The standardised house price-to-income ratio is defined as the ratio of the current price to income ratio relative to the long-term average price-to-income ratio, calculated over the period 2000 to the most recent data available. If the ratio equals 100, it means the current price-to-income ratio is equal to its long term average. House prices are provided by Eurostat, and income is calculated as adjusted household gross disposable income (B7G) per head of population based on Eurostat data.

Romania Prudential Ratios: Non Performing Loans Ratio: EBA Definition data was reported at 2.490 % in Feb 2025. This records a decrease from the previous number of 2.500 % for Jan 2025. Romania Prudential Ratios: Non Performing Loans Ratio: EBA Definition data is updated monthly, averaging 4.365 % from Sep 2014 (Median) to Feb 2025, with 126 observations. The data reached an all-time high of 21.470 % in Sep 2014 and a record low of 2.370 % in Dec 2023. Romania Prudential Ratios: Non Performing Loans Ratio: EBA Definition data remains active status in CEIC and is reported by National Bank of Romania. The data is categorized under Global Database’s Romania – Table RO.KB011: Loans: Doubtful and Non Performing Loans Ratios.

Residential market value estimates and most recent sales values for owned properties at a parcel level within Fairfax County as of the VALID_TO date in the attribute table. For methodology and a data dictionary please view the IPLS data dictionary

We combine multivariate ratio analysis (MRA) of body measurements and analyses of mitochondrial and nuclear data to examine the status of several species of European paper wasps (Polistes Latreille, 1802) closely related to P. gallicus. Our analyses unambiguously reveal the presence of a cryptic species in Europe, as two distinct species can be recognized in what has hitherto been considered Polistes bischoffi Weyrauch, 1937. One species is almost as light coloured as P. gallicus, and is mainly recorded from Southern Europe and Western Asia. The other species is darker and has a more northern distribution in Central Europe. Both species occur syntopically in Switzerland. Given that the lost lectotype of P. bischoffi originated from Sardinia, we selected a female of the southern species as a neotype. The northern species is described as P. helveticus sp. n. here. We also provide a redescription of P. bischoffi rev. stat. and an identification key including three more closely related spec...

The perfection ratio of a number is a concept that is related to perfect numbers and how closely a given number approximates the ideal perfection ratio, which is 2.0.

Perfect Numbers:

A perfect number is a positive integer that is equal to the sum of its proper divisors, excluding the number itself. For example: • 6 is a perfect number because its divisors are 1, 2, and 3, and 1 + 2 + 3 = 6 . • 28 is another perfect number because its divisors are 1, 2, 4, 7, and 14, and 1 + 2 + 4 + 7 + 14 = 28 .

Perfection Ratio:

The perfection ratio of a number n is a measure of how close the sum of its divisors (excluding the number itself) is to the number. It is defined as:

\text{Perfection Ratio} = \frac{\text{Sum of Proper Divisors of } n}{n}

•  If the perfection ratio is 2.0, the number is considered perfect.
•  If the perfection ratio is greater than 2.0, the number is abundant (i.e., the sum of its proper divisors exceeds the number itself).
•  If the perfection ratio is less than 2.0, the number is deficient (i.e., the sum of its proper divisors is less than the number itself).

Examples:

1. Perfect Number Example:
•  For n = 6 :
•  Proper divisors: 1, 2, 3 
•  Sum of proper divisors: 1 + 2 + 3 = 6 
•  Perfection ratio: \frac{6}{6} = 1.0 
•  Since the perfection ratio is 2.0 for a perfect number, we see the idea of perfect numbers where the sum of divisors divides evenly.

1. Near-Perfect Numbers

• Definition: A near-perfect number is a number for which the sum of its proper divisors is close to the number itself but not exactly equal.
• Example: Consider the number 24. Its proper divisors are 1, 2, 3, 4, 6, 8, and 12. The sum is 36, which is larger than 24, making it almost perfect in the sense that the sum of its divisors is significant but not equal to the number.

2. Almost-Perfect Numbers

• Definition: An almost-perfect number is a number where the sum of its proper divisors equals the number minus one.
• Example: The number 16 is an almost-perfect number. Its proper divisors are 1, 2, 4, and 8, which sum to 15 (16 - 1).

3. Abundant Numbers

• Definition: A number is abundant if the sum of its proper divisors is greater than the number itself.
• Example: The number 12 is abundant because its proper divisors (1, 2, 3, 4, and 6) sum to 16, which is greater than 12.

4. Deficient Numbers

• Definition: A number is deficient if the sum of its proper divisors is less than the number itself.
• Example: The number 8 is deficient because its proper divisors (1, 2, and 4) sum to 7, which is less than 8.

5. Semiperfect Numbers

• Definition: A semiperfect number is a number that is equal to the sum of some (or all) of its proper divisors.
• Example: The number 12 is semiperfect because 12 = 6 + 4 + 2 (some of its proper divisors).

Relevance to the Heat Map

• Density Analysis: By analyzing the heat map further, we might observe concentrations at other specific perfection ratios besides 2. These could indicate near-perfect, almost-perfect, abundant, deficient, or semiperfect numbers.
• Patterns and Trends: Identifying where these numbers cluster can help us understand the distribution and frequency of numbers with these properties within your dataset.

This operations dashboard shows historic and current data related to this performance measure.The performance measure dashboard is available at 4.09 Housing Inventory Ratio. Data Dictionary

We’ve been asked to create measures of communities that are “walkable” for several projects. While there is no standard definition of what makes a community “walkable”, and the definition of “walkability” can differ from person to person, we thought an indicator that explores the total length of available sidewalks relative to the total length of streets in a community could be a good place to start. In this blog post, we describe how we used open data from SPC and Allegheny County to create a new measure for how “walkable” a community is. We wanted to create a ratio of the length of a community’s sidewalks to the length of a community’s streets as a measure of pedestrian infrastructure. A ratio of 1 would mean that a community has an equal number of linear feet of sidewalks and streets. A ratio of about 2 would mean that a community has two linear feet of sidewalk for every linear foot of street. In other words, every street has a sidewalk on either side of it. In creating a measure of the ratio of streets to sidewalks, we had to do a little bit of data cleanup. Much of this was by trial and error, ground-truthing the data based on our personal experiences walking in different neighborhoods. Since street data was not shared as open data by many counties in our region either on PASDA or through the SPC open data portal, we limited our analysis of “walkability” to Allegheny County.

In looking at the sidewalk data table and map, we noticed that trails were included. While nice to have in the data, we wanted to exclude these two features from the ratio. We did this to avoid a situation where a community that had few sidewalks but was in the same blockgroup as a park with trails would get “credit” for being more “walkable” than it actually is according to our definition. We did this by removing all segments where “Trail” was in the “Type_Name” field.

We also used a similar tabular selection method to remove crosswalks from the sidewalk data “Type_Name”=”Crosswalk.” We kept the steps in the dataset along with the sidewalks.

In the street data obtained from Allegheny County’s GIS department, we felt like we should try to exclude limited-access highway segments from the analysis, since pedestrians are prohibited from using them, and their presence would have reduced the sidewalk/street ratio in communities where they are located. We did this by excluding street segments whose values in the “FCC” field (designating type of street) equaled “A11” or “A63.” We also removed trails from this dataset by excluding those classified as “H10.” Since documentation was sparse, we looked to see how these features were classified in the data to determine which codes to exclude.

After running the data initially, we also realized that excluding alleyways from the calculations also could improve the accuracy of our results. Some of the communities with substantial pedestrian infrastructure have alleyways, and including them would make them appear to be less-”walkable” in our indicator. We removed these from the dataset by removing records with a value of “Aly” or “Way” in the “St_Type” field. We also excluded streets where the word “Alley” appeared in the street name, or “St_Name” field.

The full methodology used for this dataset is captured in our blog post, and we have also included the sidewalk and street data used to create the ratio here as well.

The SORCE SOLSTICE Level 3 MgII Core to Wing Ratio 24 Hour Means product consists of daily averages of the magnesium II core-to-wing index from the SOLSTICE instrument. The SOLSTICE instrument makes measurements during each daytime orbit portion, 15 orbits per day. This product has solar spectra averaged for a day. The spectral resolution of SOLSTICE is 0.1 nm, allowing the Mg-II doublet to be fully resolved and modeled with Gaussians. The Mg-II core-to-wing ratio is used as a measurement of solar activity.The Mg-II data are arranged in a single file in a tabular ASCII text file which can be easily read into a spreadsheet application. The columns contain the date (calendar and Julian Day), the core/wing ratio, and the absolute uncertainty. The rows are arranged with one daily average measurment, repeating for each day for the length of the measurement period.

ABSTRACT This study presents a new approach for the definition of weight restrictions in Data Envelopment Analysis (DEA) for the one output, multiple inputs case, using the results of a Linear Regression model (LRM) developed with the same DEA variables. Thus, the limits of Wong-Beasley and Cone Ratio methods are chosen without interference from a decision maker, with DEA weight search intervals defined from the estimated standardized coefficients of a linear regression (which represent the statistical importance of the inputs for the definition of the DEA efficiency scores). As an example, weight restrictions for a DEA model (Constant Returns to Scale (CRS)) were obtained through the unrestricted, Wong-Beasley and Cone Ratio methods applied to a dataset consisting of hospital admissions (output), number of beds and number of health professionals (inputs) in the year 2016; and rankings were compared by a Spearman correlation procedure. The regression model had R 2 = 0.89 with coefficients 0.43 (professionals) and 0.54 (beds); and the Spearman correlation among rankings was at least R S 2 = 0.84. In conclusion, rankings were consistent and interpretable, and the approach circumvents the need for a subjective intervention by a decision maker when defining weight restrictions in DEA.

This dataset contains spatial boundaries for Bonus Plot Ratio Plans relating to the City of Perth Planning Scheme No.2The Maximum Bonus Plot Ratio Plan shows the total maximum bonus plot ratio that can be granted on a specific lot. This is either 20% or 50%.Bonus plot ratio may be granted under a single category or a combination of Special Residential, Residential, Heritage and Public Facilities.Definition under Schedule 4 “means the maximum percentage increase in the maximum plot ratio which is specified for a lot or part of a lot by the Maximum Bonus Plot Ratio Plan”;Please see https://perth.wa.gov.au/develop/planning-framework/planning-schemes and https://perth.wa.gov.au/develop/planning-framework/planning-policies-and-precinct-plans for more information regarding the City of Perth Planning Schemes.

This dataset contains spatial boundaries for Plot Ratio Plans relating to the City of Perth Planning Scheme No.2The Plot Ratio Plan determines the development potential on each lot under the City of Perth's planning authority.Plot ratio is written as a ratio i.e. a site of 1000msq with a plot ratio of 6:1 can develop a maximum of 6000msq of floor space. Therefore the higher the plot ratio of a site the greater its development potential.Definition under Schedule 4 “Plot ratio means the ratio of the floor area of a building to the area of land within the boundaries of the lots on which that building is located;”Please see https://perth.wa.gov.au/develop/planning-framework/planning-schemes and https://perth.wa.gov.au/develop/planning-framework/planning-policies-and-precinct-plans for more information regarding the City of Perth Planning Schemes.

Definition: The 90/10 decile ratio is a measure of the inequality of distribution. It is determined here in relation to the distribution of equivalised income. It sets the lower limit of the equivalised income of the highest-income decile (= upper limit of the 9. The ratio of the equivalised income of the lowest-income decile. Equivalised income is a weighted per capita income per household member, which is calculated by dividing household net income by the sum of the household weights of persons living in the household. The head of the household is assigned the weight = 1, for the other household members weights of < 1 are used because it is assumed that savings can be achieved through joint management. The new OECD scale is used as a scale of equivalence to determine the respective weights. After that, the head of household is assigned a weight of 1, other household members aged 14 or more a weight of 0.5 and household members under the age of 14 are assigned a weight of 0.3. In order to form the income decile, all persons are sorted according to the level of equivalised income and divided into ten equal groups. The first decile contains the 10 percent with the lowest, the tenth with the highest equivalised income.

Data source:
IT.NRW, Microcensus

2013-2023 Virginia Ratio of Income to Poverty Level in the Past 12 Months by Census Block Group. Contains estimates and margins of error.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table C17002 Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html)

The United States Census Bureau's American Community Survey (ACS): -What is the American Community Survey? (https://www.census.gov/programs-surveys/acs/about.html) -Geography & ACS (https://www.census.gov/programs-surveys/acs/geography-acs.html) -Technical Documentation (https://www.census.gov/programs-surveys/acs/technical-documentation.html)

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section. (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html)

Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section. (https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/)

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties.

Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation https://www.census.gov/programs-surveys/acs/technical-documentation.html). The effect of nonsampling error is not represented in these tables.

Key information about Panama Non Performing Loans Ratio

• Panama Non Performing Loans Ratio stood at 1.5 % in Dec 2024, compared with the ratio of 1.7 % in the previous month
• Panama Non Performing Loans Ratio data is updated monthly, available from Dec 2008 to Dec 2024
• The data reached an all-time high of 2.7 % in Aug 2021 and a record low of 0.9 % in Mar 2013

• In the latest reports, Money Supply M2 in Panama increased 8.4 % YoY in Nov 2024
• Panama Foreign Exchange Reserves was measured at 6.3 USD bn in Dec 2024
• The Foreign Exchange Reserves equaled 5.5 Months of Import in Dec 2024
• The country's Domestic Credit reached 63.6 USD bn in Dec 2024, representing an increased of 4.9 % YoY
• Household Debt of Panama reached 32.9 USD bn in Dec 2024, accounting for 38.3 % of the country's Nominal GDP

The Air Traffic Data International Mobility Indicators for the UK results from the investigation on air passenger data from the Sabre Corporation [1], accessed through a collaboration with the JRC Ispra. Starting from air passenger traffic volumes from each country of origin and to the final country of destination, two mobility indicators based on log flow ratios were provided: the Flow Log Ratio (FLR) and the Cumulative Flow Log Ratio (CFLR). These indicators, computed with monthly and yearly resolution, allow to eliminate short term trips observing the general pattern of longer-term mobility. The Flow Log Ratio (FLR) is defined as the logarithm of the ratio between the number of incoming individuals in a given country (e.g., entering the UK) and the number of outgoing individuals in the same observed country (e.g., leaving the UK). Specifically, for each country or set of countries of origin and destination (C1, C2), and over a specified period of time, t, we consider the incoming flow FI(t) (from C2 to C1) and the outgoing flow FO(t) (from C1 to C2). The Flow Log Ratio FLR(t) is then defined as log2(FI(t)/FO(t)). If the FLR is below 0, it means that more individuals moved out of C1 compared to those who moved in, while an index above 0 shows that C1 is an attractive country with more people coming in. An FLR of 1 means the incoming flows are twice as large as outgoing flows, while an FLR of -1 means the outgoing flows are twice less. The FLR is an indicator that allows to study the trends point by point in time and observe point-wise changes in trends. The Cumulative Flow Log Ratio (CFLR) is defined as the logarithm of the ratio between the cumulative incoming flows and cumulative outgoing flows up to the current time window t. Compared to the FLR, the CFLR allows to evaluate cumulative pattens over much longer periods, rather than performing a point-wise analysis. The indicators are provided for the UK versus the rest of the European Union. Further, we provide regional indicators using the division of EU member states into regions proposed by the EuroVoc vocabulary [2]: Northern (Finland, Denmark, Sweden, Estonia, Latvia, Lithuania), Southern (Greece, Italy, Malta, Portugal, Cyprus, Spain), Western (France, Germany, Ireland, Luxembourg, Netherlands, Austria, Belgium), Central and Eastern (Hungary, Poland, Romania, Bulgaria, Croatia, Slovakia, Czechia, Slovenia). Europe-level indicators are also included. The entire Air Traffic Data International Mobility Indicators for the UK includes monthly and yearly Flow Log Ratio and Cumulative Flow Log Ratio indicators calculated at different spatial and time resolutions. Further, the monthly set also provides the components obtained by applying Seasonal-Trend decomposition (TSD) [3] to FLR regional values. These allow for separating seasonal from overall patterns. The Air Traffic Data International Mobility Indicators for the UK include FLRs and CFLRs values calculated for the United Kingdom versus a) the 27 countries in the European Union, b) the four regions of the European Union, and c) the entire European Union. Monthly data are provided from February 2011 to October 2021, while yearly data covers 2011-2021. Moreover, the monthly dataset includes components, i.e., trend, seasonal, and residual signals, obtained by decomposing the regional EU FLRs with Statsmodels [4] Python library (using an additive model with 12 components). In publishing the dataset, we followed the DEU guidelines for publishing high-quality data. To ensure interoperability and facilitate automatic processing by machines, we used the CSV format with US-ASCII encoding. All country names follow the ISO2 standard. The European subregions follow the EuroVoc vocabulary, dates are standardised, time series are complete. The CSV files are accompanied by a README that defines all variables included in the data and cross-references publications. References: [1] Sabre. Market intelligence, global demand data. https://es.sonicurlprotection-fra.com/click?PV=2&MSGID=202302101437200109948&URLID=11&ESV=10.0.19.7431&IV=259BC11764855306985B70AF21AF9795&TT=1676039840964&ESN=Vs8xERNXlu7bOs3Tyb9f%2Fa8tNspLAa%2FGwagIu4vHdcQ%3D&KV=1536961729280&B64_ENCODED_URL=aHR0cHM6Ly93d3cuc2FicmUuY29tL3Byb2R1Y3RzL21hcmtldC1pbnRlbGxpZ2VuY2UvLA&HK=D2BCC95C29FB56BEC2A395CC3D9C17C53D482CA86C9C38AA591FB4CEC3FD597F 2021. Accessed: 2021-11-15. [2] https://es.sonicurlprotection-fra.com/click?PV=2&MSGID=202302101437200109948&URLID=10&ESV=10.0.19.7431&IV=2934525D891132A3AEF7FAE3284ABBF5&TT=1676039840964&ESN=1y%2BYp5gdrdyZM9uJx0B%2FPBEP1rDDsKvDHe7LgSX0cS8%3D&KV=1536961729280&B64_ENCODED_URL=aHR0cHM6Ly9ldXItbGV4LmV1cm9wYS5ldS9icm93c2UvZXVyb3ZvYy5odG1sP3BhcmFtcz03Miw3MjA2&HK=8C84248906662B84FF5949BF9C969AA3FE97AB3970282A47E9BDFA1EB8E0B1F6 [3] Cleveland, R.B., Cleveland, W.S., McRae, J.E. and Terpenning, I., 1990. STL: A seasonal-trend decomposition. J. Off. Stat, 6(1), pp.3-73. [4] McKinney, W., Perktold, J., & Seabold, S. (2011)....

This operations dashboard shows historic and current data related to this performance measure.The performance measure dashboard is available at 3.25 Equal Pay Ratio 9th Congressional District. Data Dictionary

The performance measure dashboard is available at 3.25 Equal Pay Ratio 9th Congressional District.

Additional Information

Source:
Contact: Wydale Holmes
Contact E-Mail: Wydale_Holmes@tempe.gov
Data Source Type: Excel
Preparation Method:
Publish Frequency: annually
Publish Method: manual
Data Dictionary

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..The age dependency ratio is derived by dividing the combined under-18 and 65-and-over populations by the 18-to-64 population and multiplying by 100..The old-age dependency ratio is derived by dividing the population 65 and over by the 18-to-64 population and multiplying by 100..The child dependency ratio is derived by dividing the population under 18 by the 18-to-64 population and multiplying by 100..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

ABSTRACT The nutritional quality of orange influences directly on its juice quality. Therefore, the DRIS (Diagnosis and Recommendation Integrated System) allows the verification of nutrients balance in plants as well as determine if its growth is associated or not to nutritionals restrictions. Thus, this research applied the total soluble solids/total titratable acidity to identify the standard group and to define the DRIS. The experiment was carried out in an orange orchard (1 ha) of the Monte Parnaso variety, in Southern Brazil. Twenty trees were geo-referenced, aiming to collect samples for foliar analysis and quantify fruits quality parameters. Therefore, it was possible to observe that total soluble solids/total titratable acidity presented the best ratio with the nutritional balance index, when compared to yield. Hence, it presents the best response when choosing standard group for DRIS calculations.

Introduction:

This analysis delves into the financial performance of top companies by examining key metrics such as revenue, earnings, market capitalisation, P/E ratio, and dividend yield. By comparing these metrics, we gain a comprehensive understanding of a company's scale, profitability, market value, and growth potential. Through visualisations, the analysis also explores correlations between these metrics and offers insights into country-level performance, highlighting economic dominance across various sectors. This holistic approach provides a multi-dimensional view of global financial powerhouses, investor confidence, and regional economic trends.

Key Metrics Used:

1. Revenue (Trailing Twelve Months - TTM): - Definition: This is the total income generated by a company from its operations in the last twelve months. - Potential Insights: High revenue often indicates market dominance or high sales volume. Comparing revenues can reveal which companies are the largest in terms of business volume.

2. Earnings (TTM): - Definition: This refers to the company's profit after taxes and expenses over the trailing twelve months. - Potential Insights: Companies with high earnings are more efficient at converting revenue into profit, suggesting better profitability or cost management. A comparison of earnings provides insight into profitability rather than just scale.

3. Market Capitalisation (Market Cap): - Definition: Market cap is the total value of a company's outstanding shares of stock, calculated as stock price multiplied by the number of shares. It indicates the company’s size in the stock market. - Potential Insights: High market cap usually indicates investor confidence in the company. Comparing market cap among the top 15 companies reveals their relative size in financial markets.

4. P/E Ratio (TTM): - Definition: Price-to-Earnings (P/E) ratio measures a company's current share price relative to its per-share earnings. - Potential Insights: A high P/E ratio may indicate that investors expect high growth in the future, while a low P/E ratio could imply undervaluation or scepticism about growth. Companies are compared by their growth prospects or current valuation.

5. Dividend Yield (TTM): - Definition: Dividend yield is a financial ratio that shows how much a company pays out in dividends each year relative to its share price. - Potential Insights: High dividend yield may indicate that a company returns more income to shareholders. It’s particularly useful for income-focused investors.

In this combined analysis, we will integrate the observations from the visualisations with the key financial metrics definitions and insights, to offer a comprehensive view of the top companies and country-level analysis across various financial dimensions.

Data Visualisations

Visualisation 1: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F2296eddd53ddd4b84346b1ea1324ec0a%2FScreenshot%202024-10-01%2015.16.51.png?generation=1727864461164331&alt=media" alt=""> Visualisation 2: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2Fb35516c91e54eda75a03ff073e94dd73%2FScreenshot%202024-10-01%2015.17.53.png?generation=1727864511265917&alt=media" alt=""> Visualisation 3: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F506ca2428d34b15cd46e4a31261763d7%2FScreenshot%202024-10-01%2015.18.37.png?generation=1727864562835491&alt=media" alt=""> Visualisation 4: https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13231939%2F41e7a3e28c757239d26226f6a0ccdca9%2FScreenshot%202024-10-01%2015.19.20.png?generation=1727864614352037&alt=media" alt=""> A Markdown document with the R code for the above visualisations. link

1. Revenue (Trailing Twelve Months - TTM)

• Definition: The total income generated from a company’s operations over the last 12 months.
• Insights from Charts:
  • Revenue vs Earnings (Visualisation 2): Companies like Saudi Aramco are massive outliers with high revenues and even higher earnings, indicating impressive profitability despite their revenue volume.
  • Top 10 Countries by Average Revenue (Visualisation 3): China, South Korea, and Japan are at the top, with companies generating significant business volumes.
  • Analysis: High revenue companies like Walmart dominate the market but may not always convert this into proportional earnings or market cap growth. This could be due to operational costs or sector-specific challenges (retail margins being lower than tech).

2. Earnings (TTM)

• Definition: Profits...