Introduction to Mode.
Calculation of modes by different methods.
Merits and Demerits of Mode.
Mode is the value which occurs the maximum number of times in a series of observations and has the highest frequency.
Calculation of Mode
1. Calculation of mode in a series of individual observations (Ungrouped data)
2. Calculation of mode in a discrete series (Grouped data)
3. Calculation of mode in a continuous series (Grouped data)
4. Calculation of mode in a unequal class intervals (Grouped data)
This document discusses various measures of central tendency including the mean, median, and mode. It provides definitions and formulas for calculating each measure. The mean is the average value and is calculated by summing all values and dividing by the total number of items. The median is the middle value when data is arranged in order. The mode is the value that occurs most frequently in a data set. Examples are provided to demonstrate calculating each measure for both grouped and ungrouped data. The advantages and disadvantages of each measure are also briefly discussed.
This document discusses organizing and presenting data through descriptive statistics. It describes various types of descriptive statistics including measures to condense data like frequency distributions and graphic presentations. It then provides examples and steps for creating frequency distribution tables and different types of graphs like bar charts, histograms, line graphs, scatterplots and pie charts to summarize both qualitative and quantitative data.
Introduction. Data presentation
Frequency distribution. Distribution center indicators. RMS. Covariance. Effects of
diversification. Choice of the weighing method.
More: https://ek.biem.sumdu.edu.ua/
Biostatistics and Research Methodology: Unit I - Measures of Central Tendency...Chaitali Dongaonkar
This document discusses measures of central tendency and introduces various types of means including arithmetic, geometric, and harmonic means. It provides definitions and formulas for calculating each type of mean. It also discusses how to handle both discrete and continuous data, including how to convert discrete class intervals into continuous intervals in order to calculate the mean. Examples are provided to demonstrate calculating the mean for both individual/ungrouped and grouped data.
Introduction to Mode.
Calculation of modes by different methods.
Merits and Demerits of Mode.
Mode is the value which occurs the maximum number of times in a series of observations and has the highest frequency.
Calculation of Mode
1. Calculation of mode in a series of individual observations (Ungrouped data)
2. Calculation of mode in a discrete series (Grouped data)
3. Calculation of mode in a continuous series (Grouped data)
4. Calculation of mode in a unequal class intervals (Grouped data)
This document discusses various measures of central tendency including the mean, median, and mode. It provides definitions and formulas for calculating each measure. The mean is the average value and is calculated by summing all values and dividing by the total number of items. The median is the middle value when data is arranged in order. The mode is the value that occurs most frequently in a data set. Examples are provided to demonstrate calculating each measure for both grouped and ungrouped data. The advantages and disadvantages of each measure are also briefly discussed.
This document discusses organizing and presenting data through descriptive statistics. It describes various types of descriptive statistics including measures to condense data like frequency distributions and graphic presentations. It then provides examples and steps for creating frequency distribution tables and different types of graphs like bar charts, histograms, line graphs, scatterplots and pie charts to summarize both qualitative and quantitative data.
Introduction. Data presentation
Frequency distribution. Distribution center indicators. RMS. Covariance. Effects of
diversification. Choice of the weighing method.
More: https://ek.biem.sumdu.edu.ua/
Biostatistics and Research Methodology: Unit I - Measures of Central Tendency...Chaitali Dongaonkar
This document discusses measures of central tendency and introduces various types of means including arithmetic, geometric, and harmonic means. It provides definitions and formulas for calculating each type of mean. It also discusses how to handle both discrete and continuous data, including how to convert discrete class intervals into continuous intervals in order to calculate the mean. Examples are provided to demonstrate calculating the mean for both individual/ungrouped and grouped data.
This document provides information on statistics and grouped data. It defines key terms related to frequency distribution tables, measures of central tendency, measures of dispersion, measures of position, and grouped data. For frequency distribution tables, it discusses variables, frequency, and ways to represent the data through graphs. For measures of central tendency, it defines mean, mode, median, harmonic mean and geometric mean. Measures of dispersion include variance, standard deviation, and mean deviation. Measures of position are quartiles, deciles, and percentiles. The document also discusses terms related to grouped data such as class intervals, class marks, and ways to represent grouped data.
A study on the ANOVA ANALYSIS OF VARIANCE.pptxjibinjohn140
ANOVA (analysis of variance) is a statistical method used to test if the means of three or more samples or groups are equal. It divides the total variation in a data set into variation between groups and variation within groups. An F-test is used to compare the ratio of between-group variation and within-group variation. If the F-calculated value is less than the F-critical value, the null hypothesis that the sample means are equal is accepted. ANOVA can test for differences between more than two groups which makes it more efficient than multiple t-tests.
This document provides information on measures of central tendency, including the median, mode, and mean. It defines these terms, explains how to calculate them, and discusses their advantages and disadvantages. Specifically, it explains that the median is the middle value when values are arranged in order, and the mode is the most frequently occurring value. Formulas are provided for calculating the median and mode from both individual and grouped data sets. The document also discusses different types of averages and provides examples of calculating the median and mode from various data distributions.
The document provides information and instructions for analyzing student exam score data. It includes:
1) A table of 80 exam scores ranging from 53 to 97.
2) Instructions to calculate descriptive statistics like minimum, maximum, range, and percentiles of the scores.
3) Directions to construct a frequency distribution table and histogram of the scores binned into intervals of 5.
4) A calculation of measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation) of the scores.
5) An analysis of the distribution's asymmetry and kurtosis.
This document provides an introduction to medical statistics and presenting data in tables and graphs. It discusses the main methods of data presentation including tabular, graphical, and mathematical presentation. For tabular presentation, it describes the characteristics and types of tables including simple, frequency distribution, and cumulative frequency tables. The main types of graphs covered are bar charts, histograms, frequency polygons, line diagrams, and pie charts. It also discusses measures of central tendency including mean, median and mode, as well as measures of dispersion like range, mean deviation, variance and standard deviation.
This document provides an overview of analysis of variance (ANOVA). It begins by defining parametric tests and discussing the assumptions of ANOVA. The key ideas of ANOVA are introduced, including comparing the variance between groups to the variance within groups. Calculations for one-way ANOVA are demonstrated, including sums of squares, mean squares, and the F-statistic. Examples are provided to illustrate one-way ANOVA calculations and interpretations. Violations of assumptions and extensions to two-way ANOVA are also discussed.
This document discusses frequency distributions and methods for graphically presenting frequency distribution data. It defines a frequency distribution as a tabulation or grouping of data into categories showing the number of observations in each group. The document outlines the parts of a frequency table as class limits, class size, class boundaries, and class marks. It then provides steps for constructing a frequency distribution table from a set of data. Finally, it discusses histograms and frequency polygons as methods for graphically presenting frequency distribution data, and provides examples of how to construct these graphs in Excel.
BIOSTATISTICS MEAN MEDIAN MODE SEMESTER 8 AND M PHARMACY BIOSTATISTICS.pptxPayaamvohra1
1. The document provides information about biostatistics including measures of central tendency, dispersion, correlation, and regression. It defines terms like mean, median, mode, range, and standard deviation.
2. Examples of calculating mean, median, and mode from individual data sets, grouped frequency distributions, and continuous series are shown step-by-step.
3. Parametric tests like t-test, ANOVA, and tests of significance are also introduced. Overall, the document covers fundamental concepts in biostatistics through examples.
The document discusses different methods to find the mode of a data set:
1) For ungrouped data, the mode is the value that occurs most frequently.
2) For grouped data, the mode lies within the modal class which has the highest frequency. A formula is provided to calculate the exact mode.
3) The graphical method involves drawing a histogram to identify the class with the highest bar, and using the formula and class boundaries to determine the mode.
This document discusses methods for summarizing data, including frequency distributions, measures of central tendency, and measures of dispersion. It provides examples and formulas for constructing frequency distributions and calculating the mean, median, mode, range, variance, and standard deviation. Key points covered include using frequency distributions to group data, calculating central tendency measures for grouped data, and methods for measuring dispersion both for raw data and grouped data.
The document discusses the concept of mode in statistics. It defines mode as the value that occurs most frequently in a data set. It provides different methods to calculate the mode for individual data series, discrete series, and grouped series. These include inspection methods, making discrete series, using the mean and median formula, and grouping methods. The document also outlines some merits of using mode, such as being easily understood, as well as some demerits, such as it not being based on all observations. It concludes by discussing some uses of mode and providing a reference.
These are slides I use when teaching my second year undergraduate statistics course. They are designed more for conceptual understanding, and do not have syntax for programs like SPSS or R. So it is a more conceptual and mathematical review, rather than a "how-to" computer guide.
Descriptive statistics can summarize and graphically present data. Tabular presentations display data in a grid, with tables showing frequencies of categories. Graphical presentations include bar graphs to show frequencies, pie charts to show proportions, and line graphs to show trends over time. Frequency distributions organize raw data into meaningful patterns for analysis by specifying class intervals and calculating frequencies and cumulative frequencies.
Taking of a measurement and the process of counting yield numbers that contain information. The objective of a person applying the tools of statistics to these numbers is to determine the nature of this information.
This task is made much easier if the numbers are organized and summarized.
Even quite small data sets are difficult to understand without some summarization. Statistical quantities such as the mean and variance can be extremely helpful in summarizing data but first we discuss tabular and graphical summaries.
There are several ways to present a statistical data like;
Frequency table
Simple bar diagrams
Multiple Bar Diagrams
Histogram
Frequency Polygon etc.
Steam and Leaf plots
Pie Charts
A frequency distribution is a tabular arrangement of data in which various items are arranged into classes or groups and the number of items falling in each class is stated.
The number of observations falling in a particular class is referred to as class frequency and is denoted by "f".
In frequency distribution all the values falling in a class are assumed to be equal to the midpoint of that class.
Data presented in the form of a frequency distribution is also called grouped data. A frequency distribution table contains a condensed summary of the original data.
There are two types of frequency distribution i) Simple Frequency distribution ) ii) Grouped Frequency distribution.
The document discusses frequency distribution tables, including how to construct them from raw data by grouping data into classes of equal intervals and determining the frequency of observations within each class. Key aspects covered include determining class limits, boundaries, frequencies, widths, and cumulative frequencies. Examples are provided to demonstrate how to build a frequency distribution table and corresponding graphical representations like histograms, frequency polygons, and ogives from sets of data.
This document discusses key concepts in statistics including descriptive and inferential statistics, populations and samples, variables, and methods of collecting and presenting data. Specifically, it defines statistics, the two main types (descriptive and inferential), populations as all elements studied and samples as subsets of populations. It also outlines common variable types, methods of collecting data, different sampling techniques, how to construct frequency distributions and cumulative frequency distributions for qualitative and quantitative variables, and how to present data using bar charts and histograms.
The document provides information about various measures of central tendency including arithmetic mean, median, mode, geometric mean, and harmonic mean. It defines each measure and provides examples of calculating them using data from frequency distributions. The arithmetic mean is the most common average and is calculated by summing all values and dividing by the total number of values. The median is the middle value when values are arranged in order. The mode is the most frequent value. The geometric mean is calculated by taking the nth root of the product of n values. The harmonic mean gives the greatest weight to the smallest values and is used to average rates.
Measure of dispersion by Neeraj Bhandari ( Surkhet.Nepal )Neeraj Bhandari
This document discusses measures of dispersion used in statistics. It defines measures such as range, quartile deviation, mean deviation, variance, and standard deviation. It provides formulas to calculate these measures and examples showing how to apply the formulas. The key points are:
- Measures of dispersion quantify how spread out or varied the values in a data set are. They help identify variation, compare data sets, and enable other statistical techniques.
- Common absolute measures include range, quartile deviation, and mean deviation. Common relative measures include coefficient of range, coefficient of quartile deviation, and coefficient of variation.
- Variance and standard deviation are calculated using all data points. Variance is the average of squared deviations
This document provides an overview of biostatistics. It defines biostatistics as the branch of statistics dealing with biological and medical data, especially relating to humans. Some key points covered include:
- Descriptive statistics are used to describe data through methods like graphs and quantitative measures. Inferential statistics are used to characterize populations based on sample results.
- Biostatistics applies statistical techniques to collect, analyze, and interpret data from biological studies and health/medical research. It is used for tasks like evaluating vaccine effectiveness and informing public health priorities.
- Common analyses in biostatistics include measures of central tendency like the mean, median, and mode to summarize data, and measures of dispersion to quantify variation. Frequency distributions are
Role and importance of asceptic area for microorganisms transferASEPTIC AREA ...SailajaReddyGunnam
The document discusses aseptic areas and their design and operation. Key points:
- Aseptic areas are designed to prevent microbial and particulate contamination of pharmaceutical and medical products.
- They include clean rooms where sterilized products are prepared and aseptic areas where non-sterilized products are prepared using sterile materials.
- Laminar airflow hoods provide a controlled sterile environment for compounding sterile preparations, using HEPA filters to filter air and prevent contamination.
- Proper design of aseptic areas, equipment, facilities, and processes are needed to minimize contamination from personnel, materials, air, surfaces and other sources.
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Similar to mode measures of central tendency-ppt.pptx
This document provides information on statistics and grouped data. It defines key terms related to frequency distribution tables, measures of central tendency, measures of dispersion, measures of position, and grouped data. For frequency distribution tables, it discusses variables, frequency, and ways to represent the data through graphs. For measures of central tendency, it defines mean, mode, median, harmonic mean and geometric mean. Measures of dispersion include variance, standard deviation, and mean deviation. Measures of position are quartiles, deciles, and percentiles. The document also discusses terms related to grouped data such as class intervals, class marks, and ways to represent grouped data.
A study on the ANOVA ANALYSIS OF VARIANCE.pptxjibinjohn140
ANOVA (analysis of variance) is a statistical method used to test if the means of three or more samples or groups are equal. It divides the total variation in a data set into variation between groups and variation within groups. An F-test is used to compare the ratio of between-group variation and within-group variation. If the F-calculated value is less than the F-critical value, the null hypothesis that the sample means are equal is accepted. ANOVA can test for differences between more than two groups which makes it more efficient than multiple t-tests.
This document provides information on measures of central tendency, including the median, mode, and mean. It defines these terms, explains how to calculate them, and discusses their advantages and disadvantages. Specifically, it explains that the median is the middle value when values are arranged in order, and the mode is the most frequently occurring value. Formulas are provided for calculating the median and mode from both individual and grouped data sets. The document also discusses different types of averages and provides examples of calculating the median and mode from various data distributions.
The document provides information and instructions for analyzing student exam score data. It includes:
1) A table of 80 exam scores ranging from 53 to 97.
2) Instructions to calculate descriptive statistics like minimum, maximum, range, and percentiles of the scores.
3) Directions to construct a frequency distribution table and histogram of the scores binned into intervals of 5.
4) A calculation of measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation) of the scores.
5) An analysis of the distribution's asymmetry and kurtosis.
This document provides an introduction to medical statistics and presenting data in tables and graphs. It discusses the main methods of data presentation including tabular, graphical, and mathematical presentation. For tabular presentation, it describes the characteristics and types of tables including simple, frequency distribution, and cumulative frequency tables. The main types of graphs covered are bar charts, histograms, frequency polygons, line diagrams, and pie charts. It also discusses measures of central tendency including mean, median and mode, as well as measures of dispersion like range, mean deviation, variance and standard deviation.
This document provides an overview of analysis of variance (ANOVA). It begins by defining parametric tests and discussing the assumptions of ANOVA. The key ideas of ANOVA are introduced, including comparing the variance between groups to the variance within groups. Calculations for one-way ANOVA are demonstrated, including sums of squares, mean squares, and the F-statistic. Examples are provided to illustrate one-way ANOVA calculations and interpretations. Violations of assumptions and extensions to two-way ANOVA are also discussed.
This document discusses frequency distributions and methods for graphically presenting frequency distribution data. It defines a frequency distribution as a tabulation or grouping of data into categories showing the number of observations in each group. The document outlines the parts of a frequency table as class limits, class size, class boundaries, and class marks. It then provides steps for constructing a frequency distribution table from a set of data. Finally, it discusses histograms and frequency polygons as methods for graphically presenting frequency distribution data, and provides examples of how to construct these graphs in Excel.
BIOSTATISTICS MEAN MEDIAN MODE SEMESTER 8 AND M PHARMACY BIOSTATISTICS.pptxPayaamvohra1
1. The document provides information about biostatistics including measures of central tendency, dispersion, correlation, and regression. It defines terms like mean, median, mode, range, and standard deviation.
2. Examples of calculating mean, median, and mode from individual data sets, grouped frequency distributions, and continuous series are shown step-by-step.
3. Parametric tests like t-test, ANOVA, and tests of significance are also introduced. Overall, the document covers fundamental concepts in biostatistics through examples.
The document discusses different methods to find the mode of a data set:
1) For ungrouped data, the mode is the value that occurs most frequently.
2) For grouped data, the mode lies within the modal class which has the highest frequency. A formula is provided to calculate the exact mode.
3) The graphical method involves drawing a histogram to identify the class with the highest bar, and using the formula and class boundaries to determine the mode.
This document discusses methods for summarizing data, including frequency distributions, measures of central tendency, and measures of dispersion. It provides examples and formulas for constructing frequency distributions and calculating the mean, median, mode, range, variance, and standard deviation. Key points covered include using frequency distributions to group data, calculating central tendency measures for grouped data, and methods for measuring dispersion both for raw data and grouped data.
The document discusses the concept of mode in statistics. It defines mode as the value that occurs most frequently in a data set. It provides different methods to calculate the mode for individual data series, discrete series, and grouped series. These include inspection methods, making discrete series, using the mean and median formula, and grouping methods. The document also outlines some merits of using mode, such as being easily understood, as well as some demerits, such as it not being based on all observations. It concludes by discussing some uses of mode and providing a reference.
These are slides I use when teaching my second year undergraduate statistics course. They are designed more for conceptual understanding, and do not have syntax for programs like SPSS or R. So it is a more conceptual and mathematical review, rather than a "how-to" computer guide.
Descriptive statistics can summarize and graphically present data. Tabular presentations display data in a grid, with tables showing frequencies of categories. Graphical presentations include bar graphs to show frequencies, pie charts to show proportions, and line graphs to show trends over time. Frequency distributions organize raw data into meaningful patterns for analysis by specifying class intervals and calculating frequencies and cumulative frequencies.
Taking of a measurement and the process of counting yield numbers that contain information. The objective of a person applying the tools of statistics to these numbers is to determine the nature of this information.
This task is made much easier if the numbers are organized and summarized.
Even quite small data sets are difficult to understand without some summarization. Statistical quantities such as the mean and variance can be extremely helpful in summarizing data but first we discuss tabular and graphical summaries.
There are several ways to present a statistical data like;
Frequency table
Simple bar diagrams
Multiple Bar Diagrams
Histogram
Frequency Polygon etc.
Steam and Leaf plots
Pie Charts
A frequency distribution is a tabular arrangement of data in which various items are arranged into classes or groups and the number of items falling in each class is stated.
The number of observations falling in a particular class is referred to as class frequency and is denoted by "f".
In frequency distribution all the values falling in a class are assumed to be equal to the midpoint of that class.
Data presented in the form of a frequency distribution is also called grouped data. A frequency distribution table contains a condensed summary of the original data.
There are two types of frequency distribution i) Simple Frequency distribution ) ii) Grouped Frequency distribution.
The document discusses frequency distribution tables, including how to construct them from raw data by grouping data into classes of equal intervals and determining the frequency of observations within each class. Key aspects covered include determining class limits, boundaries, frequencies, widths, and cumulative frequencies. Examples are provided to demonstrate how to build a frequency distribution table and corresponding graphical representations like histograms, frequency polygons, and ogives from sets of data.
This document discusses key concepts in statistics including descriptive and inferential statistics, populations and samples, variables, and methods of collecting and presenting data. Specifically, it defines statistics, the two main types (descriptive and inferential), populations as all elements studied and samples as subsets of populations. It also outlines common variable types, methods of collecting data, different sampling techniques, how to construct frequency distributions and cumulative frequency distributions for qualitative and quantitative variables, and how to present data using bar charts and histograms.
The document provides information about various measures of central tendency including arithmetic mean, median, mode, geometric mean, and harmonic mean. It defines each measure and provides examples of calculating them using data from frequency distributions. The arithmetic mean is the most common average and is calculated by summing all values and dividing by the total number of values. The median is the middle value when values are arranged in order. The mode is the most frequent value. The geometric mean is calculated by taking the nth root of the product of n values. The harmonic mean gives the greatest weight to the smallest values and is used to average rates.
Measure of dispersion by Neeraj Bhandari ( Surkhet.Nepal )Neeraj Bhandari
This document discusses measures of dispersion used in statistics. It defines measures such as range, quartile deviation, mean deviation, variance, and standard deviation. It provides formulas to calculate these measures and examples showing how to apply the formulas. The key points are:
- Measures of dispersion quantify how spread out or varied the values in a data set are. They help identify variation, compare data sets, and enable other statistical techniques.
- Common absolute measures include range, quartile deviation, and mean deviation. Common relative measures include coefficient of range, coefficient of quartile deviation, and coefficient of variation.
- Variance and standard deviation are calculated using all data points. Variance is the average of squared deviations
Similar to mode measures of central tendency-ppt.pptx (20)
This document provides an overview of biostatistics. It defines biostatistics as the branch of statistics dealing with biological and medical data, especially relating to humans. Some key points covered include:
- Descriptive statistics are used to describe data through methods like graphs and quantitative measures. Inferential statistics are used to characterize populations based on sample results.
- Biostatistics applies statistical techniques to collect, analyze, and interpret data from biological studies and health/medical research. It is used for tasks like evaluating vaccine effectiveness and informing public health priorities.
- Common analyses in biostatistics include measures of central tendency like the mean, median, and mode to summarize data, and measures of dispersion to quantify variation. Frequency distributions are
Role and importance of asceptic area for microorganisms transferASEPTIC AREA ...SailajaReddyGunnam
The document discusses aseptic areas and their design and operation. Key points:
- Aseptic areas are designed to prevent microbial and particulate contamination of pharmaceutical and medical products.
- They include clean rooms where sterilized products are prepared and aseptic areas where non-sterilized products are prepared using sterile materials.
- Laminar airflow hoods provide a controlled sterile environment for compounding sterile preparations, using HEPA filters to filter air and prevent contamination.
- Proper design of aseptic areas, equipment, facilities, and processes are needed to minimize contamination from personnel, materials, air, surfaces and other sources.
This document discusses tablet compression physics and testing. It covers the following key points:
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2) The main criteria for tablet formulations are to form tablets without defects and with acceptable mechanical properties, meeting regulatory standards.
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Polymers are large molecules formed by monomers joining together in chains. They can be either natural or synthetic. There are two main types of polymerization: addition and condensation. Addition polymers are formed through a chain reaction by the joining of monomers containing carbon-carbon double bonds. Condensation polymers are formed through a step-wise reaction involving the condensation of two different functional groups from two separate monomers. Polymers have many applications including in plastics, pharmaceutical excipients, drug delivery systems, and biomedical devices.
The document discusses statistical modeling in the pharmaceutical industry. It notes that statistical modeling is one approach used to address challenges related to cost, time, and quality in drug discovery and development. The goals of statistical models include understanding the mechanisms by which data is generated and extracting information from data. Statistical modeling aims to translate known properties and hypotheses into mathematical representations. This allows for simplified descriptions of experiments and mechanisms. The identification of appropriate models requires thorough investigation. Descriptive models aim to describe data structures while mechanistic models seek to understand underlying phenomena. Statistical modeling has advantages of reducing costs and time while improving success rates.
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The document discusses intrauterine devices (IUDs). It describes the anatomy of the uterus and explains that IUDs are small objects inserted through the cervix into the uterus to prevent pregnancy. There are two main types - non-medicated IUDs made of plastic or stainless steel, and medicated IUDs that deliver drugs like copper or progesterone. IUDs are highly effective birth control but can increase bleeding or cramping.
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Sterilization is the process of eliminating all forms of life through heat, chemicals, irradiation, high pressure, or filtration. There are physical methods like heat and radiation, chemical methods, and gaseous methods. Moist heat sterilization in an autoclave uses pressurized steam at 121-134°C for 15-30 minutes to destroy microorganisms through protein denaturation. Dry heat sterilization in a hot air oven requires higher temperatures like 160°C for 60 minutes and uses conduction and convection to penetrate materials and kill microbes through oxidative damage. Both methods are effective at sterilizing heat-stable items but moist heat is more rapid while dry heat is used for items that cannot get
Monoclonal antibodies are identical antibodies produced by a single B cell clone that recognize a specific epitope. They are produced through the fusion of B cells from an immunized animal with myeloma cells to form a hybridoma cell line. Monoclonal antibodies have various applications, including use in diagnostic tests to detect substances like hormones and tumor markers, diagnostic imaging by delivering radioisotopes to target areas, and directly targeting diseases or purifying proteins through immunoaffinity chromatography. Their specificity and ability to target single epitopes makes them useful research and medical tools.
Microencapsulation involves coating solid, liquid, or gas core materials on a small scale between 1-500 microns. It has various applications including sustained drug release, taste masking, and stabilization of compounds. There are several techniques for microencapsulation including pan coating, spray drying, solvent evaporation, coacervation, and polymerization. The choice of technique depends on the properties of the core and coating materials. Microencapsulation has many pharmaceutical applications such as modified drug release to reduce dosing frequency and mask unpleasant tastes or odors.
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This particular slides consist of- what is hypertension,what are it's causes and it's effect on body, risk factors, symptoms,complications, diagnosis and role of physiotherapy in it.
This slide is very helpful for physiotherapy students and also for other medical and healthcare students.
Here is summary of hypertension -
Hypertension, also known as high blood pressure, is a serious medical condition that occurs when blood pressure in the body's arteries is consistently too high. Blood pressure is the force of blood pushing against the walls of blood vessels as the heart pumps it. Hypertension can increase the risk of heart disease, brain disease, kidney disease, and premature death.
This particular slides consist of- what is hypotension,what are it's causes and it's effect on body, risk factors, symptoms,complications, diagnosis and role of physiotherapy in it.
This slide is very helpful for physiotherapy students and also for other medical and healthcare students.
Here is the summary of hypotension:
Hypotension, or low blood pressure, is when the pressure of blood circulating in the body is lower than normal or expected. It's only a problem if it negatively impacts the body and causes symptoms. Normal blood pressure is usually between 90/60 mmHg and 120/80 mmHg, but pressures below 90/60 are generally considered hypotensive.
Digital Health in India_Health Informatics Trained Manpower _DrDevTaneja_15.0...DrDevTaneja1
Digital India will need a big trained army of Health Informatics educated & trained manpower in India.
Presently, generalist IT manpower does most of the work in the healthcare industry in India. Academic Health Informatics education is not readily available at school & health university level or IT education institutions in India.
We look into the evolution of health informatics and its applications in the healthcare industry.
HIMMS TIGER resources are available to assist Health Informatics education.
Indian Health universities, IT Education institutions, and the healthcare industry must proactively collaborate to start health informatics courses on a big scale. An advocacy push from various stakeholders is also needed for this goal.
Health informatics has huge employment potential and provides a big business opportunity for the healthcare industry. A big pool of trained health informatics manpower can lead to product & service innovations on a global scale in India.
Emotional and Behavioural Problems in Children - Counselling and Family Thera...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Mental Health and well-being Presentation. Exploring innovative approaches and strategies for enhancing mental well-being. Discover cutting-edge research, effective strategies, and practical methods for fostering mental well-being.
NURSING MANAGEMENT OF PATIENT WITH EMPHYSEMA .PPTblessyjannu21
Prepared by Prof. BLESSY THOMAS, VICE PRINCIPAL, FNCON, SPN.
Emphysema is a disease condition of respiratory system.
Emphysema is an abnormal permanent enlargement of the air spaces distal to terminal bronchioles, accompanied by destruction of their walls and without obvious fibrosis.
Emphysema of lung is defined as hyper inflation of the lung ais spaces due to obstruction of non respiratory bronchioles as due to loss of elasticity of alveoli.
It is a type of chronic obstructive
pulmonary disease.
It is a progressive disease of lungs.
End-tidal carbon dioxide (ETCO2) is the level of carbon dioxide that is released at the end of an exhaled breath. ETCO2 levels reflect the adequacy with which carbon dioxide (CO2) is carried in the blood back to the lungs and exhaled.
Non-invasive methods for ETCO2 measurement include capnometry and capnography. Capnometry provides a numerical value for ETCO2. In contrast, capnography delivers a more comprehensive measurement that is displayed in both graphical (waveform) and numerical form.
Sidestream devices can monitor both intubated and non-intubated patients, while mainstream devices are most often limited to intubated patients.
This particular slides consist of- what is Pneumothorax,what are it's causes and it's effect on body, risk factors, symptoms,complications, diagnosis and role of physiotherapy in it.
This slide is very helpful for physiotherapy students and also for other medical and healthcare students.
Here is a summary of Pneumothorax:
Pneumothorax, also known as a collapsed lung, is a condition that occurs when air leaks into the space between the lung and chest wall. This air buildup puts pressure on the lung, preventing it from expanding fully when you breathe. A pneumothorax can cause a complete or partial collapse of the lung.
2. It is derived
from the
French word
”La Mode”
which means
fashion.
Mode is the
most
fashionable or
a typical
value of a
distribution
because it is
repeated the
highest
number of
times in the
series.
The mode is
by
definition,
the most
commonly
occurring
value.
ORIGIN
3. 1)According to Croxton
and Cowden, “The mode
of a distribution is the
value at the point around
which the items tend to be
most heavily
concentrated”.
2)A.M Tuttle said ,
“Mode is the value which
has the greater frequency
density in its immediate
neighborhood”.
Definition…
4. Types of Model Values
Unimodal Series
• The series of
observations
which contains
only one model
series
Bimodal Series
• The series of
observations
which contains
two modes
• In this the two
modes are the
same value of
greatest
density.
Multimodal
Series
• The series of
observations
which contains
more than two
modes.
• In this the
modes are the
same value of
greatest
density.
6. As per definitions, mode is always the peak, irrespective of the
shape of the curve, symmetric or asymmetric.
⚫ Perfectly symmetrical distribution is the one in which one
mode (uni-mode) is present and the three measures of central
tendency (mean, median and mode) coincide with the highest
point. Eg. Normal (bell-shaped) distribution
⚫ Skewed or asymmetric distribution is the one in which the
data distribution indicates that the three measures (central) are
not the same.
10. Applications of Mode
Mode is used by business and commercial management. It is used for the
study of fashions and consumer perceptions.
Merits
Mode is not affected by extreme values. It can be easily understood.
Demerits
a) Mode estimation does not consider all the observations.
b) In bimodal distribution, it is not possible to estimate the mode.
c) It is an unstable measure, because it may change, if sampling fluctuations
are high.
d) The mode cannot be subjected to algebraic treatments.
e) Mode is not a rigidly defined measure, sometimes, grouping table and
analysis table are prepared to find mode, which is laborious.
11. I. Computation of Mode for Individual Series
The sizes of the particles (in a powder) are considered. The mode is calculated for
the data given below. 2, 4, 5, 8, 6, 5, 4, 2, 5, 6, 8, 5, 6, 4, 5, 8, 5, 6, 4 and 5.
In the above data, the number 5 is repeated several times (seven times).
No other number is repeated 5 times. Hence, the mode is 5 m.
II. Computation of Mode for Discrete Series
In case of grouped data, the mode can be estimated, under the discrete and
continuous series.
In the above example here, two highest frequency values are for
particle size 40 and 50.
The first impression will be that the mode lies in value 40 because the
highest frequency concentration of 80 is in it.
But f = 80 may not be true because the neighboring frequencies should
also be considered for mode(max 60+80+70).
12. Preparing Grouping Table
Column I: It has original frequencies and the maximum frequency is marked by bold type
Column II: In this column the frequencies of column I are combined ‘two by two’. (1 and 2;
3 and 4; 5and 6 and so on). Here also the maximum frequency is marked by bold type.
Column III: Here, we leave the first frequency of column I and combine the others in ‘two
by two’. (2 and 3; 4 and 5; 6 and 7 and so on). Again the maximum frequency is marked by
bold type.
Column IV: In this column the frequencies of column I are combined (grouped) in ‘three by
three’. (1, 2 and 3; 4, 5 and 6; 7, 8 and 9 and so on). And again the maximum frequency is
marked by bold type.
Column V: Here we leave the first frequency of the column I and group the others in ‘three
by three’. (2, 3 and 4; 5, 6 and 7; 8, 9 and 10 and so on). Again mark the maximum frequency
by bold type.
Column VI: Now leave the first two frequencies of column I and combine the others in
‘three by three’.(3, 4 and 5; 6, 7 and 8; 9, 10 and 11 and son on). Mark the maximum
frequency by bold type.
13. Preparing Analysis Table
After preparing the grouping table, we prepare the analysis table.
✔ 1) In the table put the column numbers on the left hand
side
✔ 2)various probable value of the mode on the right hand
side.
✔ 3)The values against which frequencies are maximum
marked in the grouping table and are entered by means
of a bar in the relevant ‘box’ corresponding to the
values they represent.
✔ 4)Find total frequency of marked bars
14. Practice Problem) Determine the modal size of particle from the
following data
Size of
Particle
4 5 6 7 8 9 10 11 12 13
Frequen
cy
2 5 8 9 12 14 14 15 11 13
Sol) We find that the value of the x variable 11 has frequency
maximum number of times i.e. 15.
We also notice that the difference between the frequencies of
the values of the variable, on both sides of 15, which are very
close to 11, is very small.
This shows that the values of the variable x are heavily
concentrated on either side of 11.
Therefore, if we find mode just by inspection gives error.
16. ANALYSIS
TABLE
X
Col No
4 5 6 7 8 9 10 11 12 13
I 1
II 1 1
III 1 1
IV 1 1 1
V 1 1 1
VI 1 1 1
Total
frequenc
y
1 4 5 4 1
From the analysis table it is clear that the value 10 has the
maximum number of bars i.e. 6.
Hence the modal value is 10.
17. II. Computation of Mode for Continuous Series of
Data
The exact value of mode in the case of continuous frequency
distribution can be obtained by the following formulae:
18. Size range 0 – 10 10 – 20 20 – 30 30 – 40 40 – 50
Frequency 12 26 30 18 14
Practice Problem) The particle size range and its distribution are
furnished below. Find the mode.
Size
range(x)
Freque
ncy(f)
Grouping
Col I Col II Col III Col IV Col V Col VI
0 – 10 12 38 68
10 – 20 26 56 74
20 – 30 30 48 62
30 – 40 18 32
40 – 50 14
Sol) Let us prepare Grouping and Analysis table
GROUPING TABLE
19. X
Col No
0 – 10 10 – 20 20 – 30 30 – 40 40 - 50
I 1
II 1 1
III 1 1
IV 1 1 1
V 1 1 1
VI 1 1 1
Total
frequency
1 3 6 3 1
ANALYSIS TABLE
From the analysis table it is clear that the class range 20 -30 has the
maximum number of bars i.e. 6. Hence the modal class is 20 - 30.
20. ⮚ It can be easily understood
⮚ It can be located in some cases by inspection, mode is that point where
there is more concentration of frequencies.
⮚ It is not affected by extreme values
⮚ It represents most frequent value and hence it is very useful in practice
⮚ It can be located in open end distributions
Merits, Demerits and uses of Mode
Merits
❖ The mode is not based on all the observations
❖ The value of the mode cannot be determined in bimodal
distribution
❖ It is unstable measure as it is affected more by sampling fluctuations
❖ It cannot be subjected to algebraic treatments.
❖ It is not rigidly defined measure sometimes as it is necessary to
prepare grouping table and analysis table to find modal class
DeMerits
21. EMPIRICAL RELATION BETWEEN MEAN, MEDIAN AND MODE
A distribution in which mean, median and mode coincide is called a
symmetrical distribution. If the distribution is moderately asymmetrical
then mean, median and mode are connected by the formula:
Size
range
0 – 10 10 – 20 20 – 30 30 – 40 40 – 50 50 – 60 60 – 70 70 – 80
Frequ
ency
4 6 20 32 33
17 8 2
Practice Problem) The particle size range and its distribution are furnished
below. Find the mode.
Sol) Let us prepare Grouping and Analysis table
Mode = 3 Median – 2 Mean
22. Size
range(x
)
Freque
ncy(f)
Grouping
Col I Col II Col III Col IV Col V Col VI
0 – 10 4
10
30
10 – 20 6
26
58
20 – 30 20
52
85
30 – 40 32
65
82
40 – 50 33
50
58
50 – 60 17
25
27
60 – 70 8
10
70 – 80 2
GROUPING TABLE
23. X
Col No
0 – 10 10 – 20 20 – 30 30 – 40 40 - 50 50 – 60 60 – 70 70 – 80
I 1
II 1 1
III 1 1
IV 1 1 1
V 1 1 1 1 1 1
VI 1 1 1
Total
frequency
1 3 5 5 2 1
ANALYSIS TABLE
This is Bimodal Series. Mode is to be ascertained by applying this formula.
Mode = 3 Median – 2 Mean