Frequency distribution table for compositions of households included in the retrospective data.

Frequency distribution table per genotypes and vertical facial profiles with univariate statistics.

DOI retrieved: 1984

The following hypothesis is proposed: When additivity is valid, the molecular frequency distribution (MFD) is a weighted sum of group frequency distributions (GFDs). On the basis of the transformation rules for distribution functions from statistical theory and the rigid-rotator harmonic oscillator approximation, it is shown analytically that the hypothesis leads to group additivity of vibrationally dependent thermochemical parameters. Graph theory has been used to find the structure of the matrices used to determine group additivity values, and the results show that, within the rigid-rotator harmonic oscillator approximation, additivity of the vibrational contributions leads to the experimentally observed group additivity of the total group contributions. Support for the hypothesis is obtained from ab initio and DFT frequency calculations of a total 182 different compounds from seven polymeric series. In agreement with the hypothesis, remarkable similarities of the MFDs are observed throughout each series. Molecular frequencies can be satisfactorily calculated from model calculations based on the hypothesis, and temperature dependent heat capacities for the monomeric units can be derived that are in agreement with experimental values.

Network of 46 papers and 72 citation links related to "Differential size frequency distribution of hard coral colonies across physical reef health gradients in Northeast Peninsula Malaysia".

PART I: Distribution table: Interval Frequency Cumulative Frequency Percentage distribution Cumulative percentage distribution 10-12 2 2 13.33 13.33 12.1-14 5 7 33.33 46.66 14.1-16 8 15 53.33 99.99 16.1-18 0 15 0 99.99

18.1 0 15 0 99.99

Majority of the countries, eight, fall in the 14.1-16 category. Five countries fall in the 12.1-14 category and two countries in the 10-12 bin. The remaining categories have zero entries. This means the data does not follow a normal distribution since most of the countries are concentrated at the highest peak. This data could be better visualized in a histogram.

Frequency distribution with revised interval: Interval Frequency Cumulative Frequency Percentage Frequency Cumulative percentage <12 2 2 13.33 13.33 12-12.9 1 3 6.67 20 13-13.9 4 7 26.67 46.67 14-14.9 4 11 26.67 73.34 15-15.9 3 14 20 93.34 16-16.9 1 15 6.67 100.01 17-17.9 0 15 0 100.01

18 0 15 0 100.01 Eight countries have between 14% and 18% of their population above age 65. The number of countries with 14% - 18% of their population above 65 years remain the same even after revising the interval. The percentage of countries that have between 14-18 percent of their population above age 65 is 53.33%.

PART II Q1. Time series chart for divorce rate in Netherlands

Q2. Describe divorce rate in Netherlands before and after 1970. There is a decline in divorce rate between 1950 and 1960. There is a moderate rise in divorce rate between 1960 and 1970, the rate steadily rises between 1970 and 1980 and thereafter exhibits a slight decline between 1980 and 1990. The rate shifts to a declining trend after the year 2000. The decline does not indicate negative number of divorces, this could be attributed to increased population size and fewer number of divorce cases filed. Q3. A bar graph would best display the divorce rate for each year, hence easy comparison. Q4. Bar graph The highest number of divorce cases were recorded in the year 2000, while the least number was observed in 1960.

Set 2: Show how different elements contributed to population change in 2018

Immigration contributed 34 percent of the change in population; births, Emigration, and deaths contributed almost equal change in population.

Q2. Elements of population growth

Immigration contributed the largest change in population growth compared to birth.

Q3. A time series to show changes in male and female population

Both populations show an increasing trend over the 4 years. We could also conclude there are more females than males in the country’s population.

Frequency distribution chart of GSTT1 gene and GSTM1 gene polymorphism with respect to the risk of developing COPD (** - Statistically significant P<0.05).

This graph shows the distribution of French Internet users who shared pictures from their holidays on Snapchat in 2015, by frequency. It shows that ** percent of French Internet users have never done it.

In this cross‑sectional study, 32 untreated anterior teeth with periapical periodontitis were enrolled, and compared with the healthy contralateral teeth. Cone-beam computed tomography was used to measure diameters of the apical constrictions. 3D reconstruction technique was used to reconstruct the teeth and analysis the apical constriction types. The distances of apical constriction to apical foramen and anatomical apex were measured respectively. Comparisons were made between periapical periodontitis teeth and healthy teeth data (AC-AA, AC-AF) through paired t tests with a significance level of 5%. Differences in constriction forms were analyzed by Fisher exact test or Chi square test according to gender. Statistical Package for Social Sciences 25.0 (IBM Co, New York, NY) was used for statistical analysis. The significance level was set at p < 0.05.

TABLE 9. Frequency Distribution of lateral blotch counts for Etheostoma rupestre subspecies. Blotch counts of 5 or below indicate the presence of blotches that were too diffuse too count. Blotch counts of 6 include both fish that had 6 distinct blotches and those that had some blotches too diffuse to count.

TABLE 3. Frequency distribution for number of scales below the lateral line for each of the three Etheostoma rupestre subspecies.

The evolution of a software system can be studied in terms of how various properties as reflected by software metrics change over time. Current models of software evolution have allowed for inferences to be drawn about certain attributes of the software system, for instance, regarding the architecture, complexity and its impact on the development effort. However, an inherent limitation of these models is that they do not provide any direct insight into where growth takes place. In particular, we cannot assess the impact of evolution on the underlying distribution of size and complexity among the various classes. Such an analysis is needed in order to answer questions such as 'do developers tend to evenly distribute complexity as systems get bigger?', and 'do large and complex classes get bigger over time?'. These are questions of more than passing interest since by understanding what typical and successful software evolution looks like, we can identify anomalous situations and take action earlier than might otherwise be possible. Information gained from an analysis of the distribution of growth will also show if there are consistent boundaries within which a software design structure exists. The specific research questions that we address in Chapter 5 (Growth Dynamics) of the thesis this data accompanies are: What is the nature of distribution of software size and complexity measures? How does the profile and shape of this distribution change as software systems evolve? Is the rate and nature of change erratic? Do large and complex classes become bigger and more complex as software systems evolve? In our study of metric distributions, we focused on 10 different measures that span a range of size and complexity measures. In order to assess assigned responsibilities we use the two metrics Load Instruction Count and Store Instruction Count. Both metrics provide a measure for the frequency of state changes in data containers within a system. Number of Branches, on the other hand, records all branch instructions and is used to measure the structural complexity at class level. This measure is equivalent to Weighted Method Count (WMC) as proposed by Chidamber and Kemerer (1994) if a weight of 1 is applied for all methods and the complexity measure used is cyclomatic complexity. We use the measures of Fan-Out Count and Type Construction Count to obtain insight into the dynamics of the software systems. The former offers a means to document the degree of delegation, whereas the latter can be used to count the frequency of object instantiations. The remaining metrics provide structural size and complexity measures. In-Degree Count and Out-Degree Count reveal the coupling of classes within a system. These measures are extracted from the type dependency graph that we construct for each analyzed system. The vertices in this graph are classes, whereas the edges are directed links between classes. We associate popularity (i.e., the number of incoming links) with In-Degree Count and usage or delegation (i.e., the number of outgoing links) with Out-Degree Count. Number of Methods, Public Method Count, and Number of Attributes define typical object-oriented size measures and provide insights into the extent of data and functionality encapsulation. The raw metric data (4 .txt files and 1 .log file in a .zip file measuring ~0.5MB in total) is provided as a comma separated values (CSV) file, and the first line of the CSV file contains the header. A detailed output of the statistical analysis undertaken is provided as log files generated directly from Stata (statistical analysis software).

This data article describes the health information literacy of Nigerians on the preventive measures of COVID-19 from 7890 respondents across the federation. The data described include the socioeconomic variables of respondents (gender, educational level, and residential location), sources of COVID-19 health related information, and health information literacy scale; which is made up of 25 objective questions. Online survey was conducted between April and December, 2020. Data collected was analysed using frequency distribution table, chart and percentage. The 25 objective questions used to assess the health information literacy level of Nigerians on the preventive measures of COVID-19 was scored (correct answer is 4 marks while incorrect answer is 0). the link to the questionnaire is https://docs.google.com/forms/d/1lexlFeQ4da6C_JCl0yWUHtoxXOe2dnP0aYU8UJdECBY/edit?usp=sharing

This graph shows the distribution of French people making donations to charities or non-governmental organizations on the Internet in 2020, by frequency. It appears that the majority of French people (** percent) have never made an online donation.

This table contains 324 series, with data for years 2013 - 2015 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada) Measures (3 items: Low-frequency hearing loss; High-frequency hearing loss; Speech-frequency hearing loss) Sex (3 items: Both sexes; Males; Females) Age group (6 items: Ages 6 to 79; Ages 6 to 11; Ages 12 to 19; Ages 20 to 39; ...) Categories (2 items: Hearing loss; No hearing loss) Characteristics (3 items: Estimate; Low 95% confidence interval, estimate; High 95% confidence interval, estimate)

TABLE 6. Frequency distribution of selected fin ray counts for five species of Enneapterygius

H and L indicate holotype and lectotype, respectively

Index Time Series for BNP Paribas Easy MSCI Europe SRI S-Series PAB 5% Capped UCITS ETF Distribution. The frequency of the observation is daily. Moving average series are also typically included. NA

Online Appendix 3.1: Small groups of Neolithic pits in Devon.

Online Appendix 5.1: Iron Age and Romano-British pottery, Ed McSloy. Online Chart 5.1: Fabric Group incidence by Phase. Online Chart 5.2: Iron Age and Roman pottery quantities by area (grouped trenches). Online Chart 5.3: Roman pottery quantities by Area (grouped trench). Pottery dating and stratigraphy, including: Online Table 5.i: Trenches 7 and 8 pottery summary. Online Table 5.ii: Trench 7/8: residual pottery from graves. Online Table 5.iii: Trench 9 Summary by phase. Online Table 5.iv: Trenches 10–12 pottery summary. Online Table 5.v: Trenches 13–15. Selected Phase 4A and 4B groups. Online Table 5.vi: Trenches 16+18; Phases 3–4A (Iron Age to Early Roman). Online Table 5.vii: Trenches 16/18; Phase 4B (Late Roman).

Online Appendix 5.2 Fabric analyses via ICP of Iron Age and Roman ceramics, Kamal Badreshany. Online table 5.viii results of ICP analysis. Online Figure 5.i Image of FS3 fabric 1 in plane polarized light. Online Fig. 5.ii. Image of FS16 fabric 2 in plane polarized light. Online Fig. 5.iii Image of FS13 fabric 3 in cross polarized light. Online Fig. 5.iv Image of FS8 fabric 4 in cross polarized light. Online Fig. 5.v Results of a Principal Components Analysis of samples analysed by ICP-AES and ICP-MS. Online Fig. 5.vi Results of a Principal Components Analysis of samples analysed by ICP-AES and ICP-MS. Online Fig 5.vii Bivariate plot showing the rare earth elements (REE) sums and ratios. Online Fig. 5.viii Bivariate plot showing the rare earth elements (REE) sums and ratios.

Online Appendix 5.3: Amphora Catalogue, David Williams.

Online Appendix 5.4: The samian, P.V. Webster.

Online Appendix 5.5 Roman glass illiustrations, Denise Allen.

Online Appendix 5.6: Archaeometallurgy tables, Tim Young. Table 1: summary catalogue of materials, IPP12/13. Table 2: summary catalogue of materials, IPP14. Table 3: summary catalogue of materials, IPP15. Table 4: summary catalogue of materials, IPP16. Table 5: summary catalogue of materials, IPP17. Table 6: summary catalogue of materials, IPP18. Table 7: summary catalogue of materials, IPP19. Table 8. Distribution of residue facies by period and context. Table 9. Proportion of specified classes of material recovered from each time-period, showing the variation in composition of the residue assemblages over time. Table 10. Distribution of low-density, ceramic influenced facies by period and context. Table 11. Comparison of the SHC weight-frequency distribution of different periods. Table 12: Comparison of the overall SHC weight-frequency distribution with that of other selected Romano-British assemblages from southern Britain. Table 13. Net peak heights for selected peaks from the pXRF analysis of materials from Dainton elms Cross.

Online Appendix 5.7: Animal bone, Tess Townend, Stephen Rippon, and Alan Outram. Online Table 5.37 Measurements of cattle (a Jersey cow) and sheep used as standard animals to compare size change. Online Figure 5.46 Fracture history profile of trenches with fractured bone. Online Figure 5.49 Age profiles of sheep/goat from Phases 4A and 4B based on dental scoring, compared to husbandry kill-off models. Online Figure 5.50 Bar graph of pig age profiles for all available phases (Phases 3, 4A, 4B, 5, and 6). Online Figure 5.51 Body part abundance of cattle by phase, calculated from MNE. Online Figure 5.52 A Phase 4B cattle mandible displaying chop and cut marks (context 16005). Online Figure 5.53 Example of tool mark used to split a long bone (well F.16003, context 16005). Online Figure 5.54 Caprine skeletal part abundance by phase, calculated from MNE. Online Figure 5.55 Worked bone from Early Roman context 1118. Online Figure 5.56 Pig skeletal abundance by phase according to MNE. Online Figure 5.57 Log ratio of cattle long bones with breadth measurements from Phase 4A, 4B, and 5. Online Figure 5.58 Log ratio of caprine long bones with breadth measurements from Phase 4A, 4B, and 5. Online Figure 5.59 Phase 4A caprine tibial shaft with healed fracture evidenced by new bone growth. Online Figure 5.61 Univariate plot of caprine metatarsal SD in mm, compared to other periods and sites types from across Britain. Online Figure 5.62 Univariate plot of cattle metacarpal GL in mm, compared to other periods and sites types.

Online Appendix 5.8: Isotopic analysis of Roman animal bone, Isaac Jervis., Stephen Rippon, and Alexander Pryor. Online Table 5.48 The wear stages of teeth analysed. Online Table 5.49 The enamel 87Sr/86Sr results for all samples analysed [see Spreadsheet]. Online Table 5.50 δ18O and δ13C results from enamel carbonates for all teeth analysed [see spreadsheet].

Online Appendix 8.1 Introduction to the Greater Teignbridge Area, Stephen Rippon. Online Table 8.1 Summary of the geological sequence in the Teignbridge area with the resultant topography, soils, and pays. Online Table 8.2 Summary of the results from palaeoenvironmental analyses from off-site sequences covering the Iron Age, Roman and medieval periods in Devon and western Somerset (Exmoor).

Online Appendix 11.1 Reconstructing vills.

Online Appendix 12.1 The anatomy of a droveway: Cockington to Dewdon (Jordan), Richard Sandover.

Online Appendix 14 Stratigraphic reports on each trench (including context descriptions).

Credit Rating History Dataset

Credit Rating History

By Center for Municipal Finance [source]

About this dataset

The project that led to the creation of this dataset received funding from the Center for Corporate and Securities Law at the University of San Diego School of Law. The dataset itself can be accessed through a GitHub repository or on its dedicated website.

In terms of columns contained in this dataset, it encompasses a range of variables relevant to analyzing credit ratings. However, specific details about these columns are not provided in the given information. To acquire a more accurate understanding of the column labels and their corresponding attributes or measurements present in this dataset, further exploration or referencing additional resources may be required

How to use the dataset

• Understanding the Data

  The dataset consists of several columns that provide essential information about credit ratings and fixed income securities. Familiarize yourself with the column names and their meanings to better understand the data:

  • Column 1: [Credit Agency]
  • Column 2: [Issuer Name]
  • Column 3: [CUSIP/ISIN]
  • Column 4: [Rating Type]
  • Column 5: [Rating Source]
  • Column 6: [Rating Date]

• Exploratory Data Analysis (EDA)

  Before diving into detailed analysis, start by performing exploratory data analysis to get an overview of the dataset.

  • Identify Unique Values: Explore each column's unique values to understand rating agencies, issuers, rating types, sources, etc.

  • Frequency Distribution: Analyze the frequency distribution of various attributes like credit agencies or rating types to identify any imbalances or biases in the data.

• Data Visualization

  Visualizing your data can provide insights that are difficult to derive from tabular representation alone. Utilize various visualization techniques such as bar charts, pie charts, histograms, or line graphs based on your specific objectives.

  For example:

  • Plotting a histogram of each credit agency's ratings can help you understand their distribution across different categories.
  • A time-series line graph can show how ratings have evolved over time for specific issuers or industries.

• Analyzing Ratings Performance

  One of the main objectives of using credit rating datasets is to assess the performance and accuracy of different credit agencies. Conducting a thorough analysis can help you understand how ratings have changed over time and evaluate the consistency of each agency's ratings.

  • Rating Changes Over Time: Analyze how ratings for specific issuers or industries have changed over different periods.

  • Comparing Rating Agencies: Compare ratings from different agencies to identify any discrepancies or trends. Are there consistent differences in their assessments?

• Detecting Rating Trends

  The dataset allows you to detect trends and correlations between various factors related to

Research Ideas

• Credit Rating Analysis: This dataset can be used for analyzing credit ratings and trends of various fixed income securities. It provides historical credit rating data from different rating agencies, allowing researchers to study the performance, accuracy, and consistency of these ratings over time.
• Comparative Analysis: The dataset allows for comparative analysis between different agencies' credit ratings for a specific security or issuer. Researchers can compare the ratings assigned by different agencies and identify any discrepancies or differences in their assessments. This analysis can help in understanding variations in methodologies and improving the transparency of credit rating processes

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all ...

Aggregate data for the PLOS ONE article "Beyond funding: Acknowledgement patterns in biomedical, natural and social sciences." DOI: 10.1371/journal.pone.0185578Table 1. Explained and cumulative variance for each axisTable 2. Relative contributions of the factor to the element for disciplines (expressed as a percentage)Table 3. Number of papers indexed in WoS (all and with funding acknowledgements) and percentage of papers with funding acknowledgements, by discipline (2015)For the purposes of the analysis presented in Fig 1 and 2, the dataset was partitioned by discipline and a Correspondence Analysis was applied to these subsets and using a MATLAB program.Fig 1. Bidimensional Correspondence Analysis for acknowledgements patterns by discipline (plane 1-2)Fig 2. Bidimensional Correspondence Analysis for acknowledgements patterns by discipline (plane 3-4).Supporting Information:S1 Fig. Frequency distribution of noun phrases found in acknowledgementsS1 Table. Frequency of the 214 most frequent noun phrases, by disciplineS2 Table. Quality of representation of the rows (cumulative contribution for each NP)S3 Table. Quality of representation of the columns (cumulative contribution for each discipline)