The document discusses guidance and counselling in education. It defines guidance as assisting students to manage their lives, make decisions, and solve problems. Counselling helps students learn about themselves and their situations. The purposes of guidance and counselling are to help students develop personally and socially, adjust to challenges, and utilize their abilities. It aims to develop good citizenship and attitudes. Guidance is a continuous process of helping students reach their potential in a way that benefits themselves and society. The median and mean are described as measures to summarize grouped or ungrouped student data.
1. Measures of central tendency include the mean, median, and mode.
2. The mean is the average value found by dividing the sum of all values by the total number of values. The median is the middle value when values are arranged in order. The mode is the value that appears most frequently.
3. For grouped data, the mean is calculated using the sum of the frequency multiplied by the class midpoint divided by the total frequency. The median class is identified which has a cumulative frequency above and below half the total. The mode is the class with the highest frequency.
Lecture 3 Measures of Central Tendency and Dispersion.pptxshakirRahman10
Objectives:
Define measures of central tendency (mean, median, and mode)
Define measures of dispersion (variance and standard deviation).
Compute the measures of central tendency and Dispersion.
Learn the application of mean and standard deviation using Empirical rule and Tchebyshev’s theorem.
Measures of Central Tendency:
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
Why is it needed?
To summarize the data.
It provides with a typical value that gives the picture of the entire data set
Mean:
It is the arithmetic average of a set of numbers, It is the most common measure of central tendency.
Computed by summing all values in the data set and dividing the sum by the number of values in the data set Properties:
Applicable for interval and ratio data
Not applicable for nominal or ordinal data
Affected by each value in the data set, including extreme values.
Formula:
Mean is calculated by adding all values in the data set and dividing the sum by the number of values in the data set.
Median:
Mid-point or Middle value of the data when the measurements are arranged in ascending order.
A point that divides the data into two equal parts.
Computational Procedure:
Arrange the observations in an ascending order.
If there is an odd number of terms, the median is the middle value and If there is an even number of terms, the median is the average of the middle two terms.
Mode:
The mode is the observation that occurs most frequently in the data set.
There can be more than one mode for a data set OR there maybe no mode in a data set.
Is also applicable to the nominal data.
Comparison of Measures of Central Tendency in Positively Skewed Distributions:
Majority of the data values fall to the left of the mean and cluster at the lower end of the distribution: the tail is to the right Mean, median & mode are different When a distribution has a few extremely high scores, the mean will have a greater value than the median = positively skewed.
Majority of the data values fall to
the right of the mean and cluster at the upper end of the distribution= Negatively Skewed
This document provides an overview of basic biostatistics and descriptive statistics. Biostatistics analyzes data from biological and medical sciences. Descriptive statistics are used to organize, summarize, and describe data through measures of central tendency like mean, median, and mode and measures of variation like range, standard deviation, and variance. These statistics provide a simple summary of populations and samples to analyze health status, support scientific research, and interpret clinical data.
The document discusses different measures of central tendency including the mean, median and mode. It provides definitions and formulas for calculating different types of means:
- The arithmetic mean is calculated by summing all values and dividing by the total number of values. It can be calculated using direct or short-cut methods for both individual observations and grouped data.
- Other means include the geometric mean and harmonic mean, which are called special averages.
- The median is the middle value when values are arranged in order. The mode is the value that occurs most frequently.
- Data can be in the form of individual observations, discrete series or continuous series. Formulas are provided for calculating the mean of grouped or ungrouped data
This document discusses various measures of central tendency including the mean, median, and mode. It defines each measure and provides examples of how to calculate each from both ungrouped and grouped data. The mean is the average value 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 and divides the data set in half. The mode is the most frequently occurring value.
This document provides an overview of key concepts in data display and summary, biostatistics, and descriptive statistics. It defines data, statistics, vital statistics, biostatistics, descriptive statistics, inferential statistics, primary and secondary data, variables, categories of data, quantitative and qualitative data, measures of central tendency, measures of dispersion, and other statistical terminology. It also gives examples to illustrate concepts like mean, median, mode, range, variance, and standard deviation.
This document discusses measures of central tendency including the mean, median, and mode. It defines each measure and provides examples of how to calculate them using data sets. The mean is the average value obtained by dividing the sum of all values by the number of values. The median is the middle value when values are arranged in order. The mode is the most frequent value in the data set. The document outlines advantages and disadvantages of each measure and concludes that measures of central tendency describe the typical or central value in a data set.
This document provides an overview of statistics concepts including measures of central tendency (mean, median, mode), calculating these measures, outliers and their effect, trimmed means, weighted means, and percentiles. It includes examples and step-by-step solutions for calculating various statistical measures. Key topics covered are finding the mean, median, and mode of data sets, how outliers impact these measures, calculating trimmed and weighted means, and an introduction to percentiles.
1. Measures of central tendency include the mean, median, and mode.
2. The mean is the average value found by dividing the sum of all values by the total number of values. The median is the middle value when values are arranged in order. The mode is the value that appears most frequently.
3. For grouped data, the mean is calculated using the sum of the frequency multiplied by the class midpoint divided by the total frequency. The median class is identified which has a cumulative frequency above and below half the total. The mode is the class with the highest frequency.
Lecture 3 Measures of Central Tendency and Dispersion.pptxshakirRahman10
Objectives:
Define measures of central tendency (mean, median, and mode)
Define measures of dispersion (variance and standard deviation).
Compute the measures of central tendency and Dispersion.
Learn the application of mean and standard deviation using Empirical rule and Tchebyshev’s theorem.
Measures of Central Tendency:
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
Why is it needed?
To summarize the data.
It provides with a typical value that gives the picture of the entire data set
Mean:
It is the arithmetic average of a set of numbers, It is the most common measure of central tendency.
Computed by summing all values in the data set and dividing the sum by the number of values in the data set Properties:
Applicable for interval and ratio data
Not applicable for nominal or ordinal data
Affected by each value in the data set, including extreme values.
Formula:
Mean is calculated by adding all values in the data set and dividing the sum by the number of values in the data set.
Median:
Mid-point or Middle value of the data when the measurements are arranged in ascending order.
A point that divides the data into two equal parts.
Computational Procedure:
Arrange the observations in an ascending order.
If there is an odd number of terms, the median is the middle value and If there is an even number of terms, the median is the average of the middle two terms.
Mode:
The mode is the observation that occurs most frequently in the data set.
There can be more than one mode for a data set OR there maybe no mode in a data set.
Is also applicable to the nominal data.
Comparison of Measures of Central Tendency in Positively Skewed Distributions:
Majority of the data values fall to the left of the mean and cluster at the lower end of the distribution: the tail is to the right Mean, median & mode are different When a distribution has a few extremely high scores, the mean will have a greater value than the median = positively skewed.
Majority of the data values fall to
the right of the mean and cluster at the upper end of the distribution= Negatively Skewed
This document provides an overview of basic biostatistics and descriptive statistics. Biostatistics analyzes data from biological and medical sciences. Descriptive statistics are used to organize, summarize, and describe data through measures of central tendency like mean, median, and mode and measures of variation like range, standard deviation, and variance. These statistics provide a simple summary of populations and samples to analyze health status, support scientific research, and interpret clinical data.
The document discusses different measures of central tendency including the mean, median and mode. It provides definitions and formulas for calculating different types of means:
- The arithmetic mean is calculated by summing all values and dividing by the total number of values. It can be calculated using direct or short-cut methods for both individual observations and grouped data.
- Other means include the geometric mean and harmonic mean, which are called special averages.
- The median is the middle value when values are arranged in order. The mode is the value that occurs most frequently.
- Data can be in the form of individual observations, discrete series or continuous series. Formulas are provided for calculating the mean of grouped or ungrouped data
This document discusses various measures of central tendency including the mean, median, and mode. It defines each measure and provides examples of how to calculate each from both ungrouped and grouped data. The mean is the average value 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 and divides the data set in half. The mode is the most frequently occurring value.
This document provides an overview of key concepts in data display and summary, biostatistics, and descriptive statistics. It defines data, statistics, vital statistics, biostatistics, descriptive statistics, inferential statistics, primary and secondary data, variables, categories of data, quantitative and qualitative data, measures of central tendency, measures of dispersion, and other statistical terminology. It also gives examples to illustrate concepts like mean, median, mode, range, variance, and standard deviation.
This document discusses measures of central tendency including the mean, median, and mode. It defines each measure and provides examples of how to calculate them using data sets. The mean is the average value obtained by dividing the sum of all values by the number of values. The median is the middle value when values are arranged in order. The mode is the most frequent value in the data set. The document outlines advantages and disadvantages of each measure and concludes that measures of central tendency describe the typical or central value in a data set.
This document provides an overview of statistics concepts including measures of central tendency (mean, median, mode), calculating these measures, outliers and their effect, trimmed means, weighted means, and percentiles. It includes examples and step-by-step solutions for calculating various statistical measures. Key topics covered are finding the mean, median, and mode of data sets, how outliers impact these measures, calculating trimmed and weighted means, and an introduction to percentiles.
This document provides information about statistical methods for summarizing data, including measures of central tendency, variability, and position. It discusses the mean, median, mode, range, variance, standard deviation, z-scores, and percentiles. The mean is the average value and considers all data points. The median divides the data in half. The mode is the most frequent value. Variance and standard deviation measure how spread out values are around the mean. Percentiles and z-scores indicate a value's position relative to others in the data set.
This document provides definitions and explanations of key statistical concepts including:
1. Statistics is defined as the science of collecting, classifying, presenting, and interpreting data. Central tendency measures like mean, median, and mode are used to summarize data.
2. Measures of dispersion like range, interquartile range, mean deviation, and standard deviation describe how spread out the data is from the central tendency. Standard deviation is the most accurate measure as it considers both the deviation from the mean and the mathematical signs.
3. Examples are provided to demonstrate calculating the mean, median, mode, and standard deviation for both ungrouped and grouped data series. The standard deviation provides the best estimation of the population mean when
Exploring Measures of Central Tendency
In this presentation, we delve into the fundamental concept of Measures of Central Tendency. These statistical tools - Mean, Median, and Mode - are at the heart of data analysis, guiding us to understand where the center of our data lies.
We explore each measure's definition and its unique role in analyzing data. Learn when to wisely apply mean, median, or mode based on your data's distribution. Discover the real-life applications that make these concepts crucial in various industries.
By grasping the significance of central tendency, you'll be better equipped to make informed decisions and draw meaningful conclusions from your data. Join the discussion and deepen your understanding of these fundamental statistical tools.
The document provides an overview of how to understand and interpret student course evaluation data, including explaining basic statistics like percentages, means, medians, modes, and standard deviations that are used to analyze evaluation results. It also discusses how to interpret qualitative feedback from students and offers recommendations for faculty on gathering additional feedback and using resources to help analyze evaluations.
Central tendency refers to statistical measures that identify a central or typical value for a data set. The three main measures are the mean, median, and mode. The mean is the average value calculated by dividing the sum of all values by the number of values. The median is the middle value of the data set when sorted. The mode is the most frequently occurring value. Different measures are better suited depending on the type of data and how it is distributed.
The document discusses different measures of central tendency including the mean, median, and mode. The mean is the average value calculated by adding all values and dividing by the total number of values. The median is the middle value when values are arranged from lowest to highest. The mode is the most frequently occurring value in the data set. The document provides examples of calculating each measure and discusses their advantages and disadvantages.
Unit 1 - Measures of Dispersion - 18MAB303T - PPT - Part 2.pdfAravindS199
The document discusses various measures of dispersion, which describe how data values are spread around the mean. It describes absolute measures like range, interquartile range, mean deviation, and standard deviation. Range is the difference between highest and lowest values. Standard deviation calculates the average distance of all values from the mean. It is the most robust measure as it considers all data points. The document also provides examples of calculating different dispersion measures and their merits and limitations.
Measure of central tendency provides a very convenient way of describing a set of scores with a single number that describes the PERFORMANCE of the group.
It is also defined as a single value that is used to describe the “center” of the data.
This document provides an overview of measures of central tendency including the mean, median, and mode. It defines each measure and provides examples of how to calculate them from raw data and grouped frequency distributions. The mean is the average and is calculated by summing all values and dividing by the total number of data points. The median is the middle value when data is ordered from lowest to highest. The mode is the most frequent value. Methods for calculating each measure from both ungrouped and grouped data are described.
Measures of Central Tendency-Mean, Median , Mode- Dr. Vikramjit SinghVikramjit Singh
This presentation discusses in details about different measures of central tendency like- mean, median, mode, Geometric Mean, Harmonic Mean and Weighted Mean.
The document discusses various methods for collecting, organizing, and analyzing quantitative data. It describes data as facts expressed numerically that are useful for decision making. Common data types include individual series, discrete series, and continuous series. Methods for calculating the arithmetic mean or average as a measure of central tendency are provided for each data type, including direct formulas and shortcut methods. The goal of statistical analysis is to summarize collected data and identify trends or conclusions.
Statistical analysis is an important tool for researchers to analyze collected data. There are two major areas of statistics: descriptive statistics which develops indices to describe data, and inferential statistics which tests hypotheses and generalizes findings. Descriptive statistics measures central tendency (mean, median, mode), dispersion (range, standard deviation), and skewness. Relationship between variables is measured using correlation and regression analysis. Statistical tools help summarize large datasets, identify patterns, and make reliable inferences.
This document discusses measures of dispersion in statistics. It defines dispersion as the extent of variation in a data set from the average value. There are two main types of dispersion - absolute and relative. Absolute measures express variation in units of the data and include range, variance, standard deviation, and quartile deviation. Relative measures allow comparison between data sets by being unit-free, such as the coefficient of variation. Key absolute measures are then explained in more detail, along with their merits and demerits.
This document discusses measures of dispersion and the normal distribution. It defines measures of dispersion as ways to quantify the variability in a data set beyond measures of central tendency like mean, median, and mode. The key measures discussed are range, quartile deviation, mean deviation, and standard deviation. It provides formulas and examples for calculating each measure. The document then explains the normal distribution as a theoretical probability distribution important in statistics. It outlines the characteristics of the normal curve and provides examples of using the normal distribution and calculating z-scores.
This document discusses measures of central tendency and dispersion in statistics. It defines central tendency as a single value that describes the center of a data distribution. Common measures include the mean, median, and mode. The mean is the average value calculated by adding all values and dividing by the total number. The median is the middle value when data is ordered from lowest to highest. The mode is the most frequent value. Dispersion measures the spread of data and includes the range, mean deviation, standard deviation, and variance. Standard deviation summarizes how far data points are from the mean. Variance is the square of the standard deviation. The document provides examples of calculating these measures and their characteristics and uses.
The document discusses various statistical concepts including descriptive statistics, measures of central tendency, and analyzing data distributions. It defines statistics, explains why statistics are important in education, and outlines different types of statistics including descriptive statistics. The document also covers topics such as calculating the mean, median, and mode of data sets, constructing frequency distributions and histograms, and identifying symmetrical and skewed data distributions.
The document discusses various measures of central tendency and standard scores used to compare scores from different tests. It defines mean, median and mode as measures of central tendency, and explains how the normal distribution results in a bell-shaped curve. It then discusses converting raw scores to standard scores using z-scores and t-scores in order to compare scores from different tests on a common scale. Z-scores indicate the distance from the mean in standard deviations, while t-scores have a mean of 50 and standard deviation of 10.
Biostatistics cource for clinical pharmacyBatizemaryam
This document discusses methods for summarizing data, including measures of central tendency and dispersion. It defines the mean, median, and mode as common measures of central tendency. For grouped data, it explains how to calculate the mean, median, and mode. The document also discusses measures of dispersion such as range, interquartile range, variance, and standard deviation. It provides examples of calculating various summary statistics for both ungrouped and grouped data.
This document defines statistics and its uses in community medicine. It outlines the objectives of describing statistics, summarizing data in tables and graphs, and calculating measures of central tendency and dispersion. Various data types, sources, and methods of presentation including tables and graphs are described. Common measures used to summarize data like percentile, measures of central tendency, and measures of dispersion are defined.
This document provides an overview of communication, including definitions, levels, elements, forms, factors influencing communication, effective communication methods, barriers to communication, and principles of therapeutic communication. It defines communication as a two-way process involving the transfer of information between a minimum of one sender and receiver. The different levels of communication discussed are intrapersonal, interpersonal, transpersonal, small group, and public. The elements, forms, and principles of communication are also summarized.
This document provides information about statistical methods for summarizing data, including measures of central tendency, variability, and position. It discusses the mean, median, mode, range, variance, standard deviation, z-scores, and percentiles. The mean is the average value and considers all data points. The median divides the data in half. The mode is the most frequent value. Variance and standard deviation measure how spread out values are around the mean. Percentiles and z-scores indicate a value's position relative to others in the data set.
This document provides definitions and explanations of key statistical concepts including:
1. Statistics is defined as the science of collecting, classifying, presenting, and interpreting data. Central tendency measures like mean, median, and mode are used to summarize data.
2. Measures of dispersion like range, interquartile range, mean deviation, and standard deviation describe how spread out the data is from the central tendency. Standard deviation is the most accurate measure as it considers both the deviation from the mean and the mathematical signs.
3. Examples are provided to demonstrate calculating the mean, median, mode, and standard deviation for both ungrouped and grouped data series. The standard deviation provides the best estimation of the population mean when
Exploring Measures of Central Tendency
In this presentation, we delve into the fundamental concept of Measures of Central Tendency. These statistical tools - Mean, Median, and Mode - are at the heart of data analysis, guiding us to understand where the center of our data lies.
We explore each measure's definition and its unique role in analyzing data. Learn when to wisely apply mean, median, or mode based on your data's distribution. Discover the real-life applications that make these concepts crucial in various industries.
By grasping the significance of central tendency, you'll be better equipped to make informed decisions and draw meaningful conclusions from your data. Join the discussion and deepen your understanding of these fundamental statistical tools.
The document provides an overview of how to understand and interpret student course evaluation data, including explaining basic statistics like percentages, means, medians, modes, and standard deviations that are used to analyze evaluation results. It also discusses how to interpret qualitative feedback from students and offers recommendations for faculty on gathering additional feedback and using resources to help analyze evaluations.
Central tendency refers to statistical measures that identify a central or typical value for a data set. The three main measures are the mean, median, and mode. The mean is the average value calculated by dividing the sum of all values by the number of values. The median is the middle value of the data set when sorted. The mode is the most frequently occurring value. Different measures are better suited depending on the type of data and how it is distributed.
The document discusses different measures of central tendency including the mean, median, and mode. The mean is the average value calculated by adding all values and dividing by the total number of values. The median is the middle value when values are arranged from lowest to highest. The mode is the most frequently occurring value in the data set. The document provides examples of calculating each measure and discusses their advantages and disadvantages.
Unit 1 - Measures of Dispersion - 18MAB303T - PPT - Part 2.pdfAravindS199
The document discusses various measures of dispersion, which describe how data values are spread around the mean. It describes absolute measures like range, interquartile range, mean deviation, and standard deviation. Range is the difference between highest and lowest values. Standard deviation calculates the average distance of all values from the mean. It is the most robust measure as it considers all data points. The document also provides examples of calculating different dispersion measures and their merits and limitations.
Measure of central tendency provides a very convenient way of describing a set of scores with a single number that describes the PERFORMANCE of the group.
It is also defined as a single value that is used to describe the “center” of the data.
This document provides an overview of measures of central tendency including the mean, median, and mode. It defines each measure and provides examples of how to calculate them from raw data and grouped frequency distributions. The mean is the average and is calculated by summing all values and dividing by the total number of data points. The median is the middle value when data is ordered from lowest to highest. The mode is the most frequent value. Methods for calculating each measure from both ungrouped and grouped data are described.
Measures of Central Tendency-Mean, Median , Mode- Dr. Vikramjit SinghVikramjit Singh
This presentation discusses in details about different measures of central tendency like- mean, median, mode, Geometric Mean, Harmonic Mean and Weighted Mean.
The document discusses various methods for collecting, organizing, and analyzing quantitative data. It describes data as facts expressed numerically that are useful for decision making. Common data types include individual series, discrete series, and continuous series. Methods for calculating the arithmetic mean or average as a measure of central tendency are provided for each data type, including direct formulas and shortcut methods. The goal of statistical analysis is to summarize collected data and identify trends or conclusions.
Statistical analysis is an important tool for researchers to analyze collected data. There are two major areas of statistics: descriptive statistics which develops indices to describe data, and inferential statistics which tests hypotheses and generalizes findings. Descriptive statistics measures central tendency (mean, median, mode), dispersion (range, standard deviation), and skewness. Relationship between variables is measured using correlation and regression analysis. Statistical tools help summarize large datasets, identify patterns, and make reliable inferences.
This document discusses measures of dispersion in statistics. It defines dispersion as the extent of variation in a data set from the average value. There are two main types of dispersion - absolute and relative. Absolute measures express variation in units of the data and include range, variance, standard deviation, and quartile deviation. Relative measures allow comparison between data sets by being unit-free, such as the coefficient of variation. Key absolute measures are then explained in more detail, along with their merits and demerits.
This document discusses measures of dispersion and the normal distribution. It defines measures of dispersion as ways to quantify the variability in a data set beyond measures of central tendency like mean, median, and mode. The key measures discussed are range, quartile deviation, mean deviation, and standard deviation. It provides formulas and examples for calculating each measure. The document then explains the normal distribution as a theoretical probability distribution important in statistics. It outlines the characteristics of the normal curve and provides examples of using the normal distribution and calculating z-scores.
This document discusses measures of central tendency and dispersion in statistics. It defines central tendency as a single value that describes the center of a data distribution. Common measures include the mean, median, and mode. The mean is the average value calculated by adding all values and dividing by the total number. The median is the middle value when data is ordered from lowest to highest. The mode is the most frequent value. Dispersion measures the spread of data and includes the range, mean deviation, standard deviation, and variance. Standard deviation summarizes how far data points are from the mean. Variance is the square of the standard deviation. The document provides examples of calculating these measures and their characteristics and uses.
The document discusses various statistical concepts including descriptive statistics, measures of central tendency, and analyzing data distributions. It defines statistics, explains why statistics are important in education, and outlines different types of statistics including descriptive statistics. The document also covers topics such as calculating the mean, median, and mode of data sets, constructing frequency distributions and histograms, and identifying symmetrical and skewed data distributions.
The document discusses various measures of central tendency and standard scores used to compare scores from different tests. It defines mean, median and mode as measures of central tendency, and explains how the normal distribution results in a bell-shaped curve. It then discusses converting raw scores to standard scores using z-scores and t-scores in order to compare scores from different tests on a common scale. Z-scores indicate the distance from the mean in standard deviations, while t-scores have a mean of 50 and standard deviation of 10.
Biostatistics cource for clinical pharmacyBatizemaryam
This document discusses methods for summarizing data, including measures of central tendency and dispersion. It defines the mean, median, and mode as common measures of central tendency. For grouped data, it explains how to calculate the mean, median, and mode. The document also discusses measures of dispersion such as range, interquartile range, variance, and standard deviation. It provides examples of calculating various summary statistics for both ungrouped and grouped data.
This document defines statistics and its uses in community medicine. It outlines the objectives of describing statistics, summarizing data in tables and graphs, and calculating measures of central tendency and dispersion. Various data types, sources, and methods of presentation including tables and graphs are described. Common measures used to summarize data like percentile, measures of central tendency, and measures of dispersion are defined.
This document provides an overview of communication, including definitions, levels, elements, forms, factors influencing communication, effective communication methods, barriers to communication, and principles of therapeutic communication. It defines communication as a two-way process involving the transfer of information between a minimum of one sender and receiver. The different levels of communication discussed are intrapersonal, interpersonal, transpersonal, small group, and public. The elements, forms, and principles of communication are also summarized.
Hypertrophic pyloric stenosis occurs when the pyloric sphincter muscle thickens, narrowing the stomach outlet. It typically affects young infants, especially firstborn males. Projectile vomiting after feeding is the main symptom. Diagnosis involves abdominal x-ray or ultrasound showing thickening of the pylorus. Surgical pyloromyotomy is the standard treatment and relieves the obstruction by cutting the thickened muscle. Nursing care focuses on maintaining nutrition, hydration and family support until surgery can be performed.
Guidance and counseling are related but distinct concepts. Guidance involves directing and leading individuals, while counseling is a specialized guidance service that helps people learn about themselves and make plans for their future. The purpose of guidance and counseling is to help individuals develop their potential and make informed decisions about their education and career. It also aims to promote personal growth and good citizenship. Guidance is a continuous process of assisting individuals in developing to their fullest potential in a way that benefits themselves and society. It helps people set goals, solve problems, and make their own choices and decisions.
This lesson plan summarizes key information about celiac disease. Celiac disease is an autoimmune disorder triggered by gluten that damages the small intestine. It affects about 1 in 250 people globally. The main symptoms are related to malabsorption of nutrients from food as the small intestine is damaged. Treatment requires a lifelong gluten-free diet to allow the small intestine to heal. Teachers and students will learn about the definition, causes, clinical manifestations, diagnostic tests, management through a gluten-free diet, and potential complications of celiac disease.
This document discusses the analysis of quantitative and qualitative data in research. It describes the key steps in analyzing quantitative data, including data preparation, compilation, editing, coding, classification, tabulation, descriptive statistics, inferential statistics, and interpreting results. For qualitative data analysis, it outlines ordering and reducing data, summarizing data using various techniques, drawing conclusions, reporting findings, and establishing validity.
This document discusses juvenile diabetes mellitus (DM). It defines DM as a disorder of glucose intolerance caused by insufficient insulin production or action, leading to hyperglycemia. There are two main types: type 1 DM results from autoimmune destruction of beta cells and requires insulin therapy; type 2 DM is increasing in children due to obesity and lifestyle factors. Symptoms include frequent urination, thirst, weight loss, and fatigue. Treatment involves insulin therapy via injections or pumps, monitoring blood sugar levels, nutrition management, and education to prevent complications like diabetic ketoacidosis and hypoglycemia.
The document discusses various anatomical planes and movements used to describe the human body. There are three main planes: the sagittal plane divides the body into left and right sections, the coronal plane divides the body into front and back sections, and the transverse plane divides the body into upper and lower sections. Various movements like flexion, extension, abduction, adduction, and others are then defined in relation to these anatomical planes. Specific examples are provided to illustrate each term.
This document discusses cardiovascular diseases in children, including congenital heart defects. It defines congenital heart defects as heart conditions present at birth that affect the shape or function of the heart. Congenital heart defects are the most common type of birth defect and can cause blood to flow abnormally. The document outlines the different types of congenital heart defects, including cyanotic defects which reduce oxygen in the blood, and acyanotic defects which do not affect oxygen levels but can still cause complications. Common causes of congenital heart defects are discussed such as genetic factors, maternal health conditions like rubella, and environmental factors like smoking and alcohol during pregnancy.
The document provides an overview of reviewing literature for research. It defines a literature review, outlines its importance and purposes, and describes the types, sources, and steps involved in conducting a review. A literature review identifies what is already known about the research topic, determines gaps, and helps develop hypotheses. It involves comprehensively searching primary and secondary sources, creating an annotated bibliography organized by themes, and writing an introduction, body, and conclusion that synthesizes and evaluates the current state of knowledge on the topic. Conducting a high-quality, unbiased review is crucial for positioning new research and preventing duplication of efforts.
This document provides an overview of nursing research. It begins by outlining the objectives of the lecture, which are to define nursing research, discuss the nurse's role in research participation, and review the research process and types of research methods. It then discusses why research is important for nursing, highlighting that it allows the profession to grow and practice evidence-based care. The document reviews quantitative and qualitative research methods and different types within each. It also outlines the consumer-producer continuum in nursing research and defines key research terms and concepts.
The document provides an overview of the nursing research process. It discusses sources of knowledge, the scientific method, problem solving methods, differences between research and problem solving, definitions of research, the need for nursing research, characteristics of good research, qualities of a good researcher, and the phases of the research process including assessment, diagnosis, planning, implementation, and evaluation.
Hemophilia is a congenital bleeding disorder caused by deficiency of coagulation factors, mainly factor VIII and IX. It occurs in 1 in 5000 males and is transmitted by asymptomatic females. Symptoms include prolonged bleeding after injuries or surgery and bleeding into joints and muscles. Treatment involves replacing the missing coagulation factor through administration of specific factor concentrates during bleeding episodes. Nursing care focuses on preventing bleeding episodes, managing pain and swelling, and supporting family coping. Prognosis depends on severity of symptoms and availability of treatment.
Hemophilia is a congenital bleeding disorder caused by deficiency of coagulation factors, mainly factor VIII and IX. It occurs in 1 in 5000 males and is transmitted by asymptomatic females. Symptoms include prolonged bleeding after injuries or surgery and bleeding into joints and muscles. Treatment involves replacing the missing coagulation factor through administration of specific factor concentrates during bleeding episodes. Nursing care focuses on preventing bleeding episodes, managing pain and swelling, and supporting family coping. Prognosis depends on severity of symptoms and availability of treatment.
Thalassemia is a hereditary blood disorder characterized by a reduction in hemoglobin synthesis. It is classified as thalassemia major, intermediate, or minor depending on severity. Thalassemia major requires lifelong blood transfusions and chelation therapy for iron overload. Symptoms include anemia, jaundice, hepatosplenomegaly, bone deformities, and risk of infection. Management involves blood transfusions, chelation therapy, splenectomy, and supportive care. Prognosis is poor for thalassemia major but better for intermediate and minor forms with treatment.
Statistics can be categorized into descriptive and inferential types. Descriptive statistics summarize data from samples using measures like mean and standard deviation, while inferential statistics interpret descriptive statistics to draw conclusions. There are four levels of measurement scales: nominal for categories without ordering; ordinal for ordered categories; interval for equal intervals but arbitrary zero; and ratio for absolute zero. Proper use of statistics and scales allows for accurate data analysis across various fields.
Wilms tumor is a malignant renal tumor that occurs most often in children under 5 years old. It develops within the kidney and can spread to surrounding tissues and distant organs. Risk factors include African American race and family history. Staging is based on extent of tumor spread - early stage tumors are confined to the kidney while later stages involve lymph nodes, other abdominal organs, or distant metastasis. Treatment involves surgical removal of the affected kidney along with radiation and chemotherapy. Nursing care focuses on monitoring for side effects of treatment like bleeding, infections, and mouth sores as well as providing emotional support.
Kangaroo mother care is a method of caring for preterm and low birth weight infants that involves skin-to-skin contact between the infant and mother. It has been shown to improve infant health outcomes such as increased weight gain, improved breastfeeding success rates, better motor development and cognitive function scores, lower mortality rates, and decreased infections. Kangaroo mother care also benefits the mother through improved bonding and confidence in caring for the infant.
Anorectal malformations are developmental deformities of the lower end of the alimentary tract. They range from minor abnormalities occurring in 1 in 500 births to major abnormalities in 1 in 5000 births. They can be classified based on whether the rectum terminates above or below the levator ani muscle. Clinical manifestations include absence of stool passage, stool passing through unusual openings, and swollen belly. Diagnostic evaluations include physical exams, imaging, and tests to detect other abnormalities. Management involves medical treatments like nothing by mouth and colostomies as well as surgical procedures like posterior sagittal pull-through with the goal of restoring bowel function while preventing complications.
The Ballard score is used to determine gestational age by assessing 6 physical signs and 6 neuromuscular signs of maturity in newborns. Scores are given for each sign and added together, with higher scores indicating more maturity. The neuromuscular assessment examines posture, hand flexion, arm movement, knee bending, elbow movement, and foot movement. The physical assessment considers skin texture, hair, foot creases, breast tissue, eyes/ears, and genitals.
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Application to conduct study on research title 'Awareness and knowledge of oral cancer and precancer among dental outpatient in Klinik Pergigian Merlimau, Melaka'
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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.
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2. Introduction:
• Guidance & counselling are twin concepts & have emerged as essential elements of
every educational activity.
• Guidance & counselling are not synonymous term. Counselling is a part of
guidance.
• Guidance, in educational context, means to indicate, point out, show the way, lead
out & direct.
• Counselling is a specialized service of guidance. It is the process of helping
individuals learn more about themselves & their present & possible future situations
to make a substantial contribution to the society.
4. Definition:
• Guidance is a process of dynamic interpersonal relationships designed to
influence the attitudes and subsequent behavior of a person. –Good
• Guidance is assistance made available by personally qualified and adequately
trained personnel to an individual of any age to help him manage his own life
activities, to develop his own points of view, make his own decisions and
carry his own burdens- Crow and Crow.
• Guidance as a process through which an individual is able to solve their
problems and pursue a path suited to their abilities and aspirations – JM
Brewer.
5. • Guidance is a continuous process of helping the individual development in
the maximum of their capacity in the direction most beneficial to himself and
to society- stoops and wahlquist.
6. Purpose of Guidance
1. To bring confidence in selecting appropriate course of action for adjustment in
various walks of life.
2. Helping in a balanced development.
3. To help to determine the courses most appropriate to their needs and abilities.
4. To plan the future in the individual’s line of interest, abilities and social needs.
7.
8. Purpose of guidance and counselling
• The purpose of Guidance and Counselling can be related to 1) Individual and 2) Society.
1) Individual Related Purpose
(i) To help students recognize and develop their potentialities to achieve their educational aims and
objectives and improve academically and to develop positive attitude.
(ii) To help students aware about vocational and career opportunities available regionally, nationally
and internationally so as to help them make informed decisions from among various choices.
(iii) To help students for all round personal and social development on the basis of their interests,
abilities and resources.
(iv) To help students recognize their capacities, develop self confidence and adjust to academic,
school, family and personal problems.
9. 2) Society Related Purpose
(i) To develop good citizenship in students
(ii) To develop positive attitude towards family life and the society
(iii) To help in proper and best utilization of resources.
10. Other functions of guidance and counselling…
• To provide optimum development & well-being for individual.
• To help individuals adjust to themselves & the society.
• To help people understand themselves in relation to the world.
• To aid individuals in efficient decision making.
• To help individuals plan for a productive life in their social context by focusing on their
assets, skills, strengths & possibilities for further development.
• To bring about changes in the attitude & behavior of individuals.
11. Characteristics of Guidance
It is a process as
It helps every individual to help himself to recognize and use his inner resources,
To set goals
To make plans
To work out his own problems of development
It is a continuous process Choice & problem points are the distinctive concerns of guidance
It is the assistance to the individual in the process of development rather than a direction of that
development
Guidance is a service meant for all Guidance is both generalized & a specialized service.
12.
13. Short cut or Assumed mean method:
• When observations in data set are large in size, it is a laborious work to find
mean. To avoid this difficulty, short cut method is adopted.
• Assume arbitrary mean i.e., an value from data set (which will simplify the
calculations) and subtract this assumed mean from each observation.
• We get what is known as differences or deviations.
• Obtain mean for deviations by usual method.
14. Contd….
• Observations
• Original data: X1, X2, ………Xn
• Differences or X1-a, X2-a, …….. Xn-a
• Deviations: d1, d2,…..dn
• Where a is any value from dataset.
• Mean for deviations(d) = sum d/n. Thus, Mean of original data(X)=a+d
15. Example:
• In a series of 10 postmorterms following observations regarding weight (in
gms) of liver were found.
• 1420 1405 1425 1410 1415
1435 1430 1415 1445 1430
17. Computation of grouped data
• In Statistics, data plays a vital role in estimating the different types of parameters. To
draw any conclusions from the given data, first, we need to arrange the data in such a
way that one can perform suitable statistical experiments. We know that data can be
grouped into two ways, namely, Discrete and Continuous frequency distribution.
18. Discrete frequency distribution:
• Suppose we have X1, X2, …….. Xn observations with corresponding
frequencies f1, f2,…..,fn. The AM is defined as
• 𝑥 =
𝑓1𝑥1+𝑓2𝑥2+⋯+𝑓𝑛𝑥𝑛
𝑓1+𝑓2
+…+𝑓𝑛
• In notation form, we have
• MeanX= ∑(f.x)/ ∑f
= ∑(f.x)/N
= Sum (Frequency×observation)
• Total Frequency
19. Calculate the average number of children per
family from the following data:
NO: of children No: of families
0 30
1 52
2 60
3 65
4 18
5 10
6 05
20. Solution:
NO: of Children
(X)
NO: of families
(f)
Total NO: of Children
(f.x)
0 30 0×30=0
1 52 1×52=52
2 60 2×60=120
3 65 3×65=195
4 18 4×18=72
5 10 5×10=50
6 5 6×5=30
Total 240 519
22. Continuous frequency distribution:
• In continuous frequency distribution, the frequency is not associated with
any specified single value but spread over entire class.
• It creates difficulty for finding mid values X1, X2,….,Xn. To overcome this
difficulty, we make a reasonable assumption that the frequency is associated
with mid-value of class, or the frequency is distributed uniformly over the
entire class.
• Mean (X) = Sum(f.x)/ Sum(f)
23. The following are different steps to calculate average
for continuous frequency distribution
• Step 1- Write all class intervals serially in the first column and corresponding
frequency in the second column.
• Step 2- The mid values of each class interval are obtained by adding lower
and upper class interval and dividing resultant quantity by 2 and put these
values in third column.
• Step 3- Multiply each ‘f’ by corresponding X and write this product in fourth
column. The addition of this column gives sum(fx). i.e ∑f.x.
24. Notation form:
• X= Sum of fourth column
Sum of second column
= Sum (f.x)
Sum(f)
25. Example:
• Find the average age (in years) at the time of death in city A.
Age Interval NO: of Deaths
0-10 16
10-20 09
20-30 20
30-40 11
40-50 07
50-60 12
60-70 09
70-80 04
80-90 02
28. 2. MEDIAN
• The mean is unduly affected by extreme observations and cannot be
calculated for distribution with open end class and qualitative variables like
honesty, sex, religion etc.
• To overcome these drawbacks, we use other measures of central tendency
like median.
29. Definition:
• When all the observations of a variable are arranged in either ascending or
descending order, the middle observation is known as median. It divides the
whole data into two equal portions.
• In other words, 50% of the observations will be smaller than the median
while 50% of the observations will be larger than it.
30. Computation of Median:
Ungrouped Data:
• As discussed above, the median is one of the measures of central tendency,
which gives the middle value of the given data set.
• While finding the median of the ungrouped data, first arrange the given data
in ascending order, and then find the median value.
31. • If the total number of observations (n) is odd, then the median is (n+1)/2 th
observation.
• If the total number of observations (n) is even, then the median will be average of
n/2th and the (n/2)+1 th observation.
32. Example:
For example, 6, 4, 7, 3 and 2 is the given data set.
• To find the median of the given dataset, arrange it in ascending order.
• Therefore, the dataset is 2, 3, 4, 6 and 7.
• In this case, the number of observations is odd. (i.e) n= 5
• Hence, median = (n+1)/2 th observation.
• Median = (5+1)/2 = 6/2 = 3rd observation.
• Therefore, the median of the given dataset is 4
33. Calculation for grouped data
• In a grouped data, it is not possible to find the median for the given observation by
looking at the cumulative frequencies. The middle value of the given data will be in
some class interval. So, it is necessary to find the value inside the class interval that
divides the whole distribution into two halves.
• we have to find the median class.
• To find the median class, we have to find the cumulative frequencies of all the classes
and n/2. After that, locate the class whose cumulative frequency is greater than (nearest
to) n/2. The class is called the median class.
36. Solution:
• To find the median height, first, we need to find the class intervals and their corresponding frequencies.
• The given distribution is in the form of being less than type,145, 150 …and 165 gives the upper limit. Thus,
the class should be below 140, 140-145, 145-150, 150-155, 155-160 and 160-165.
• From the given distribution, it is observed that,
• 4 girls are below 140. Therefore, the frequency of class intervals below 140 is 4.
• 11 girls are there with heights less than 145, and 4 girls with height less than 140
• Hence, the frequency distribution for the class interval 140-145 = 11-4 = 7
• Likewise, the frequency of 145 -150= 29 – 11 = 18
• Frequency of 150-155 = 40-29 = 11
• Frequency of 155 – 160 = 46-40 = 6
• Frequency of 160-165 = 51-46 = 5
37. Therefore, the frequency distribution table along
with the cumulative frequencies are given below:
Class Intervals Frequency Cumulative Frequency
Below 140 4 4
140 – 145 7 11
145 – 150 18 29
150 – 155 11 40
155 – 160 6 46
160 – 165 5 51
38. Contd….
• Here, n= 51.
• Therefore, n/2 = 51/2 = 25.5
• Thus, the observations lie between the class interval 145-150, which is called the
median class.
• Therefore,
• Lower class limit = 145
• Class size, h = 5
• Frequency of the median class, f = 18
• Cumulative frequency of the class preceding the median class, cf = 11.
39. • Now, substituting the values in the formula, we get
• Median=145+(25.5−1118)×5
• Median = 145 + (72.5/18)
• Median = 145 + 4.03
• Median = 149.03.
• Therefore, the median height for the given data is 149. 03 cm.