships between CT skills and other abilities or traits.)

C. Filename: Combine_SMTSL.zip
Short description: Quantitative Data. The zip files contains 5 excel files which stores students' raw data. consists of student's input on each SMTSL item. The pre-data were collected through an online survey, while post-data were collected through pen and paper. The data will be analysed by ANOVA to compare the effectiveness of the intervention. (Students' Motivation Towards Science learning (SMTSL) defined six factors that related to the motivation in science learning including self-efficacy, active learning strategies and so on, in order to measure participants' motivation towards science learning: A. Self-efficacy, B. Active learning , trategies, C. Science learning value, D. Performance goal, E. Achievement goal, and F. Learning environment stimulation)

D. Filename: Combine_Discourse Transcription_1.zip and Combine_Discourse Transcription_2.zip
Short description: Qualitative Data.The zip files contains 6 excel files which 6 teachers' classroom teaching discourse transcriptions. The data will be analysed by thematic analysis to compare the effectiveness of the intervention. (38 science classroom discourse videos of 8th graders were transcribed and coded by Academically Productive Talk framework (APT). APT is drawing from sociological, linguistic, and anthropological perspectives, comprises four primary constructs or objectives.)

E. Filename: Combine_Inquiry Report.zip
Short description: Qualitative Data. The zip files contains 2 excel files which 2 schools' inquiry report scores according rubrics. The data will be analysed by thematic analysis to compare the effectiveness of the intervention. (To assess the quality of students' arguments, a validated scoring rubric was employed to evaluate the student's written argument. These aspects primarily concentrated on the student's proficiency in five perspectives (Walker & Sampson, 2013, p. 573): (AR1) Provide a well-articulated, adequate, and accurate claim that answers the research question, (AR2) Use genuine evidence to support the claim and to present the evidence in an appropriate manner, (AR3) Provide enough valid and reliable evidence to support the claim, (AR4) Provide a rationale is sufficient and appropriate, and (AR5) Compare his or her findings with other groups in the project.)

F. Filename: Combined_Interview Transcription.xlsx
Short description: Qualitative Data. The file contains all the students' interview transcriptions. The data will be analysed by thematic analysis to compare the effectiveness of the intervention. (A semi-structured interviews was conducted to gather interviewees' motivation of CT and learning motivation in the context of science. The interview data would be used to complement the quantitative results (i.e., TCTS-PS, CCTDI, and SMTSL scores).

In this project, I analysed the employees of an organization located in two distinct countries using Excel. This project covers:

1) How to approach a data analysis project 2) How to systematically clean data 3) Doing EDA with Excel formulas & tables 4) How to use Power Query to combine two datasets 5) Statistical Analysis of data 6) Using formulas like COUNTIFS, SUMIFS, XLOOKUP 7) Making an information finder with your data 8) Male vs. Female Analysis with Pivot tables 9) Calculating Bonuses based on business rules 10) Visual analytics of data with 4 topics 11) Analysing the salary spread (Histograms & Box plots) 12) Relationship between Salary & Rating 13) Staff growth over time - trend analysis 14) Regional Scorecard to compare NZ with India

Including various Excel features such as: 1) Using Tables 2) Working with Power Query 3) Formulas 4) Pivot Tables 5) Conditional formatting 6) Charts 7) Data Validation 8) Keyboard Shortcuts & tricks 9) Dashboard Design

This project delves into the sales data of Maven Coffee Shop to uncover valuable insights and trends. Using Excel, we meticulously cleaned, analyzed, and visualized the data to help understand the business's performance across various dimensions. The original data set was posted on Kaggle.

Key Objectives:

Analyze Sales Trends: Identify sales patterns over time to understand peak periods and sales growth. Evaluate Product Performance: Determine which products and categories drive the most revenue. Assess Store Performance: Compare sales across different store locations to highlight top-performing stores. Interactive Insights: Create an interactive dashboard that allows stakeholders to explore the data without the risk of unintentional edits.

Features:

Comprehensive Data Cleaning: Ensured the data is accurate and ready for analysis. Detailed Revenue Analysis: Explored total sales, average transaction values, and unit sales. Product and Store Analysis: Investigated sales by product category, type, and store location. Interactive Dashboard: Designed a user-friendly Excel dashboard for dynamic data interaction.

This project serves as a practical example of using Excel for data analysis and visualization, providing actionable insights into the operational and financial aspects of a coffee shop business.

Data for meta-analysis derived from studies comparing timber harvest or tree size to nest site occupancy or productivity in northern goshawks (Accipiter gentilis)These are data derived from published literature for a meta-analysis assessing the degree to which timber harvest and tree size explain productivity and site occupancy in northern goshawks (Accipiter gentilis)

We present these data in two alternative formats. (A) An Excel spreadsheet with multiple tabs. This spread sheet includes many comments linked to individual cells explaining the derivation of individual values. (B) A series of .csv files, each corresponding to a different tab in the Excel file. These are the same data as in the Excel sheet, but without the comments linked to individual cells.

The Excel tabs / CSV files are as follows: (1) repro_raw These are the studies that compared productivity (mostly number of fledged young per pair or per nest) to either timber harvest or tree size. We report the individual effe...

This is a study of historical election related legal cases in Sweden. Documentation is only available in Swedish.

Purpose:

The aim of this research project is to describe and explain how and why corrupt electoral practices were abolished in established democracies.

This dataset is made up of an MS-Excel file containing information regarding election related legal cases in Sweden between the years of 1713 and 1869. Currently data is only available in Swedish and only presented in this Excel-format.

Many of the cases can be linked to one or more photographs of material from the National Archives. See the dataset Swedish election corruption in a historical-comparative perspective - Photographs (https://doi.org/10.5878/002722).

Photo documentation and transcription of old texts.

his project involves the creation of an interactive Excel dashboard for SwiftAuto Traders to analyze and visualize car sales data. The dashboard includes several visualizations to provide insights into car sales, profits, and performance across different models and manufacturers. The project makes use of various charts and slicers in Excel for the analysis.

Objective: The primary goal of this project is to showcase the ability to manipulate and visualize car sales data effectively using Excel. The dashboard aims to provide:

Profit and Sales Analysis for each dealer. Sales Performance across various car models and manufacturers. Resale Value Analysis comparing prices and resale values. Insights into Retention Percentage by car models. Files in this Project: Car_Sales_Kaggle_DV0130EN_Lab3_Start.xlsx: The original dataset used to create the dashboard. dashboards.xlsx: The final Excel file that contains the complete dashboard with interactive charts and slicers. Key Visualizations: Average Price and Year Resale Value: A bar chart comparing the average price and resale value of various car models. Power Performance Factor: A column chart displaying the performance across different car models. Unit Sales by Model: A donut chart showcasing unit sales by car model. Retention Percentage: A pie chart illustrating customer retention by car model. Tools Used: Microsoft Excel for creating and organizing the visualizations and dashboard. Excel Slicers for interactive filtering. Charts: Bar charts, pie charts, column charts, and sunburst charts. How to Use: Download the Dataset: You can download the Car_Sales_Kaggle_DV0130EN_Lab3_Start.xlsx file from Kaggle and follow the steps to create a similar dashboard in Excel. Open the Dashboard: The dashboards.xlsx file contains the final version of the dashboard. Simply open it in Excel and start exploring the interactive charts and slicers.

This interactive sales dashboard is designed in Excel for B2C type of Businesses like Dmart, Walmart, Amazon, Shops & Supermarkets, etc. using Slicers, Pivot Tables & Pivot Chart.

Dashboard Overview

1. Sales dashboard ==> basically, it is designed for the B2C type of business. like Dmart, Walmart, Amazon, Shops & supermarkets, etc.
2. Slices ==> slices are used to drill down the data, on the basis of yearly, monthly, by sales type, and by mode of payment.
3. Total Sales/Total Profits ==> here is, the total sales, total profit, and profit percentage these all are combined into a monthly format and we can hide or unhide it to view it as individually or comparative.
4. Product Visual ==> the visual indicates product-wise sales for the selected period. Only 10 products are visualized at a glance, and you can scroll up & down to view other products in the list.
5. Daily Sales ==> It shows day-wise sales. (Area Chart)
6. Sales Type/Payment Mode ==> It shows sales percentage contribution based on the type of selling and mode of payment.
7. Top Product & Category ==> this is for the top-selling product and product category.
8. Category ==> the final one is the category-wise sales contribution.

Datasheets Overview

1. The dataset has the master data sheet or you can call it a catalog. It is added in the table form.
2. The first column is the product ID the list of items in this column is unique.
3. Then we have the product column instead of these two columns, we can manage with only one also but I kept it separate because sometimes product names can be the same, but some parameters will be different, like price, supplier, etc.
4. The next column is the category column, which is the product category. like cosmetics, foods, drinks, electronics, etc.
5. Then we have 4th column which is the unit of measure (UOM) you can update it also, based on the products you have.
6. And the last two columns are buying price and selling price, which means unit purchasing price and unit selling price.

Input Sheet

The first column is the date of Selling. The second column is the product ID. The third column is quantity. The fourth column is sales types, like direct selling, are purchased by a wholesaler or ordered online. The fifth column is a mode of payment, which is online or in cash. You can update these two as per requirements. The last one is a discount percentage. if you want to offer any discount, you can add it here.

Analysis Sheet: where all backend calculations are performed.

So, basically these are the four sheets mentioned above with different tasks.

However, a sales dashboard enables organizations to visualize their real-time sales data and boost productivity.

A dashboard is a very useful tool that brings together all the data in the forms of charts, graphs, statistics and many more visualizations which lead to data-driven and decision making.

Questions & Answers

1. What percentage of profit ratio of sales are displayed in the year 2021 and year 2022? ==> Total profit ratio of sales in the year 2021 is 19% with large sales of PRODUCT42, whereas profit ratio of sales for 2022 is 22% with large sales of PRODUCT30.
2. Which is the top product that have large number of sales in year 2021-2022? ==> The top product in the year 2021 is PRODUCT42 with the total sales of $12,798 whereas in the year 2022 the top product is PRODUCT30 with the total sales of $13,888.
3. In Area Chart which product is highly sold on 28th April 2022? ==> The large number of sales on 28th April 2022 is for PRODUCT14 with a 24% of profit ratio.
4. What is the sales type and payment mode present? ==> The sale type and payment modes show the sales percentage contribution based on the type of selling and mode of payment. Here, the sale types are Direct Sales with 52%, Online Sales with 33% and Wholesaler with 15%. Also, the payment modes are Online mode and Cash equally distributed with 50%.
5. In which month the direct sales are highest in the year 2022? ==> The highest direct sales can be easily identified which is designed by monthly format and it’s the November month where direct sales are highest with 28% as compared with other months.
6. Which payment mode is highly received in the year 2021 and year 2022? ==> The payments received in the year 2021 are the cash payments with 52% as compared with online transactions which are 48%. Also, the cash payment highly received is in the month of March, July and October with direct sales of 42%, Online with 45% and wholesaler with 13% with large sales of PRODUCT24. ==> The payments received in the year 2022 are the Online payments with 52% as compared with cash payments which are 48%. Also, the online payment highly received is in the month of Jan, Sept and December with direct sales of 45%, Online with 37% and whole...