One phase association fit curves for pattern curve data. Axes represent association between mean fluorescence (MFI) values obtained for different know cytokine with results expressed in pg/ml (y axis). Y=Y0 + (Plateau-Y0)*(1-exp.(-K*x)) equation was used for calculating cytokine concentration for patient samples. Figure S2. High producer frequencies for Lown stratified Chagasic patients. Bar graph shows the percentage of high producers for the different cytokines studied for Lown clinical scoring. Control high producer’s percentages is showed as dotted line and Chagasic frequencies (high & low SD risk) as bars. Threshold to determining high producers was calculated on ROC curve values for patients included in this group. Figure S3. High producer frequencies for blood pressure stratified Chagasic patients. Bar graph shows the percentage of high producers for the different cytokines studied for blood pressure clinical scoring. Control high producer’s percentages is showed as dotted line and Chagasic frequencies (normotensive & hypertensive) as bars. Threshold to determining high producers was calculated on ROC curve values for patients included in this group. Figure S3. High producer frequencies for amiodarone treatment stratified Chagasic patients. Bar graph shows the percentage of high producers for the different cytokines studied for blood pressure clinical scoring. Control high producer’s percentages is showed as dotted line and Chagasic frequencies (untreated & treated) as bars. Threshold to determining high producers was calculated on ROC curve values for patients included in this group. (XLSX 980 kb)

Figure S5. Bar graphs showing the amplification (red) and deletion frequency of somatic copy number variation of FRGs in the TCGA-SKCM cohort. The height of each bar represents the alteration frequency.

This bar chart displays the frequency at which French having a Twitter account used the social media in 2019. It appears that ** percent of the respondents stated using Twitter daily or almost daily, while ***** percent never used the social network.

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.

This table provides means and 95% confidence intervals that were used to generate Figs 1B, 2B, and 2D. (XLSX)

Welcome folks, and thanks for choosing this dataset. In this dataset, you will be finding a lot of useful stuff which enhances your excel skills and google sheets skills. In this dataset you will be having 2300 rows of data in which you will find all top 100 singers and their songs with their ranking every year Tasks using his data set all you need to do is obtain the top 5 % singers and make the bar graph using their scores to find the scores of each singer use the below formula 101-ranking of the singer and using this scores draw the bar graph Task2 In this, you need to draw the graph based on the frequency of the singers who appeared more than or equal to 15 times from 1992 to 2014 based on this frequency draw another graph and see the changes & feel free to post your queries in the discussion pace and try to post your answers too

bye bye...

A stacked bar chart illustrating how frequently teams use AI SEO for various marketing tasks.

This bar chart presents the frequency at which French respondents stated consuming fish per week in a survey from January 2019. It appears that ** percent of the surveyed never ate fish, while ** percent stated consuming fish once to twice a week.

Supplementary Material 1. Supplementary figure S1: median RSCU values of the Sf14 and S. flexneri 2457 T genomes Stacked bar graph comparing the median RSCU values of each codon in Sf14 and host S. flexneri 2457T. Asterisks indicate the difference in RSCU is significant. Supplementary figure S2: average RSCU values for the S. flexneri 2457 T genome and all gene groups of interest Bar graphs showing the average RSCU values for all codons in 2457 T for (A) genome, (B) metabolism genes, (C) biosynthesis genes, (D) gene expression genes, (E) stress response and drug resistance genes, and (F) transport and miscellaneous genes. Supplementary figure S3: average RSCU values for Sf14 genome and all Sf14 gene groups Bar graphs showing the average RSCU values for all codons in Sf14 (A) genome, (B) early genes, (C) middle genes, (D) late genes, and (E) hypothetical genes. Supplementary figure S4: PCA plot of RSCU scores for all codons in the Sf14 and S. flexneri 2457 T gene groups Genes are assigned a color based on their group, as indicated in the key on the right. An unlabeled version of this plot is available in the text (Fig. 4). Some labels are hidden due to multiple overlapping observations. Supplementary figure S5: tAI values for both Sf14 and S. flexneri 2457 T genomes and host genes of interest Bar graphs depicting the tAI values for the (A) host genome, (B) host genes of interest, (C) Sf14 genome; (D) Boxplot of the distribution of tAI values in S. flexneri 2457 T and Sf14. Supplementary figure S6: Sf14-encoded tRNA mutation frequency by isotype and position (A) Bar graph of the mutation frequency for Sf14 tRNAs according to their isotype. (B) Bar graph of the frequency of mutations at each anticodon loop position. Supplementary figure S7: comparison of tRNA ∆G values between Sf14 and S. flexneri 2457 T Free energy (∆G) values for Sf14-encoded tRNAs and their counterparts in 2457T. Sf14 ∆G values are on the y-axis, and 2457 T ∆G values are on the x-axis. Supplementary figure S8: ∆∆G values (Sf14 – S. flexneri 2457 T) and the impact of mutation position on ∆∆G Dot plot of the mutation position (x-axis) of Sf14 mutated tRNAs as it relates to ∆∆G values (y-axis). Position “0” indicates no mutations were found. Supplementary figure S9: secondary structure of four representative mutated Sf14-encoded tRNAs Four Sf14-encoded tRNAs were modeled and compared to their host counterparts, with mutations in the anticodon loops highlighted in yellow. The tRNAs modeled were (A) tRNA-Leu-CAA, (B) tRNA-Leu-TAG, (C) tRNA-Phe-GAA, and (D) tRNA-Gln-TTG.

Figure S6. Unidimensional bar graphs of medication class prescribing frequency by region. Bar graphs of each of the top 10 medication classes prescribed (by percentage of individual prescriber prescriptions) for each of 24 medical specialty groupings, plotted for each of 10 Federal Regions. Note that drug class prescribing percentages are mean levels, and truncated at 21% to make the visualizations informative. (ZIP 5280 kb)

Introduction

The high quality singers have to be identified based on the rankings given by Billboard. The top 100 rankings data is available on Wikipedia from 1992 to 2014.

Our project goal is to fetch this data from Wikipedia, load it into spreadsheets and create a data set. On this dataset apply a model to identify the high quality singers.

Project Description

High quality singers were drawn based on the following :

• Prolific singers-1" with column names Artist and Frequency with data number of times a singer appears in the top 100 over the given time period.
• Prolific singers-2" with column names Artist and Score of the Artist.

Metrics 1 and Metrics 2 have the same 1 and 2 positions. Next positions have drastically changed. This is because, even if the frequency of singers is high, their score i.e. the sum of their ranks, appearing over years is low. That means prolific singers in metrics 2 are calculated using a qualitative approach whereas metrics 1 is a quantitative one.

Images

• Bar-plot of the most prolific singers. Singers on this chart should have appeared at least more than 15 times
• Bar graph has been plotted by selecting the top 5 percentile of the data.

Conclusion So we say that a singer who appeared once at a higher and once at a lower ranking is a "higher quality" singer than one who appeared twice at a lower ranking.

This bar chart presents the frequency at which French respondents stated consuming fruits per week in a survey from January 2019. It appears that a large majority of the surveyed ate fruits more than ***** times a week, and ** percent consumed fruits *** to ***** times a week.

Sea ice amphipod (macrofauna) distribution and abundance across the Arctic aggregated from 47 sources between 1977 and 2012 by the CBMP Sea Ice Biota Expert Network. Bar graphs illustrate the frequency of occurrence (%) of amphipods in samples that contained at least one ice-associated amphipod. Red circles illustrate the total abundances of all ice-associated amphipods in quantitative samples (individuals m-2) at locations of sampling for each Arctic Marine Area (AMA). Number of sampling efforts for each region is given in parenthesis after region name. Blue dots represent samples where only presence/ absence data were available and where amphipods were present.

STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - Chapter 3 - Page 44 - Figure 3.1.6

From the report draft: "This summary includes 47 data sources of under-ice amphipods published between 1977 and 2012. When available, we collected information on abundance or density (ind. m-2, or ind. m-3 that were converted to ind. m-2) and biomass (g m-2, wet weight). If abundance or biomass data were not available, we examined presence/relative abundance information. Frequency of occurrence was calculated for regions across the Arctic using integrated data for all available years."

Article title should theoretically reflect the content to which it represents. But, there is a paucity of quantitative methods for measuring the relevance of a title to an article’s content. One approach for quantifying the relevance of a title with respect to an article’s content is through mining the frequency of occurrence of words in the article title in the main text of the article. Profiling of high frequency of occurrence of many words in the title would suggests higher degree of relevance of the title with respect to the article’s content. But this must be taken in light of the caveat that some titles are phrased to express the conclusion of a paper, and would thus suffer from a lower frequency of occurrence of words of the title in the main text. This work reports a dataset that has been obtained through analysing the frequency of occurrence of words in the title of journal article in the main text of the article. This process is aided by an in-house MATLAB software coded for the project. Articles from different fields of biology such as biochemistry, bioinformatics, biotechnology, cell biology, computational biology, genetics, genomics, microbiology, molecular biology, structural biology, synthetic biology, and systems biology were put through the same analysis, and an Excel file detailing the frequency of occurrence of words in the title of the article serve as the output. The data is also visualized as a bar chart in the same Excel file.

This bar chart shows how often French people visited a hair salon in France in 2018. We learn that more than ******* of the respondents went to the hairdresser once every *** months.

The prevalence bulletin contains bar charts of overall prevalence in Northern Ireland for the 20 Quality & Outcomes Framework registers which count patients with specific conditions or diseases. The report also contains frequency distribution charts for each register.

Source agency: Health, Social Service and Public Safety (Northern Ireland)

Designation: Official Statistics not designated as National Statistics

Language: English

Alternative title: NI GMS QOF Prevalence Bulletin

This bar chart shows the frequency at which the French renewed their skis or snowboards in 2016. Nearly ** percent of those questioned bought new skis or a new snowboard every three years, compared to five percent who bought new equipment once a year.

This bar chart presents the frequency at which French respondents stated consuming eggs per week in a survey from January 2019. It appears that ***** percent of the surveyed never ate eggs, while ** percent stated consuming eggs once to twice a week.

Original responses in sheets. (XLSX)

This bar chart presents the percentage of French people who flew in the last twelve months for professional reasons in 2019, by frequency of flights. It appears that **** percent of respondents who went on business trips used airplane travel * to * times.