This document discusses various methods for presenting statistical data, including tabulation and drawing. It describes tabulation methods like simple tables and frequency distribution tables that can include qualitative or quantitative data. For drawings, it explains different types of graphs that can be used for continuous variables, like histograms, frequency polygons, and cumulative frequency diagrams. It also covers diagrams for discrete variables, such as bar diagrams, pie charts, pictograms, and map diagrams. The document provides examples and guidelines for constructing and interpreting these different data presentation methods.
The document discusses principles and methods for presenting data, including:
- Data should be arranged to interest readers while maintaining important details simply.
- The two basic methods are tabulation and charts/diagrams.
- Tabulation rules include numbering, titling, defining headings clearly.
- Common charts are pie charts, bar diagrams, histograms, frequency polygons, and scatter diagrams. Each has strengths for different data types.
Data:
A set of values recorded on one or more observational units i.e. Object, person etc
Types of data:
Qualitative/ Quantitative data
Discrete/ Continuous data
Primary/ Secondary data
Nominal/ Ordinal data
This document discusses principles and methods for presenting data. It outlines that data should be arranged concisely to arouse interest while retaining important details. The two basic methods of presentation are tabulation and charts/diagrams. Tabulation involves organizing data in tables which should be clearly labeled and structured. Charts and diagrams provide visual summaries and allow comparisons, though some detail is lost. Common types include bar charts, histograms, scatter plots and cumulative frequency diagrams. Proper formatting and scaling is important to effectively convey patterns and relationships in the data.
This document provides an overview of key concepts in research methodology and biostatistics. It discusses statistics, biostatistics, research methods, descriptive statistics, variables, and types of variables with respect to measurement. Specifically, it defines statistics as the organization, summarization, and analysis of quantitative data to communicate their meaning. It also defines biostatistics as the application of statistics to topics in biology, especially related to medicine.
This document provides information on various statistical and research methodology concepts used in rehabilitation sciences. It defines key terms like independent and dependent variables, types of variables, measures of central tendency including mean, median and mode, and how to represent data through tables, graphs and distributions. Descriptive statistics are used to characterize data through these various analytical tools in order to summarize populations and samples.
This document discusses different types of data and methods for presenting data. It describes qualitative and quantitative data, discrete and continuous data, and primary and secondary data. It also covers nominal and ordinal data. Common methods for presenting data include tabulation, bar charts, histograms, frequency polygons, cumulative frequency diagrams, scatter diagrams, line diagrams, and pie charts. The document provides guidelines for constructing tables and various chart types to clearly present data in a way that facilitates analysis and understanding.
This document discusses different types of data and methods for presenting data. It describes qualitative and quantitative data, discrete and continuous data, and primary and secondary data. It also covers nominal and ordinal data. Common methods for presenting data include tabulation and various charts or diagrams. Tabulation involves organizing data into tables, following specific rules. Charts allow visualization of data and include bar charts, histograms, frequency polygons, cumulative frequency diagrams, scatter diagrams, line diagrams, and pie charts. Each chart has specific purposes and guidelines for effective presentation of data.
biostatstics :Type and presentation of datanaresh gill
The document provides an overview of different types of data and methods for presenting data. It discusses qualitative vs quantitative data, primary vs secondary data, and different ways to present data visually including bar charts, histograms, frequency polygons, scatter diagrams, line diagrams and pie charts. Guidelines are provided for tabular presentation of data to make it clear, concise and easy to understand.
The document discusses principles and methods for presenting data, including:
- Data should be arranged to interest readers while maintaining important details simply.
- The two basic methods are tabulation and charts/diagrams.
- Tabulation rules include numbering, titling, defining headings clearly.
- Common charts are pie charts, bar diagrams, histograms, frequency polygons, and scatter diagrams. Each has strengths for different data types.
Data:
A set of values recorded on one or more observational units i.e. Object, person etc
Types of data:
Qualitative/ Quantitative data
Discrete/ Continuous data
Primary/ Secondary data
Nominal/ Ordinal data
This document discusses principles and methods for presenting data. It outlines that data should be arranged concisely to arouse interest while retaining important details. The two basic methods of presentation are tabulation and charts/diagrams. Tabulation involves organizing data in tables which should be clearly labeled and structured. Charts and diagrams provide visual summaries and allow comparisons, though some detail is lost. Common types include bar charts, histograms, scatter plots and cumulative frequency diagrams. Proper formatting and scaling is important to effectively convey patterns and relationships in the data.
This document provides an overview of key concepts in research methodology and biostatistics. It discusses statistics, biostatistics, research methods, descriptive statistics, variables, and types of variables with respect to measurement. Specifically, it defines statistics as the organization, summarization, and analysis of quantitative data to communicate their meaning. It also defines biostatistics as the application of statistics to topics in biology, especially related to medicine.
This document provides information on various statistical and research methodology concepts used in rehabilitation sciences. It defines key terms like independent and dependent variables, types of variables, measures of central tendency including mean, median and mode, and how to represent data through tables, graphs and distributions. Descriptive statistics are used to characterize data through these various analytical tools in order to summarize populations and samples.
This document discusses different types of data and methods for presenting data. It describes qualitative and quantitative data, discrete and continuous data, and primary and secondary data. It also covers nominal and ordinal data. Common methods for presenting data include tabulation, bar charts, histograms, frequency polygons, cumulative frequency diagrams, scatter diagrams, line diagrams, and pie charts. The document provides guidelines for constructing tables and various chart types to clearly present data in a way that facilitates analysis and understanding.
This document discusses different types of data and methods for presenting data. It describes qualitative and quantitative data, discrete and continuous data, and primary and secondary data. It also covers nominal and ordinal data. Common methods for presenting data include tabulation and various charts or diagrams. Tabulation involves organizing data into tables, following specific rules. Charts allow visualization of data and include bar charts, histograms, frequency polygons, cumulative frequency diagrams, scatter diagrams, line diagrams, and pie charts. Each chart has specific purposes and guidelines for effective presentation of data.
biostatstics :Type and presentation of datanaresh gill
The document provides an overview of different types of data and methods for presenting data. It discusses qualitative vs quantitative data, primary vs secondary data, and different ways to present data visually including bar charts, histograms, frequency polygons, scatter diagrams, line diagrams and pie charts. Guidelines are provided for tabular presentation of data to make it clear, concise and easy to understand.
Frequency distribution, types of frequency distribution.
Ungrouped frequency distribution
Grouped frequency distribution
Cumulative frequency distribution
Relative frequency distribution
Relative cumulative frequency distribution
Graphical representation of frequency distribution
I. Representation of Grouped data
1.Line graphs
2.Bar diagrams
a) Simple bar diagram
b)Multiple/Grouped bar diagram
c)Sub-divided bar diagram.
d) % bar diagram
3. Pie charts
4.Pictogram
II. Graphical representation of ungrouped data
1, Histogram
2.Frequency polygon
3.Cumulative change diagram
4. Proportional change diagram
5. Ratio diagram
This document provides an overview of biostatistics. It defines biostatistics and discusses topics like data collection, presentation through tables and charts, measures of central tendency and dispersion, sampling, tests of significance, and applications in various medical fields. The key areas covered include defining variables and parameters, common statistical terms, sources of data collection, methods of presenting data through tabulation and diagrams, analyzing data through measures like mean, median, mode, range and standard deviation, sampling and related errors, significance tests, and uses of biostatistics in areas like epidemiology and clinical trials.
This document provides an overview of biostatistics. It defines biostatistics and discusses topics like data collection, presentation through tables and charts, measures of central tendency and dispersion, sampling, tests of significance, and applications of biostatistics in various medical fields. The document aims to introduce students to important biostatistical concepts and their use in research, clinical trials, epidemiology and other areas of medicine.
This document provides an overview of key concepts in biostatistics including data display and summary. It defines different types of data, variables, and statistical measures. Descriptive statistics like mean, median and mode are used to summarize central tendencies, while measures like range, variance and standard deviation describe data dispersion. Various graphs including histograms, boxplots and stem-and-leaf plots are discussed as tools for data visualization.
This document provides an introduction to statistics, defining key concepts and uses. It discusses how statistics is the science of collecting, organizing, analyzing, and interpreting numerical data. Various types of data are described including quantitative, qualitative, discrete, continuous, and different scales of measurement. Common statistical analyses like descriptive statistics, inferential statistics, and different ways of presenting data through tables and graphs are also outlined.
This document provides an overview of basic statistics concepts. It defines statistics as the science of collecting, presenting, analyzing, and reasonably interpreting data. Descriptive statistics are used to summarize and organize data through methods like tables, graphs, and descriptive values, while inferential statistics allow researchers to make general conclusions about populations based on sample data. Variables can be either categorical or quantitative, and their distributions and presentations are discussed.
- Biostatistics refers to applying statistical methods to biological and medical problems. It is also called biometrics, which means biological measurement or measurement of life.
- There are two main types of statistics: descriptive statistics which organizes and summarizes data, and inferential statistics which allows conclusions to be made from the sample data.
- Data can be qualitative like gender or eye color, or quantitative which has numerical values like age, height, weight. Quantitative data can further be interval/ratio or discrete/continuous.
- Common measures of central tendency include the mean, median and mode. Measures of variability include range, standard deviation, variance and coefficient of variation.
- Correlation describes the relationship between two variables
This document provides an overview of statistics and its uses. It discusses how statistics is the study of collecting, analyzing, and presenting data. It then describes some common uses of statistics like simplifying data, facilitating comparisons, and testing hypotheses. The document also lists some key terms in statistics and different types of data. It discusses different types of statistical analyses like descriptive statistics, inferential statistics, and frequencies distributions. Finally, it provides examples of common ways to visually represent data through tables, bar graphs, pie charts, histograms, and other diagrams.
This document provides an overview of key concepts in statistics. It discusses how statistics is used to collect, organize, summarize, present, and analyze numerical data to derive valid conclusions. It defines common statistical terminology like data, quantitative vs. qualitative data, measures of central tendency (mean, median, mode), measures of variability (range, standard deviation), the normal distribution curve, and coefficient of variation. The document also explains common statistical tests like the z-test, t-test, ANOVA, chi-square test and concepts like sensitivity and specificity. Overall, the document serves as a high-level introduction to foundational statistical methods and analyses.
This document discusses different methods for presenting data through tables and graphs. It covers descriptive statistics, types of data, purposes of data presentation, frequency distributions, relative frequency distributions, histograms, ogives, bar graphs, pie charts, and choosing the appropriate method based on the type of data. The key goals are to facilitate interpretation of data, effective communication, and displaying patterns and relationships.
General statistics, emphasis of statistics with regards to healthcare, types of stats, methods of sampling, errors in sampling, different types of tests, measures of dispersion, correlation, types of correlation
Here are the key types of statistics:
- Descriptive statistics: Used to describe and summarize the characteristics of a data set through measures like mean, median, mode, range, standard deviation, etc.
- Inferential statistics: Used to draw conclusions about a population based on a sample. It involves hypothesis testing, confidence intervals, regression analysis, etc.
- Univariate analysis: Analysis of a single variable.
- Bivariate analysis: Analysis of the relationship between two variables.
- Multivariate analysis: Analysis of the relationship between more than two variables simultaneously.
- Parametric statistics: Methods that assume the population follows a known probability distribution (e.g. normal distribution).
- Non
This document discusses statistical procedures for analyzing different types of data based on their structure. It describes three basic data structures: 1) a single group with one score per participant, 2) a single group with multiple variables measured per participant, and 3) multiple groups with scores measuring the same variable. For each data structure, it provides examples of descriptive and inferential statistics that can be used based on the scale of measurement (nominal, ordinal, interval/ratio).
This document provides an overview of biostatistics and research methodology. It defines key statistical terms and concepts, describes methods of data collection and presentation, discusses sampling and different sampling methods, and outlines the steps in research including defining a problem, developing objectives and hypotheses, collecting and analyzing data, and interpreting results. Common statistical analyses covered include measures of central tendency, dispersion, significance testing, correlation, and regression.
Data presentationasddfffsfghgdhjdsja.pptxEmma910932
The document provides information on different methods for presenting epidemiological data, including:
- Line listings display individual case data in a table with rows and columns. Each row represents one case and columns contain variables like age, sex, symptoms.
- Frequency distributions summarize categorical variable values and frequencies in tables or graphs. One-way tables show frequencies of one variable's categories. Two-way tables cross-tabulate two variables.
- Graphs like histograms, bar charts, and line graphs can visually display patterns in the data. Histograms plot frequencies against variable values to show distributions. Line graphs depict trends over time.
Integrating Ayurveda into Parkinson’s Management: A Holistic ApproachAyurveda ForAll
Explore the benefits of combining Ayurveda with conventional Parkinson's treatments. Learn how a holistic approach can manage symptoms, enhance well-being, and balance body energies. Discover the steps to safely integrate Ayurvedic practices into your Parkinson’s care plan, including expert guidance on diet, herbal remedies, and lifestyle modifications.
Frequency distribution, types of frequency distribution.
Ungrouped frequency distribution
Grouped frequency distribution
Cumulative frequency distribution
Relative frequency distribution
Relative cumulative frequency distribution
Graphical representation of frequency distribution
I. Representation of Grouped data
1.Line graphs
2.Bar diagrams
a) Simple bar diagram
b)Multiple/Grouped bar diagram
c)Sub-divided bar diagram.
d) % bar diagram
3. Pie charts
4.Pictogram
II. Graphical representation of ungrouped data
1, Histogram
2.Frequency polygon
3.Cumulative change diagram
4. Proportional change diagram
5. Ratio diagram
This document provides an overview of biostatistics. It defines biostatistics and discusses topics like data collection, presentation through tables and charts, measures of central tendency and dispersion, sampling, tests of significance, and applications in various medical fields. The key areas covered include defining variables and parameters, common statistical terms, sources of data collection, methods of presenting data through tabulation and diagrams, analyzing data through measures like mean, median, mode, range and standard deviation, sampling and related errors, significance tests, and uses of biostatistics in areas like epidemiology and clinical trials.
This document provides an overview of biostatistics. It defines biostatistics and discusses topics like data collection, presentation through tables and charts, measures of central tendency and dispersion, sampling, tests of significance, and applications of biostatistics in various medical fields. The document aims to introduce students to important biostatistical concepts and their use in research, clinical trials, epidemiology and other areas of medicine.
This document provides an overview of key concepts in biostatistics including data display and summary. It defines different types of data, variables, and statistical measures. Descriptive statistics like mean, median and mode are used to summarize central tendencies, while measures like range, variance and standard deviation describe data dispersion. Various graphs including histograms, boxplots and stem-and-leaf plots are discussed as tools for data visualization.
This document provides an introduction to statistics, defining key concepts and uses. It discusses how statistics is the science of collecting, organizing, analyzing, and interpreting numerical data. Various types of data are described including quantitative, qualitative, discrete, continuous, and different scales of measurement. Common statistical analyses like descriptive statistics, inferential statistics, and different ways of presenting data through tables and graphs are also outlined.
This document provides an overview of basic statistics concepts. It defines statistics as the science of collecting, presenting, analyzing, and reasonably interpreting data. Descriptive statistics are used to summarize and organize data through methods like tables, graphs, and descriptive values, while inferential statistics allow researchers to make general conclusions about populations based on sample data. Variables can be either categorical or quantitative, and their distributions and presentations are discussed.
- Biostatistics refers to applying statistical methods to biological and medical problems. It is also called biometrics, which means biological measurement or measurement of life.
- There are two main types of statistics: descriptive statistics which organizes and summarizes data, and inferential statistics which allows conclusions to be made from the sample data.
- Data can be qualitative like gender or eye color, or quantitative which has numerical values like age, height, weight. Quantitative data can further be interval/ratio or discrete/continuous.
- Common measures of central tendency include the mean, median and mode. Measures of variability include range, standard deviation, variance and coefficient of variation.
- Correlation describes the relationship between two variables
This document provides an overview of statistics and its uses. It discusses how statistics is the study of collecting, analyzing, and presenting data. It then describes some common uses of statistics like simplifying data, facilitating comparisons, and testing hypotheses. The document also lists some key terms in statistics and different types of data. It discusses different types of statistical analyses like descriptive statistics, inferential statistics, and frequencies distributions. Finally, it provides examples of common ways to visually represent data through tables, bar graphs, pie charts, histograms, and other diagrams.
This document provides an overview of key concepts in statistics. It discusses how statistics is used to collect, organize, summarize, present, and analyze numerical data to derive valid conclusions. It defines common statistical terminology like data, quantitative vs. qualitative data, measures of central tendency (mean, median, mode), measures of variability (range, standard deviation), the normal distribution curve, and coefficient of variation. The document also explains common statistical tests like the z-test, t-test, ANOVA, chi-square test and concepts like sensitivity and specificity. Overall, the document serves as a high-level introduction to foundational statistical methods and analyses.
This document discusses different methods for presenting data through tables and graphs. It covers descriptive statistics, types of data, purposes of data presentation, frequency distributions, relative frequency distributions, histograms, ogives, bar graphs, pie charts, and choosing the appropriate method based on the type of data. The key goals are to facilitate interpretation of data, effective communication, and displaying patterns and relationships.
General statistics, emphasis of statistics with regards to healthcare, types of stats, methods of sampling, errors in sampling, different types of tests, measures of dispersion, correlation, types of correlation
Here are the key types of statistics:
- Descriptive statistics: Used to describe and summarize the characteristics of a data set through measures like mean, median, mode, range, standard deviation, etc.
- Inferential statistics: Used to draw conclusions about a population based on a sample. It involves hypothesis testing, confidence intervals, regression analysis, etc.
- Univariate analysis: Analysis of a single variable.
- Bivariate analysis: Analysis of the relationship between two variables.
- Multivariate analysis: Analysis of the relationship between more than two variables simultaneously.
- Parametric statistics: Methods that assume the population follows a known probability distribution (e.g. normal distribution).
- Non
This document discusses statistical procedures for analyzing different types of data based on their structure. It describes three basic data structures: 1) a single group with one score per participant, 2) a single group with multiple variables measured per participant, and 3) multiple groups with scores measuring the same variable. For each data structure, it provides examples of descriptive and inferential statistics that can be used based on the scale of measurement (nominal, ordinal, interval/ratio).
This document provides an overview of biostatistics and research methodology. It defines key statistical terms and concepts, describes methods of data collection and presentation, discusses sampling and different sampling methods, and outlines the steps in research including defining a problem, developing objectives and hypotheses, collecting and analyzing data, and interpreting results. Common statistical analyses covered include measures of central tendency, dispersion, significance testing, correlation, and regression.
Data presentationasddfffsfghgdhjdsja.pptxEmma910932
The document provides information on different methods for presenting epidemiological data, including:
- Line listings display individual case data in a table with rows and columns. Each row represents one case and columns contain variables like age, sex, symptoms.
- Frequency distributions summarize categorical variable values and frequencies in tables or graphs. One-way tables show frequencies of one variable's categories. Two-way tables cross-tabulate two variables.
- Graphs like histograms, bar charts, and line graphs can visually display patterns in the data. Histograms plot frequencies against variable values to show distributions. Line graphs depict trends over time.
Integrating Ayurveda into Parkinson’s Management: A Holistic ApproachAyurveda ForAll
Explore the benefits of combining Ayurveda with conventional Parkinson's treatments. Learn how a holistic approach can manage symptoms, enhance well-being, and balance body energies. Discover the steps to safely integrate Ayurvedic practices into your Parkinson’s care plan, including expert guidance on diet, herbal remedies, and lifestyle modifications.
Local Advanced Lung Cancer: Artificial Intelligence, Synergetics, Complex Sys...Oleg Kshivets
Overall life span (LS) was 1671.7±1721.6 days and cumulative 5YS reached 62.4%, 10 years – 50.4%, 20 years – 44.6%. 94 LCP lived more than 5 years without cancer (LS=2958.6±1723.6 days), 22 – more than 10 years (LS=5571±1841.8 days). 67 LCP died because of LC (LS=471.9±344 days). AT significantly improved 5YS (68% vs. 53.7%) (P=0.028 by log-rank test). Cox modeling displayed that 5YS of LCP significantly depended on: N0-N12, T3-4, blood cell circuit, cell ratio factors (ratio between cancer cells-CC and blood cells subpopulations), LC cell dynamics, recalcification time, heparin tolerance, prothrombin index, protein, AT, procedure type (P=0.000-0.031). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and N0-12 (rank=1), thrombocytes/CC (rank=2), segmented neutrophils/CC (3), eosinophils/CC (4), erythrocytes/CC (5), healthy cells/CC (6), lymphocytes/CC (7), stick neutrophils/CC (8), leucocytes/CC (9), monocytes/CC (10). Correct prediction of 5YS was 100% by neural networks computing (error=0.000; area under ROC curve=1.0).
TEST BANK For Community Health Nursing A Canadian Perspective, 5th Edition by...Donc Test
TEST BANK For Community Health Nursing A Canadian Perspective, 5th Edition by Stamler, Verified Chapters 1 - 33, Complete Newest Version Community Health Nursing A Canadian Perspective, 5th Edition by Stamler, Verified Chapters 1 - 33, Complete Newest Version Community Health Nursing A Canadian Perspective, 5th Edition by Stamler Community Health Nursing A Canadian Perspective, 5th Edition TEST BANK by Stamler Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Pdf Chapters Download Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Pdf Download Stuvia Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Study Guide Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Ebook Download Stuvia Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Questions and Answers Quizlet Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Studocu Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Quizlet Test Bank For Community Health Nursing A Canadian Perspective, 5th Edition Stuvia Community Health Nursing A Canadian Perspective, 5th Edition Pdf Chapters Download Community Health Nursing A Canadian Perspective, 5th Edition Pdf Download Course Hero Community Health Nursing A Canadian Perspective, 5th Edition Answers Quizlet Community Health Nursing A Canadian Perspective, 5th Edition Ebook Download Course hero Community Health Nursing A Canadian Perspective, 5th Edition Questions and Answers Community Health Nursing A Canadian Perspective, 5th Edition Studocu Community Health Nursing A Canadian Perspective, 5th Edition Quizlet Community Health Nursing A Canadian Perspective, 5th Edition Stuvia Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Pdf Chapters Download Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Pdf Download Stuvia Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Study Guide Questions and Answers Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Ebook Download Stuvia Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Questions Quizlet Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Studocu Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Quizlet Community Health Nursing A Canadian Perspective, 5th Edition Test Bank Stuvia
Histololgy of Female Reproductive System.pptxAyeshaZaid1
Dive into an in-depth exploration of the histological structure of female reproductive system with this comprehensive lecture. Presented by Dr. Ayesha Irfan, Assistant Professor of Anatomy, this presentation covers the Gross anatomy and functional histology of the female reproductive organs. Ideal for students, educators, and anyone interested in medical science, this lecture provides clear explanations, detailed diagrams, and valuable insights into female reproductive system. Enhance your knowledge and understanding of this essential aspect of human biology.
Rasamanikya is a excellent preparation in the field of Rasashastra, it is used in various Kushtha Roga, Shwasa, Vicharchika, Bhagandara, Vatarakta, and Phiranga Roga. In this article Preparation& Comparative analytical profile for both Formulationon i.e Rasamanikya prepared by Kushmanda swarasa & Churnodhaka Shodita Haratala. The study aims to provide insights into the comparative efficacy and analytical aspects of these formulations for enhanced therapeutic outcomes.
8 Surprising Reasons To Meditate 40 Minutes A Day That Can Change Your Life.pptxHolistified Wellness
We’re talking about Vedic Meditation, a form of meditation that has been around for at least 5,000 years. Back then, the people who lived in the Indus Valley, now known as India and Pakistan, practised meditation as a fundamental part of daily life. This knowledge that has given us yoga and Ayurveda, was known as Veda, hence the name Vedic. And though there are some written records, the practice has been passed down verbally from generation to generation.
Adhd Medication Shortage Uk - trinexpharmacy.comreignlana06
The UK is currently facing a Adhd Medication Shortage Uk, which has left many patients and their families grappling with uncertainty and frustration. ADHD, or Attention Deficit Hyperactivity Disorder, is a chronic condition that requires consistent medication to manage effectively. This shortage has highlighted the critical role these medications play in the daily lives of those affected by ADHD. Contact : +1 (747) 209 – 3649 E-mail : sales@trinexpharmacy.com
Muktapishti is a traditional Ayurvedic preparation made from Shoditha Mukta (Purified Pearl), is believed to help regulate thyroid function and reduce symptoms of hyperthyroidism due to its cooling and balancing properties. Clinical evidence on its efficacy remains limited, necessitating further research to validate its therapeutic benefits.
Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
1. Methods of Presentation of Data
Dr. Buddhi Krishna Shrestha
1st year Resident
Internal Medicine, NAMS
2. • The main sources for collection of medical
statistics are:
1. Experiments
2. Surveys
3. Records.
3. The statistical data obtained from the above
sources can be divided into two broad
categories:
1. Qualitative (or Discrete) Data
• No notion of magnitude or size of the characteristic or
attribute
• Only one variable
• Example: attacked, escaped, died, cured etc
2. Quantitative (or Continuous) Data
• The quantitative data have a magnitude.
• The characteristics is measured either on an interval or on a
ratio scale.
• There are two variables—the characteristics and the
frequency.
• Example: Height, weight, blood pressure etc
4. Principles of Presentation of Data
• Data should be arranged in such a way that it sparks interest in
reader
• Data should be made sufficiently concise without losing
important details
• Data should be presented in simple form to enable the reader
to form quick impressions and to draw some conclusion,
directly or indirectly.
5. • Should facilitate further statistical analysis
• Should define the problem and suggest its solution
6. METHODS OF PRESENTATION
• The first step in statistical analysis is to present the data in an
easy way to be understood.
• There are two main methods of presenting frequencies of a
variable character or a variable.
1.Tabulation
2.Drawing
7. Rules and guidelines for Tabular
Presentation
1. Table must be numbered
2. Brief and self explanatory title must be given to each table
3. The heading of columns and rows must be clear, sufficient, concise
and fully defined.
4. The data must be presented according to size of importance,
chronologically, alphabetically or geographically
5. If data includes rate or proportion, mention the denominator
6. Figures needing comparison should be placed as close as possible
8.
9. Tabulation
• Tabulation are devices for presenting data
from a mass of statistical data.
• Preparation of frequency distribution table is
the first requirement.
• Can be simple or complex depending upon the
number of measurements of single set or
multiple sets of items
10. Simple table
Diseases Cases
Hypertension 25000
Diabetes Mellitus 38000
Cancer 2000
Total 65000
Number of Cases of Non Communicable
diseases in XYZ Hospital in 2019
11. Frequency Distribution Table with
Qualitative Data
Cases of COVID-19 in adults and children in the
months of July and August 2020 in XYZ Hospital
12. Frequency Distribution Table with
Quantitative Data
Systolic Blood Pressure Level in Hypertensive Patients at the
diagnosis
13. • After classwise or groupwise tabulation, the
frequencies of a characteristic can be presented by
two kinds of drawings: Graphs and diagrams.
• Presentation of quantitative, continuous or
measured data is through graphs
• Presentation of qualitative, discrete or counted data
is through diagrams.
14. Presentation of quantitative, continuous or
measured data is through graphs.
The common graphs in use are:
•Histogram
•Frequency polygon
•Frequency curve
•Line chart or graph
•Cumulative frequency diagram
•Scatter or dot diagram.
15. Presentation of qualitative, discrete or
counted data is through diagrams.
The common diagrams in use are:
•Bar diagram
•Pie or sector diagram
•Pictogram or picture diagram
•Map diagram or spot map.
16. • Used for Qualitative, Continuous, Variables
• It is used to present variables which have no gaps eg.
age, weight, height, blood pressure, blood sugar etc.
• It consist of a series of blocks
• The class intervals are given along horizontal axis and
the frequency along the vertical axis
Histogram
18. • Frequency Polygon is an area diagram of frequency
distribution over a histogram
• It is a linear representation of a frequency table and
histogram, obtained by joining the mid points of the
histogram blocks
• Frequency is plotted at the central point of a group
Frequency Polygon
21. • Here the frequency of data in each category
represents the sum of data from the category and
the preceding categories
• Cumulative frequencies are plotted opposite the
group limits of the variable
• These points are joined by smooth free hand curve
to get a cumulative frequency diagram or Ogive
Cumulative Frequency Diagram or O'give
22.
23. • Also called the correlation diagram, it is useful to represent
the relationship between two numeric measurements, each
observation being represented by a point corresponding to
its value on each axis
• In negative correlation, the points will be scattered in
downward direction, meaning that the relation between the
two studied measurements is controversial i.e. if one
measure increases the other decreases. While in positive
correlation, the points will be scattered in upward direction.
Scatter/ Dot diagram
Scatter/ Dot
diagram
26. • It is diagram showing the relationship between two
numeric variables ( as the scatter) but the points are joined
together to form a line ( either broken line or a smooth
curve)
• Used to show the trend of events with the passage of time
Line Diagram
27. Time to achieve maximum antiplatelet effect with 75 mg
aspirin/day. It is maximum in first day then declines unless
taken next day again
28. Bar Diagram
• Widely used, easy to prepare tool for comparing categories of
mutually exclusive discrete data
• Different categories are indicated on one axis and frequency
of data in each category on another axis
• Length of the bar indicate the magnitude of the frequency of
the character to be compared
29. • Spacing between various bar should be
equal to half of the width of the bar
• 3 types of Bar diagram:
1. Simple Multiple
2. Compound Component
3. Proportional
31. • Each observation has more than one value,
represented by a group of bars
• Percentage of males and females in different
countries, percentage of deaths from heart diseases
in old and young age, mode of delivery ( caserean
or vaginal) in different female age groups
Multiple Diagram
33. • Subdivision of a single bar to indicate the composition of
the total divided into sections according to their relative
proportion
• For example two communities are compared in their
proportion of energy obtained from various food stuff,
each bar represents energy intake by one community,the
height of the bar is 100, it is divided horizontally into 3
components ( Protein, Fat and Carbohydrate) of diet, each
component is represented by different color or shape
Proportional or component bar
diagram
34.
35. • Consist of a circle whose area represents the total
frequency (100%) which is divided into segments
• Each segment represents a proportional composition of
the total frequency
Pie diagram
36. Pie diagram showing alcohol drinking status of 391 alcohol
drinkingsubjects under medical and ocular history of
Bhaktapur (Nepal) vision impairmentand glaucoma study.
37. Pictogram or Picture Diagram
• It is a popular method to impress the
frequency of the occurrence of events to
common man such as attacks, deaths, number
operated, admitted, discharged, accidents,
etc. in a population.
38. AIDS in developing and industrially developed countries. The
burden of disease caused by HIV
infection is clear.
39. Map Diagram or Spot Map
• These maps are prepared to show geographical
distribution of frequencies of characteristic.
Nos. 1 and 2 are specially applied to generate data needed for specific purposes while the records provide ready-made data for routine and continuous information.
In most of the studies, the information is collected in large quantity and the data should be classified and presented in the form of a frequency distribution table as shown in Tables 2.1 and 2.3. This is a very important step in statistical analysis. It groups large number of series or observations of master table and presents the data very concisely, giving all information at a glance. All the frequencies considered together form the frequency distribution