Measure of dispersion
โข
3 likesโข346 views
Mean deviation is a measure of dispersion that calculates the average distance of data points from the central value (usually the mean). It is calculated by taking the absolute value of differences between each data point and the mean, then finding the average of those absolute differences. For a sample of data, the formula is the sum of the absolute values of (x - x-bar) divided by n. Mean deviation indicates how far data points typically are from the central value. It is useful for business applications but can be affected by outliers and does not retain positive and negative signs.
Report
Share
Report
Share
Recommended
Measure of Dispersion
This document defines and explains various measures of dispersion used in statistics, including range, quartile deviation, mean deviation, standard deviation, and Lorenz curve. It provides formulas for calculating each measure and includes examples to demonstrate how to apply the formulas. The key measures discussed are standard deviation, which is considered the most useful and popular, and measures of skewness, which quantify the symmetry of a distribution based on comparing the mean and third central moment.
MEASURE OF DISPERSION
This document discusses various measures of dispersion, including range, interquartile range, mean deviation, and standard deviation. It provides formulas and steps to calculate each measure. The standard deviation is described as the most common measure of dispersion, as it takes into account every observation in the data and measures the average deviation from the mean. The coefficient of variation is also introduced as a relative measure of variability, expressed as a percentage.
Measure of Dispersion
Measures of dispersion describe how spread out or clustered the data is. The range is the difference between the highest and lowest values. Standard deviation measures how far each value deviates from the mean by finding the deviations, squaring them, summing them, dividing by n-1, and taking the square root. It accounts for all data points unlike the range. To calculate standard deviation, first find the mean, then deviations from it, square those values, sum them, and divide the sum by n-1 before taking the square root.
Measures of dispersion
This document defines and compares several common measures of statistical dispersion, or variation within a data set from the average value. It explains that measures of dispersion include range, interquartile range, variance, standard deviation, and others. Each measure is defined and their advantages and disadvantages for describing how spread out numbers are in a data set are discussed. For example, the range is simple to calculate but influenced by outliers, while the standard deviation takes all values into account but can also be impacted by outliers.
Measure of dispersion by Neeraj Bhandari ( Surkhet.Nepal )
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
Measures of dispersion
This document defines and explains several common measures of dispersion used in statistics including range, mean absolute deviation, variance, standard deviation, and coefficient of variation. Range is the difference between the highest and lowest values. Mean absolute deviation measures the average distance between values and the mean. Variance and standard deviation both measure how spread out numbers are by taking the average of the squared distances from the mean, with standard deviation being the square root of variance. Coefficient of variation expresses standard deviation as a percentage of the mean to allow comparison between data sets with different means.
Statistics report THE RANGE
The range is the simplest measure of variability, defined as the difference between the highest and lowest values in a data set. It is quick to calculate but does not provide a full picture of the data distribution and can be strongly influenced by outliers. Other measures of variability include the average deviation, which calculates the average amount each score deviates from the mean, and the interquartile range, which is less influenced by outliers than the range. The interquartile range only considers data between the first and third quartiles and ignores half the data points.
Measures of Dispersion
This pused to find measures of dispersion.pt tells us what dispersion is, need of dispersion and the different methods
Recommended
Measure of Dispersion
This document defines and explains various measures of dispersion used in statistics, including range, quartile deviation, mean deviation, standard deviation, and Lorenz curve. It provides formulas for calculating each measure and includes examples to demonstrate how to apply the formulas. The key measures discussed are standard deviation, which is considered the most useful and popular, and measures of skewness, which quantify the symmetry of a distribution based on comparing the mean and third central moment.
MEASURE OF DISPERSION
This document discusses various measures of dispersion, including range, interquartile range, mean deviation, and standard deviation. It provides formulas and steps to calculate each measure. The standard deviation is described as the most common measure of dispersion, as it takes into account every observation in the data and measures the average deviation from the mean. The coefficient of variation is also introduced as a relative measure of variability, expressed as a percentage.
Measure of Dispersion
Measures of dispersion describe how spread out or clustered the data is. The range is the difference between the highest and lowest values. Standard deviation measures how far each value deviates from the mean by finding the deviations, squaring them, summing them, dividing by n-1, and taking the square root. It accounts for all data points unlike the range. To calculate standard deviation, first find the mean, then deviations from it, square those values, sum them, and divide the sum by n-1 before taking the square root.
Measures of dispersion
This document defines and compares several common measures of statistical dispersion, or variation within a data set from the average value. It explains that measures of dispersion include range, interquartile range, variance, standard deviation, and others. Each measure is defined and their advantages and disadvantages for describing how spread out numbers are in a data set are discussed. For example, the range is simple to calculate but influenced by outliers, while the standard deviation takes all values into account but can also be impacted by outliers.
Measure of dispersion by Neeraj Bhandari ( Surkhet.Nepal )
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
Measures of dispersion
This document defines and explains several common measures of dispersion used in statistics including range, mean absolute deviation, variance, standard deviation, and coefficient of variation. Range is the difference between the highest and lowest values. Mean absolute deviation measures the average distance between values and the mean. Variance and standard deviation both measure how spread out numbers are by taking the average of the squared distances from the mean, with standard deviation being the square root of variance. Coefficient of variation expresses standard deviation as a percentage of the mean to allow comparison between data sets with different means.
Statistics report THE RANGE
The range is the simplest measure of variability, defined as the difference between the highest and lowest values in a data set. It is quick to calculate but does not provide a full picture of the data distribution and can be strongly influenced by outliers. Other measures of variability include the average deviation, which calculates the average amount each score deviates from the mean, and the interquartile range, which is less influenced by outliers than the range. The interquartile range only considers data between the first and third quartiles and ignores half the data points.
Measures of Dispersion
This pused to find measures of dispersion.pt tells us what dispersion is, need of dispersion and the different methods
Measures of dispersion
This document discusses various measures of dispersion used in statistics. It begins by stating the learning objectives are to calculate common measures of dispersion from grouped and ungrouped data, including range, interquartile range, mean deviation, and standard deviation. It then defines dispersion as the scatteredness of data around the average and explains why measures of dispersion are needed, such as to determine the reliability of an average. The document proceeds to explain different measures of dispersion like range, interquartile range, mean deviation, and standard deviation. It concludes by stating that while no single measure is ideal, the standard deviation is the most important as it is most frequently used and considers the value of every observation.
Measures of Spread
The document discusses various statistical concepts including range, mean deviation, variance, and standard deviation. It provides formulas and steps to calculate each measure. The range is the distance between the highest and lowest values. Mean deviation measures the average deviation from the mean. Variance is the average of the squared deviations from the mean and standard deviation is the square root of the variance, representing the average distance from the mean. Examples are given to demonstrate calculating each measure for both ungrouped and grouped data.
2. measures of dis[persion
This document discusses various measures of dispersion used to quantify how spread out or clustered data values are around a central tendency. It defines key terms like range, variance, standard deviation, and coefficient of variation. Examples are provided to demonstrate how to calculate these measures for both individual and grouped data. The normal distribution curve is also discussed to show how dispersion relates to the percentage of values that fall within a given number of standard deviations from the mean.
Mean Deviation
This document defines and provides the formula for calculating mean deviation, which is a measure of variation that uses all the scores in a distribution. It is more reliable than range. Mean deviation is calculated by finding the absolute difference between each score and the mean, summing the absolute differences, and dividing by the number of observations. Two examples of calculating mean deviation for sets of data are provided, along with exercises asking students to find the mean deviation of additional data sets and define standard deviation.
Measures of dispersion
Measures of Dispersion: Absolute and Relative Measures of Dispersion, their computation & properties.
3.2 measures of variation
This document discusses measures of variation used to assess how far data points are from the average or mean. It defines key terms like range, variance, and standard deviation. Variance measures the mathematical dispersion of data relative to the mean, while standard deviation gives a value in the original units of measurement, making it easier to interpret. Formulas are provided for calculating sample variance and standard deviation versus population variance and standard deviation. Chebyshev's Theorem is introduced, stating that a certain minimum percentage of data must fall within a specified number of standard deviations of the mean. An example applies these concepts.
Measures of dispersion
The document discusses various measures of central tendency, dispersion, and shape used to describe data numerically. It defines terms like mean, median, mode, variance, standard deviation, coefficient of variation, range, interquartile range, skewness, and quartiles. It provides formulas and examples of how to calculate these measures from data sets. The document also discusses concepts like normal distribution, empirical rule, and how measures of central tendency and dispersion do not provide information about the shape or symmetry of a distribution.
Dispersion
Introduction of Dispersion, measures, ranges, quartile deviation, mean deviation, standard deviation,
Measures of dispersion
This document discusses measures of dispersion, which indicate how spread out or variable observed data values are around a central measure like the mean. It describes two main types of dispersion measures: range measures, which consider the distance between extreme values, and deviation measures, which consider the average distance of values from the mean. Specific measures discussed include the range, interquartile range, variance, and standard deviation. The standard deviation is presented as the most commonly used measure because it is easily interpreted in the original units of measurement and captures how far values typically deviate from the mean.
State presentation2
This document summarizes various statistical measures used to analyze and describe data distributions, including measures of central tendency (mean, median, mode), dispersion (range, standard deviation, variance), skewness, and kurtosis. It provides formulas and methods for calculating each measure along with interpretations of the results. Measures of central tendency provide a single value to represent the center of the data set. Measures of dispersion describe how spread out or varied the data values are. Skewness and kurtosis measure the symmetry and peakedness of distributions compared to the normal curve.
Measure of dispersion statistics
This document presents information on measures of dispersion used to analyze student height data. It defines measure of dispersion as the average deviation from a central value to describe data spread. Formulas are provided for range, coefficient of range, mean deviation, variance, and standard deviation. Raw height data for 25 students is analyzed, finding a range of 23 inches, mean of 64.7 inches, and standard deviation of approximately 5 inches. Measures of dispersion are used to understand data homogeneity, mean reliability, and distribution comparisons.
Measures of Variation or Dispersion
Dr Athar Khan
Associate Professor
Liaquat College of Medicine & Dentistry
Karachi, Pakistan 2019
matharm@yahoo.com
Absolute Measures of dispersion
MEANING OF DISPERSION In Statistics,
This term is used commonly to mean scatter,
Deviation, Fluctuation, Spread or variability of data.
The degree to which the individual values of the variate scatter away from the average or the central value, is called a dispersion
Definition of dispersion
This document discusses various measures of dispersion used to quantify how spread out or variable a data set is. It defines dispersion and explains the purposes of measuring it. The key measures of dispersion discussed are range, quartile deviation, mean deviation, variance, standard deviation, and coefficient of variation. Formulas are provided for calculating each measure along with their merits and limitations. The conclusion emphasizes that measures of dispersion are useful for comparing distributions and further statistical analysis.
Measures of Dispersion
If everything were the same, we would have no need of statistics. But, people's heights, ages, etc., do vary. We often need to measure the extent to which scores in a dataset differ from each other. Such a measure is called the dispersion of a distribution.
Lesson03_new
This document summarizes various statistical measures used to describe and analyze numerical data, including measures of central tendency (mean, median, mode), measures of variation (range, interquartile range, variance, standard deviation, coefficient of variation), and ways to describe the shape of distributions (symmetric vs. skewed using box-and-whisker plots). It provides definitions and formulas for calculating these common statistical concepts.
Chapter03
This document outlines key concepts in descriptive statistics including measures of central tendency (mean, median, mode), measures of variability (range, standard deviation, variance), and measures of shape (skewness, kurtosis). It defines these terms and concepts, provides examples of how to compute them, and explains how to interpret and compare distributions based on these measures. The learning objectives are to understand and be able to calculate various descriptive statistics and use them to analyze and describe data distributions.
Variability
The document discusses variability and measures of variability. It defines variability as a quantitative measure of how spread out or clustered scores are in a distribution. The standard deviation is introduced as the most commonly used measure of variability, as it takes into account all scores in the distribution and provides the average distance of scores from the mean. Properties of the standard deviation are examined, such as how it does not change when a constant is added to all scores but does change when all scores are multiplied by a constant.
Standard deviation by nikita
Dr. R.K. Rao presented a seminar on standard deviation. The seminar covered the definition of standard deviation as the positive square root of the arithmetic mean of the squared deviations from the mean. It discussed various methods for calculating standard deviation for individual data series, discrete series, and grouped series. The seminar also reviewed the uses and merits of standard deviation as a statistical measure of dispersion.
Measures of dispersions
1. The document discusses various measures of dispersion used to quantify how spread out or variable a data set is. It describes measures such as range, mean deviation, variance, and standard deviation.
2. It also discusses relative measures of dispersion like the coefficient of variation, which allows comparison of variability between data sets with different units or averages. The coefficient of variation expresses variability as a percentage of the mean.
3. Additional concepts covered include skewness, which refers to the asymmetry of a distribution, and kurtosis, which measures the peakedness of a distribution compared to a normal distribution. Positive or negative skewness and leptokurtic, mesokurtic, or platykurtic k
descriptive statistics- 1.pptx
This document discusses descriptive statistics techniques for quantitative data analysis. It defines two main approaches in statistics - descriptive statistics which are used to summarize and organize data, and inferential statistics which are used to make inferences about populations from samples. Descriptive statistics techniques discussed include visual displays, measures of central tendency (mean, median, mode), and measures of variability or dispersion (range, variance, standard deviation). Formulas for calculating various measures are provided along with explanations of their advantages and disadvantages.
5.DATA SUMMERISATION.ppt
Jsjjdjd dikdjdjddn xjdjdjjd dndjcnjfj. Dndjdjdjd djdjdjjsnjs dbdbdb Ududu djdjdd djdjdjjd fjdios djdjdjd dkdjdjdnjd dbdjdd bdbdjd d dbdjdb dndjdjdjd
More Related Content
What's hot
Measures of dispersion
This document discusses various measures of dispersion used in statistics. It begins by stating the learning objectives are to calculate common measures of dispersion from grouped and ungrouped data, including range, interquartile range, mean deviation, and standard deviation. It then defines dispersion as the scatteredness of data around the average and explains why measures of dispersion are needed, such as to determine the reliability of an average. The document proceeds to explain different measures of dispersion like range, interquartile range, mean deviation, and standard deviation. It concludes by stating that while no single measure is ideal, the standard deviation is the most important as it is most frequently used and considers the value of every observation.
Measures of Spread
The document discusses various statistical concepts including range, mean deviation, variance, and standard deviation. It provides formulas and steps to calculate each measure. The range is the distance between the highest and lowest values. Mean deviation measures the average deviation from the mean. Variance is the average of the squared deviations from the mean and standard deviation is the square root of the variance, representing the average distance from the mean. Examples are given to demonstrate calculating each measure for both ungrouped and grouped data.
2. measures of dis[persion
This document discusses various measures of dispersion used to quantify how spread out or clustered data values are around a central tendency. It defines key terms like range, variance, standard deviation, and coefficient of variation. Examples are provided to demonstrate how to calculate these measures for both individual and grouped data. The normal distribution curve is also discussed to show how dispersion relates to the percentage of values that fall within a given number of standard deviations from the mean.
Mean Deviation
This document defines and provides the formula for calculating mean deviation, which is a measure of variation that uses all the scores in a distribution. It is more reliable than range. Mean deviation is calculated by finding the absolute difference between each score and the mean, summing the absolute differences, and dividing by the number of observations. Two examples of calculating mean deviation for sets of data are provided, along with exercises asking students to find the mean deviation of additional data sets and define standard deviation.
Measures of dispersion
Measures of Dispersion: Absolute and Relative Measures of Dispersion, their computation & properties.
3.2 measures of variation
This document discusses measures of variation used to assess how far data points are from the average or mean. It defines key terms like range, variance, and standard deviation. Variance measures the mathematical dispersion of data relative to the mean, while standard deviation gives a value in the original units of measurement, making it easier to interpret. Formulas are provided for calculating sample variance and standard deviation versus population variance and standard deviation. Chebyshev's Theorem is introduced, stating that a certain minimum percentage of data must fall within a specified number of standard deviations of the mean. An example applies these concepts.
Measures of dispersion
The document discusses various measures of central tendency, dispersion, and shape used to describe data numerically. It defines terms like mean, median, mode, variance, standard deviation, coefficient of variation, range, interquartile range, skewness, and quartiles. It provides formulas and examples of how to calculate these measures from data sets. The document also discusses concepts like normal distribution, empirical rule, and how measures of central tendency and dispersion do not provide information about the shape or symmetry of a distribution.
Dispersion
Introduction of Dispersion, measures, ranges, quartile deviation, mean deviation, standard deviation,
Measures of dispersion
This document discusses measures of dispersion, which indicate how spread out or variable observed data values are around a central measure like the mean. It describes two main types of dispersion measures: range measures, which consider the distance between extreme values, and deviation measures, which consider the average distance of values from the mean. Specific measures discussed include the range, interquartile range, variance, and standard deviation. The standard deviation is presented as the most commonly used measure because it is easily interpreted in the original units of measurement and captures how far values typically deviate from the mean.
State presentation2
This document summarizes various statistical measures used to analyze and describe data distributions, including measures of central tendency (mean, median, mode), dispersion (range, standard deviation, variance), skewness, and kurtosis. It provides formulas and methods for calculating each measure along with interpretations of the results. Measures of central tendency provide a single value to represent the center of the data set. Measures of dispersion describe how spread out or varied the data values are. Skewness and kurtosis measure the symmetry and peakedness of distributions compared to the normal curve.
Measure of dispersion statistics
This document presents information on measures of dispersion used to analyze student height data. It defines measure of dispersion as the average deviation from a central value to describe data spread. Formulas are provided for range, coefficient of range, mean deviation, variance, and standard deviation. Raw height data for 25 students is analyzed, finding a range of 23 inches, mean of 64.7 inches, and standard deviation of approximately 5 inches. Measures of dispersion are used to understand data homogeneity, mean reliability, and distribution comparisons.
Measures of Variation or Dispersion
Dr Athar Khan
Associate Professor
Liaquat College of Medicine & Dentistry
Karachi, Pakistan 2019
matharm@yahoo.com
Absolute Measures of dispersion
MEANING OF DISPERSION In Statistics,
This term is used commonly to mean scatter,
Deviation, Fluctuation, Spread or variability of data.
The degree to which the individual values of the variate scatter away from the average or the central value, is called a dispersion
Definition of dispersion
This document discusses various measures of dispersion used to quantify how spread out or variable a data set is. It defines dispersion and explains the purposes of measuring it. The key measures of dispersion discussed are range, quartile deviation, mean deviation, variance, standard deviation, and coefficient of variation. Formulas are provided for calculating each measure along with their merits and limitations. The conclusion emphasizes that measures of dispersion are useful for comparing distributions and further statistical analysis.
Measures of Dispersion
If everything were the same, we would have no need of statistics. But, people's heights, ages, etc., do vary. We often need to measure the extent to which scores in a dataset differ from each other. Such a measure is called the dispersion of a distribution.
Lesson03_new
This document summarizes various statistical measures used to describe and analyze numerical data, including measures of central tendency (mean, median, mode), measures of variation (range, interquartile range, variance, standard deviation, coefficient of variation), and ways to describe the shape of distributions (symmetric vs. skewed using box-and-whisker plots). It provides definitions and formulas for calculating these common statistical concepts.
Chapter03
This document outlines key concepts in descriptive statistics including measures of central tendency (mean, median, mode), measures of variability (range, standard deviation, variance), and measures of shape (skewness, kurtosis). It defines these terms and concepts, provides examples of how to compute them, and explains how to interpret and compare distributions based on these measures. The learning objectives are to understand and be able to calculate various descriptive statistics and use them to analyze and describe data distributions.
Variability
The document discusses variability and measures of variability. It defines variability as a quantitative measure of how spread out or clustered scores are in a distribution. The standard deviation is introduced as the most commonly used measure of variability, as it takes into account all scores in the distribution and provides the average distance of scores from the mean. Properties of the standard deviation are examined, such as how it does not change when a constant is added to all scores but does change when all scores are multiplied by a constant.
Standard deviation by nikita
Dr. R.K. Rao presented a seminar on standard deviation. The seminar covered the definition of standard deviation as the positive square root of the arithmetic mean of the squared deviations from the mean. It discussed various methods for calculating standard deviation for individual data series, discrete series, and grouped series. The seminar also reviewed the uses and merits of standard deviation as a statistical measure of dispersion.
Measures of dispersions
1. The document discusses various measures of dispersion used to quantify how spread out or variable a data set is. It describes measures such as range, mean deviation, variance, and standard deviation.
2. It also discusses relative measures of dispersion like the coefficient of variation, which allows comparison of variability between data sets with different units or averages. The coefficient of variation expresses variability as a percentage of the mean.
3. Additional concepts covered include skewness, which refers to the asymmetry of a distribution, and kurtosis, which measures the peakedness of a distribution compared to a normal distribution. Positive or negative skewness and leptokurtic, mesokurtic, or platykurtic k
What's hot (20)
Similar to Measure of dispersion
descriptive statistics- 1.pptx
This document discusses descriptive statistics techniques for quantitative data analysis. It defines two main approaches in statistics - descriptive statistics which are used to summarize and organize data, and inferential statistics which are used to make inferences about populations from samples. Descriptive statistics techniques discussed include visual displays, measures of central tendency (mean, median, mode), and measures of variability or dispersion (range, variance, standard deviation). Formulas for calculating various measures are provided along with explanations of their advantages and disadvantages.
5.DATA SUMMERISATION.ppt
Jsjjdjd dikdjdjddn xjdjdjjd dndjcnjfj. Dndjdjdjd djdjdjjsnjs dbdbdb Ududu djdjdd djdjdjjd fjdios djdjdjd dkdjdjdnjd dbdjdd bdbdjd d dbdjdb dndjdjdjd
Statistics in research
Descriptive statistics are used to summarize and describe characteristics of a data set. It includes measures of central tendency like mean, median, and mode, measures of variability like range and standard deviation, and the distribution of data through histograms. Inferential statistics are used to generalize results from a sample to the population it represents through estimation of population parameters and hypothesis testing. Correlation and regression analysis are used to study relationships between two or more variables.
Empirics of standard deviation
the mean,median, mode, their uses and how reliable they can be. the upgrade from variance to standard deviation
S1 pn
This document discusses measures of central tendency and different methods for calculating averages. It begins by defining central tendency as a single value that represents the characteristics of an entire data set. Three common measures of central tendency are introduced: the mean, median, and mode. The document then focuses on explaining how to calculate the arithmetic mean, or average, including the direct method, shortcut method, and how it applies to discrete and continuous data series. Weighted averages are also covered. In summary, the document provides an overview of key concepts in measures of central tendency and how to calculate various types of averages.
MEAN DEVIATION
The mean deviation is a measure of how spread out values are from the average. It is calculated by:
1) Finding the mean of all values.
2) Calculating the distance between each value and the mean.
3) Taking the average of those distances. This provides the mean deviation, which tells us how far on average values are from the central mean. Examples show calculating mean deviation for both grouped and ungrouped data sets.
Measures of Central Tendency and Dispersion (Week-07).pptx
The document discusses various measures of central tendency and distribution used in statistics. It defines central tendency as a value that represents the center of a data set, such as the mean, median, and mode. It then describes different averages that can be used as measures of central tendency, including the arithmetic mean, median, mode, geometric mean, harmonic mean, and weighted mean. The document also covers measures of dispersion like range, mean deviation, standard deviation, variance, and quartile deviation. It provides formulas and examples to calculate these statistical concepts.
Lesson 7 measures of dispersion part 2
This document provides an introduction and overview of key concepts related to measures of variability and dispersion in statistics. It discusses average absolute deviation, which is the mean of the absolute values of deviations from the data's mean or median. It then covers standard deviation, which is the positive square root of the average of squared deviations from the mean, and is the most widely used measure of dispersion. Formulas and steps for calculating average absolute deviation and standard deviation are provided. An example of calculating standard deviation using a data set of ages is worked through.
Measures of dispersion range qd md
This document discusses various measures of dispersion in statistics. It defines dispersion as the extent to which items in a data set vary from the central value. Some key measures of dispersion discussed include range, interquartile range, quartile deviation, mean deviation, and standard deviation. Formulas and examples are provided for calculating range, quartile deviation, and mean deviation from data sets. The objectives, properties, merits and demerits of each measure are outlined.
CABT Math 8 measures of central tendency and dispersion
This document provides an introduction to statistics. It discusses what statistics is, the two main branches of statistics (descriptive and inferential), and the different types of data. It then describes several key measures used in statistics, including measures of central tendency (mean, median, mode) and measures of dispersion (range, mean deviation, standard deviation). The mean is the average value, the median is the middle value, and the mode is the most frequent value. The range is the difference between highest and lowest values, the mean deviation is the average distance from the mean, and the standard deviation measures how spread out values are from the mean. Examples are provided to demonstrate how to calculate each measure.
Lesson 23 planning data analyses using statistics
This document discusses strategies for analyzing collected data, including descriptive and inferential statistics. Descriptive statistics like measures of central tendency (mean, median, mode) and dispersion (range, standard deviation) are used to summarize and describe data. Inferential statistics like t-tests, ANOVA, and tests of correlation can analyze relationships, differences between groups, and make generalizations from samples to populations. The document provides formulas and examples of how to calculate and interpret various statistical measures.
Measures of central tendency and dispersion
The document discusses various measures of central tendency and dispersion used in statistical analysis. It defines measures of central tendency like arithmetic mean, median and mode, and provides their formulas and properties. It also discusses measures of dispersion such as range, mean deviation, standard deviation, variance and their characteristics. The document provides examples and steps to calculate various averages and measures of dispersion for a given data set.
Ders 1 mean mod media st dev.pptx
This document discusses computing statistics for single-variable data. It describes six common statistics: three measures of central tendency (mean, median, mode), two measures of spread (variance and standard deviation), and one measure of symmetry (skewness). Formulas are provided for calculating each statistic. Examples are given for computing statistics for both discrete and continuous data sets.
Presentation slide on central tendency
Central tendency refers to typical or average values in a data set. There are three main measures of central tendency: mean, median, and mode. The mean is the average and is calculated by summing all values and dividing by the total number. The median is the middle value when data is arranged from lowest to highest. The mode is the most frequently occurring value. These measures were calculated for two sample data sets to illustrate how to find the mean, median, and mode from grouped and ungrouped data.
The Mean Deviation.pptx
The mean deviation is defined as the average of the absolute deviations of values from the average or mean. It is calculated by taking the deviations without considering positive or negative signs. For a set of values X1, X2, X3, etc., the mean deviation is calculated by summing the absolute value of each value minus the mean, and dividing by n. In an example of grouped data, the mean deviation was calculated to be 6.12 for a data set where the mean was 55.08.
MEASURES OF DISPERSION I BSc Botany .ppt
This document discusses various statistical measures of dispersion. It defines dispersion as how spread out or varied a set of numerical data is from the average value. There are two types of measures - absolute, which have the same units as the data, and relative, which are unit-less and used to compare datasets. Examples of measures discussed include range, mean deviation, standard deviation, variance, and coefficient of variation. The document also covers frequency distributions, binomial distributions, chi-square tests, and data analysis processes.
Bio statistics
1. The document discusses key concepts in biostatistics including measures of central tendency, dispersion, correlation, regression, and sampling.
2. Measures of central tendency described are the mean, median, and mode. Measures of dispersion include range, standard deviation, and quartile deviation.
3. The importance of statistical analysis for living organisms in areas like medicine, biology and public health is highlighted. Examples are provided to demonstrate calculation of statistical measures.
Measures of Variation
๏บPlease Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 3: Describing, Exploring, and Comparing Data
3.2: Measures of Variation
Measure of Central Tendency
What is mean?
Different types of mean, & their relation.
What is median?
What is mode?
Relation between mean, median, mode.
Usage in Business.
Statistics
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.
Similar to Measure of dispersion (20)
Measures of Central Tendency and Dispersion (Week-07).pptx
Measures of Central Tendency and Dispersion (Week-07).pptx
ย
CABT Math 8 measures of central tendency and dispersion
CABT Math 8 measures of central tendency and dispersion
ย
More from Sana butt
skills of Public relation officer in an education institution
A public relations officer in an educational institution has many important responsibilities, including maintaining positive relationships between the institution and the surrounding community, students, faculty, staff, and alumni. They help advertise the institution to expand its applicant pool, organize large events to publicize achievements and brand recognition, and create and uphold a favorable image through press releases and social media strategies. Having a public relations officer is essential for effective management of a school's reputation and marketing of its programs and accomplishments.
Knowledge gap theory
The Knowledge Gap Hypothesis proposes that as the mass media disseminates information to a social system, those with higher socioeconomic status will acquire and understand the information faster and more thoroughly than those with lower socioeconomic status, widening the gap in knowledge between the two groups. The hypothesis was first proposed in 1970 and suggests this gap is more likely to occur on topics of general interest covered by mass media, like public affairs and science, rather than more specialized topics. Factors that can contribute to the knowledge gap include differences in communication skills, previous knowledge, social contacts, and how the mass media system is oriented.
Consumer Audience
Cultural, social, psychological, and behavioral factors influence consumer behavior and decision-making. Market segmentation involves dividing the overall market into distinct subgroups based on characteristics like demographics, geographics, psychographics, and behaviors. When targeting specific market segments, companies profile the segments to understand their needs, attitudes, and lifestyles in order to develop effective marketing strategies.
Panama
The document summarizes key events in the Panama Papers case in Pakistan. It discusses:
1) The filing of petitions by PTI and others seeking PM Sharif's disqualification.
2) Ongoing Supreme Court hearings over several months where both sides presented arguments and evidence.
3) The formation of a Joint Investigation Team that submitted a report requesting corruption references against Sharif and family.
4) The Supreme Court's final verdict in July 2017 that disqualified Nawaz Sharif from office and ordered corruption references filed against him.
Brain psychology
The central nervous system is made up of the brain and spinal cord. The brain processes sensory information and controls the body. It is made up of the forebrain, midbrain, and hindbrain. The forebrain includes the thalamus, hypothalamus, limbic system, and cerebrum. Each part of the brain controls different functions like vision, hearing, movement, emotion, and memory. The brain is divided into left and right hemispheres that control opposite sides of the body and specialize in different tasks. Various techniques like lesions, stimulation, EEG, CAT scans, PET scans, and MRI are used to study the functions of different brain areas.
Media or technological determinism
Marshall McLuhan proposed that society progresses through four phases shaped by dominant communication technologies: the tribal oral phase, literary visual phase, print industrial phase, and current electronic global village phase. McLuhan's influential theory was that the medium itself, not the content it carries, should be the focus of study as the medium shapes how people perceive and understand information. He described how "hot" and "cold" media differentially engage audiences.
Two step flow theory
The two-step flow theory of communication proposes that influence flows from mass media to opinion leaders and then from opinion leaders to the general public. Researchers found that during a presidential election campaign, people were more influenced by informal interpersonal communication with opinion leaders than by direct exposure to media messages. The theory claims information moves in two stages: first, opinion leaders are exposed to media and gain information; second, opinion leaders pass this information along with their own interpretations to other people in their social networks.
Psychology
This document discusses theories of language acquisition and development. It describes three main theories:
1) Nativist theory proposes that language is innate and humans are born with an innate language acquisition device. Chomsky was a key proponent and argued language is too complex to be learned without innate mechanisms.
2) Interactionist theory sees both nature and nurture as important, with language acquired through interactions between innate capabilities and environmental exposure. Vygotsky and Bruner contributed interactionist perspectives.
3) Behaviorist theory views language as a set of habits learned through conditioning and reinforcement, influenced by the work of Pavlov on classical conditioning and Skinner on operant conditioning. Overall the document examines the cognitive and environmental factors
Indian film history
Bollywood refers to the Hindi-language film industry based in Mumbai, India. It derives its name from a portmanteau of Bombay (the former name of Mumbai) and Hollywood. Bollywood is the largest film producer in India and one of the largest film industries in the world in terms of films and tickets produced and sold annually. The Indian film industry has evolved greatly from the silent films of the 1910s to become a technologically advanced, globally recognized industry producing over 1000 films annually across multiple regional languages including Hindi, Bengali, Tamil, Telugu, and others. Key events in its history include the first talkie film in 1931, the arrival of color film in the late 1930s, and the "
Say no to drugs
The document discusses the issue of drug addiction in Pakistan. It states that approximately 8.9 million people in Pakistan are drug addicted, with heroin and cannabis being the most commonly used drugs. Drug use is particularly prevalent among youth and students, with over half of students at some private schools and universities reporting using drugs. The document identifies several factors that can increase the risk of drug addiction, such as family and social issues, peer pressure, and easy access and availability of drugs. It maintains that movies sometimes glamorize drug use and can influence youth as well. The document outlines several negative health impacts of drug addiction and proposes solutions like education campaigns, treatment programs, and increasing recreational opportunities for youth to address the problem of rising drug addiction
Codes of Ethics
Reporting involves informing others through general assignment reporting or beat reporting while adhering to ethical standards. Beat reporting focuses on a specific field like crime, where reporters develop expertise in their area. Crime reporting communicates information about incidents accurately and completely while respecting privacy and pursuing facts rather than sensationalism. Reporters find information about crimes and connected individuals through police contacts, documents, and online and on-the-ground sources.
Importance and objective of editorial
An editorial presents a newspaper's opinion on issues and aims to guide public opinion. It reflects the majority view of the newspaper's editorial board. The key objectives of an editorial are to logically analyze issues, explain events and their implications, hold rulers accountable, promote political awareness and national welfare, and adapt critical thinking. An editorial represents the public voice and acts as a source of discussion on important matters.
mass media ethics - needs and scope
This document discusses the needs and scope of mass media ethics. It provides an overview of codes of ethics for mass media, including that codes set standards for conduct, ensure internal rather than external oversight, and help practitioners focus on important principles. However, codes also face criticisms like lack of enforcement and ambiguity. The document then outlines general themes in media codes, including objectivity, truth, minimizing harm, and accuracy.
More from Sana butt (13)
skills of Public relation officer in an education institution
skills of Public relation officer in an education institution
ย
Recently uploaded
How to Setup Default Value for a Field in Odoo 17
In Odoo, we can set a default value for a field during the creation of a record for a model. We have many methods in odoo for setting a default value to the field.
Information and Communication Technology in Education
(๐๐๐ ๐๐๐) (๐๐๐ฌ๐ฌ๐จ๐ง 2)-๐๐ซ๐๐ฅ๐ข๐ฆ๐ฌ
๐๐ฑ๐ฉ๐ฅ๐๐ข๐ง ๐ญ๐ก๐ ๐๐๐ ๐ข๐ง ๐๐๐ฎ๐๐๐ญ๐ข๐จ๐ง:
Students will be able to explain the role and impact of Information and Communication Technology (ICT) in education. They will understand how ICT tools, such as computers, the internet, and educational software, enhance learning and teaching processes. By exploring various ICT applications, students will recognize how these technologies facilitate access to information, improve communication, support collaboration, and enable personalized learning experiences.
๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ ๐ญ๐ก๐ ๐ซ๐๐ฅ๐ข๐๐๐ฅ๐ ๐ฌ๐จ๐ฎ๐ซ๐๐๐ฌ ๐จ๐ง ๐ญ๐ก๐ ๐ข๐ง๐ญ๐๐ซ๐ง๐๐ญ:
-Students will be able to discuss what constitutes reliable sources on the internet. They will learn to identify key characteristics of trustworthy information, such as credibility, accuracy, and authority. By examining different types of online sources, students will develop skills to evaluate the reliability of websites and content, ensuring they can distinguish between reputable information and misinformation.
220711130100 udita Chakraborty Aims and objectives of national policy on inf...
Aims and objectives of national policy on information and communication technology(ICT) in school education in india
NIPER 2024 MEMORY BASED QUESTIONS.ANSWERS TO NIPER 2024 QUESTIONS.NIPER JEE 2...
NIPER JEE PYQ
NIPER JEE QUESTIONS
MOST FREQUENTLY ASK QUESTIONS
NIPER MEMORY BASED QUWSTIONS
A Free 200-Page eBook ~ Brain and Mind Exercise.pptx
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin ๐๐ค๐ค๐ฅฐ
HYPERTENSION - SLIDE SHARE PRESENTATION.
IT WILL BE HELPFULL FOR THE NUSING STUDENTS
IT FOCUSED ON MEDICAL MANAGEMENT AND NURSING MANAGEMENT.
HIGHLIGHTS ON HEALTH EDUCATION.
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptx
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
How to Fix [Errno 98] address already in use
This slide will represent the cause of the error โ[Errno 98] address already in useโ and the troubleshooting steps to resolve this error in Odoo.
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
220711130082 Srabanti Bag Internet Resources For Natural Science
Internet resources for natural science
ู
ุตุญู ุงููุฑุงุกุงุช ุงูุนุดุฑ ุฃุนุฏ ุฃุญุฑู ุงูุฎูุงู ุณู
ูุฑ ุจุณูููู.pdf
ู
ุตุญู ุงููุฑุงุกุงุช ุงูุนุดุฑ ุฃุนุฏ ุฃุญุฑู ุงูุฎูุงู ุณู
ูุฑ ุจุณูููู.pdfุณู
ูุฑ ุจุณูููู
ู
ุตุญู ุฃุญุฑู ุงูุฎูุงู ูููุฑุงุก ุงูุนุดุฑุฉุฃุนุฏ ุฃุญุฑู ุงูุฎูุงู ุจุงูุชูููู ูุตูุง ุณู
ูุฑ ุจุณูููู ุบูุฑ ุงููู ููย
A Visual Guide to 1 Samuel | A Tale of Two Hearts
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
Recently uploaded (20)
Information and Communication Technology in Education
Information and Communication Technology in Education
ย
220711130100 udita Chakraborty Aims and objectives of national policy on inf...
220711130100 udita Chakraborty Aims and objectives of national policy on inf...
ย
NIPER 2024 MEMORY BASED QUESTIONS.ANSWERS TO NIPER 2024 QUESTIONS.NIPER JEE 2...
NIPER 2024 MEMORY BASED QUESTIONS.ANSWERS TO NIPER 2024 QUESTIONS.NIPER JEE 2...
ย
A Free 200-Page eBook ~ Brain and Mind Exercise.pptx
A Free 200-Page eBook ~ Brain and Mind Exercise.pptx
ย
SWOT analysis in the project Keeping the Memory @live.pptx
SWOT analysis in the project Keeping the Memory @live.pptx
ย
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptx
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptx
ย
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
Elevate Your Nonprofit's Online Presence_ A Guide to Effective SEO Strategies...
ย
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...
ย
220711130082 Srabanti Bag Internet Resources For Natural Science
220711130082 Srabanti Bag Internet Resources For Natural Science
ย
ู
ุตุญู ุงููุฑุงุกุงุช ุงูุนุดุฑ ุฃุนุฏ ุฃุญุฑู ุงูุฎูุงู ุณู
ูุฑ ุจุณูููู.pdf
ู
ุตุญู ุงููุฑุงุกุงุช ุงูุนุดุฑ ุฃุนุฏ ุฃุญุฑู ุงูุฎูุงู ุณู
ูุฑ ุจุณูููู.pdf
ย
Measure of dispersion
- 3. Definition ๏A measure of dispersion, computed by taking arithmetic mean of abslute vales of the deviation of the functional value from the central value usually the mean or mode. ๏The mean of the distance of each value from their mean.
- 4. Formula ๏Formula of mean deviation is ๏For sample data Mean Deviation= ฮฃ(๐โ ๐) ๐ ๏For frequency distribution M.D= โ๐(๐โ ๐ โ๐
- 5. Where X= Each value or observation ๐= Mean n= Number of value
- 6. Co-efficient of mean deviation ๏It is defined as ratio of mean deviation to the average used in the calculation of mean deviation. ๏For sample data Co-efficient of mean deviation = ๐.๐ ๐ฟ
- 7. ๏For population data Co-efficient of mean deviation = ๐.๐ ๐
- 8. Example ๏ 3,6,6,7,8,11,15,16 find the mean deviation Solution: Step#1 find simple mean or average Mean = 3+6+6+7+8+11+15+11+16/9 Mean = 72/9 Mean = 7
- 9. Steps ๏Three steps to find the mean i. Find the mean of all values ii. Find the difference of each value from that mean iii. Find the mean of those differences
- 10. Example 271,354,296,301,331,320,285,298,327,316,287,314 Find the mean deviation Solution# Step 1# find the simple mean = 271+354+296+302+333+326+285+298+327+316+ 287+314 ๐๐ = ๐๐๐๐ ๐๐ = ๐ฟ=Mean=309
- 11. X X- ๐ฟ Absolute values 271 271-309 38 354 354-309 45 296 296-309 13 301 301-309 8 333 333-309 24 326 326-309 17 285 285-309 24 298 298-309 11 327 327-309 18 316 316-309 7 287 287-309 22 314 314-309 5 Step#2 Find the absolute value from that mean โ(๐ โ ๐) = 232
- 12. Step#3 Find the mean deviation M.D = โ(๐โ ๐) ๐ = 232 12 M.D = 19.33 Co-efficient of mean deviation = ๐๐.๐๐ ๐๐๐ = 0.062
- 13. Merits of Mean Deviation ๏It is simple to understand ๏It is easy to calculate ๏It is based on all the observations of the series ๏It is not much effected by the value of exteme items of the series
- 14. ๏It facilitates comparison between different items of series ๏ It truly represents the average of deviation of the items of series ๏It has practical usefulness in the field of business and commerce
- 15. Demerits of Mean Deviation ๏It violates the algebraic principle by ignoring the positive and negative signs ๏It is not capable of further algebraic treatment ๏It is much effected by the fluctuations in sampling ๏It is difficult to calculate when the actual value of an average come out in fraction ๏It is not suitable for sociological study