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The mode is the value that occurs most frequently in a data set. A data set can have multiple modes if two or more values tie for most frequent. Mode is calculated by finding the most common value(s). Mode is easy to calculate but does not use all data points. Weighted mean assigns weights to values based on importance before calculating the average. The relationship between mean, median and mode indicates the symmetry or skew of a distribution.

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Data collection

This document discusses quantitative and qualitative data and methods of data collection. It covers key aspects of experimental design such as eliminating bias, controlling extraneous variables, and ensuring statistical precision. Descriptive statistics like the mean, median, and mode are introduced as ways to interpret quantitative findings from experiments and surveys. Sampling techniques are also discussed as a way to obtain representative data.

Data and Data Collection in Data Science.ppt

What is Data and methods of Data Collection in Data Science and Technology.

UNIT I -Data and Data Collection1.ppt

This document discusses quantitative research methods and design. It covers key elements of experimental design including eliminating bias, controlling extraneous variables, and ensuring statistical precision. It also discusses important quantitative measures like the independent and dependent variables, sampling techniques, descriptive statistics, and interpreting experimental results. Accuracy versus precision is explored along with definitions of mean, median, and mode as common statistical measures.

UNIT I -Data and Data Collection1.ppt

This document discusses various quantitative research methods and techniques for data collection and analysis. It covers topics such as experimental design, independent and dependent variables, sampling methods, descriptive statistics like mean, median and mode, and interpreting quantitative findings. Key aspects of a good research design discussed are freedom from bias, control of confounding variables, and statistical precision.

UNIT I -Data and Data Collection.ppt

The document discusses key concepts in quantitative data and research methods, including:
- The two main types of data are quantitative (numbers) and qualitative (words, images).
- Common data collection techniques include observations, tests, surveys, and document analysis.
- Key elements of a good quantitative research design include freedom from bias, control of confounding and extraneous variables, and statistical precision.
- Important statistical concepts discussed include independent and dependent variables, accuracy vs precision, interpreting experimental results, sampling methodology, and descriptive statistics like mean, median, and mode.

Data and Data Collection - Quantitative and Qualitative

This document discusses key concepts in quantitative data and research methods. It covers the differences between quantitative and qualitative data, common data collection techniques, experimental design principles like controlling for confounding variables and bias, and how to interpret quantitative findings through descriptive statistics like mean, median, and mode. The goal is to accurately acquire data to represent the population being studied and draw valid conclusions about causes and effects.

Measures of Central Tendency.ppt

A measure of central tendency (also referred to as measures of centre or central location) is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution.

Mean Mode Median.docx

The document discusses the three measures of central tendency in statistics - mean, median, and mode. It defines each measure and provides formulas to calculate them for both grouped and ungrouped data sets. The mean is the sum of all values divided by the number of values. The median is the middle value when values are arranged in order. The mode is the value that occurs most frequently. Formulas are given to find the mean, median, and mode of raw and frequency distribution data.

Data collection

This document discusses quantitative and qualitative data and methods of data collection. It covers key aspects of experimental design such as eliminating bias, controlling extraneous variables, and ensuring statistical precision. Descriptive statistics like the mean, median, and mode are introduced as ways to interpret quantitative findings from experiments and surveys. Sampling techniques are also discussed as a way to obtain representative data.

Data and Data Collection in Data Science.ppt

What is Data and methods of Data Collection in Data Science and Technology.

UNIT I -Data and Data Collection1.ppt

This document discusses quantitative research methods and design. It covers key elements of experimental design including eliminating bias, controlling extraneous variables, and ensuring statistical precision. It also discusses important quantitative measures like the independent and dependent variables, sampling techniques, descriptive statistics, and interpreting experimental results. Accuracy versus precision is explored along with definitions of mean, median, and mode as common statistical measures.

UNIT I -Data and Data Collection1.ppt

This document discusses various quantitative research methods and techniques for data collection and analysis. It covers topics such as experimental design, independent and dependent variables, sampling methods, descriptive statistics like mean, median and mode, and interpreting quantitative findings. Key aspects of a good research design discussed are freedom from bias, control of confounding variables, and statistical precision.

UNIT I -Data and Data Collection.ppt

The document discusses key concepts in quantitative data and research methods, including:
- The two main types of data are quantitative (numbers) and qualitative (words, images).
- Common data collection techniques include observations, tests, surveys, and document analysis.
- Key elements of a good quantitative research design include freedom from bias, control of confounding and extraneous variables, and statistical precision.
- Important statistical concepts discussed include independent and dependent variables, accuracy vs precision, interpreting experimental results, sampling methodology, and descriptive statistics like mean, median, and mode.

Data and Data Collection - Quantitative and Qualitative

This document discusses key concepts in quantitative data and research methods. It covers the differences between quantitative and qualitative data, common data collection techniques, experimental design principles like controlling for confounding variables and bias, and how to interpret quantitative findings through descriptive statistics like mean, median, and mode. The goal is to accurately acquire data to represent the population being studied and draw valid conclusions about causes and effects.

Measures of Central Tendency.ppt

A measure of central tendency (also referred to as measures of centre or central location) is a summary measure that attempts to describe a whole set of data with a single value that represents the middle or centre of its distribution.

Mean Mode Median.docx

The document discusses the three measures of central tendency in statistics - mean, median, and mode. It defines each measure and provides formulas to calculate them for both grouped and ungrouped data sets. The mean is the sum of all values divided by the number of values. The median is the middle value when values are arranged in order. The mode is the value that occurs most frequently. Formulas are given to find the mean, median, and mode of raw and frequency distribution data.

2.3

The document discusses various measures of central tendency including the mean, median, and mode. It provides definitions and formulas for calculating each measure, as well as examples of how to find the mean, median, and mode of data sets. It also discusses weighted means, finding the mean of grouped data, and the different shapes distributions can take, such as symmetric, skewed left, and skewed right.

3. Mean__Median__Mode__Range.ppt

This document contains a lesson on calculating and interpreting measures of central tendency (mean, median, mode) and spread (range) from data sets. It includes definitions of these statistical terms, examples of calculating them for various data sets, and discussions of how outliers impact the mean, median and mode. The key lesson is on identifying which measure of central tendency (mean, median or mode) best describes a particular data set and why.

stat.ppt

This document contains a lesson on calculating and interpreting measures of central tendency (mean, median, mode) and range from data sets. It includes definitions of these terms, examples of finding the mean, median, mode and range of various data sets, and discussions of how outliers impact the measures of central tendency. The lesson emphasizes that different measures may be best suited for different data distribution shapes and the presence of outliers.

Mean__Median__Mode__Range.ppt

This document contains a lesson on mean, median, mode, and range. It includes definitions of these statistical terms, examples of calculating them for different data sets, and discussions of how outliers can affect the values. The lesson emphasizes that the mean, median, and mode should be selected based on which measure best describes the distribution of the actual data.

Measures of central tendency

This document discusses measures of central tendency including mean, median, and mode. It provides definitions and formulas for calculating each measure from both grouped and ungrouped data. It compares the key differences between the measures such as their dependence on all data points, influence of outliers, and stability. It indicates that mode would be the most suitable measure to find the film viewed by most people.

Mean, Median, Mode and Range Central Tendency.pptx

This document provides definitions and examples for calculating measures of central tendency (mean, median, mode) and dispersion (range) from numeric data. It defines each concept - mean as the average, median as the middle value, mode as the most frequent value, and range as the difference between highest and lowest values. Formulas for calculating each are presented. Worked examples demonstrate calculating the mean, median, mode, and range for sample data sets. The purpose is to help students understand and apply these statistical concepts to analyze and interpret data in daily life.

Measures in Statistics. kjc.pptx

This document provides information about various measures of central tendency including the mean, median, and mode. It defines each measure and provides examples of how to calculate them using both raw data and grouped data. The mean is the average value and can be influenced by outliers. The median is the middle value and is not affected by outliers. The mode is the most frequent value. Each measure is suited for certain types of data distributions and the document discusses when each should be used.

Stattistic ii - mode, median, mean

This document discusses different measures of central tendency including mode, median, and mean. It provides examples and explanations of how to calculate each measure for a set of data. For mode, it explains that the mode is the most frequent value. For median, it describes that the median is the middle value when values are arranged in order. And for mean, it defines the mean as the sum of all values divided by the number of values.

Problems on Mean,Mode,Median Standard Deviation

Problems on Mean,Mode,Median Standard DeviationNigar Kadar Mujawar,Womens College of Pharmacy,Peth Vadgaon,Kolhapur,416112

This document contains examples of calculating mean, mode, median, and standard deviation from sets of data. It provides step-by-step explanations and solutions for finding these values from various data sets containing numbers. Examples include finding the mean, median, and mode of data sets; calculating standard deviation by determining the mean deviation from the mean, variance, and taking the square root of variance; and calculating the coefficient of standard deviation. The document serves as a guide for solving different statistical measurement problems.Chapter 4

The document discusses various measures of central tendency and variation used in statistics. It defines and provides examples of calculating the mean, median, mode, range, average deviation, variance and standard deviation. The mean is the sum of all values divided by the number of values and is useful when data is symmetric. The median is the middle value when values are arranged in order. The mode is the most frequent value. Range is the difference between highest and lowest values. Variance and standard deviation quantify how spread out values are from the mean.

Module 3 statistics

This module discusses measures of variability such as range and standard deviation. It provides examples of computing the range of various data sets as the difference between the highest and lowest values. Standard deviation is introduced as a more reliable measure that considers how far all values are from the mean. Students learn to calculate standard deviation by finding the deviation of each value from the mean, squaring the deviations, taking the average of the squared deviations, and extracting the square root. They practice computing and interpreting the range and standard deviation of sample data sets.

Measures of central tendency median mode

This document provides information on measures of central tendency, including the median, mode, and mean. It defines these terms, explains how to calculate them, and discusses their advantages and disadvantages. Specifically, it explains that the median is the middle value when values are arranged in order, and the mode is the most frequently occurring value. Formulas are provided for calculating the median and mode from both individual and grouped data sets. The document also discusses different types of averages and provides examples of calculating the median and mode from various data distributions.

Chapter 3 260110 044503

This document provides an overview of key concepts for describing and summarizing data, including measures of central tendency (mean, median, mode), measures of variation (range, variance, standard deviation), and concepts like skewness. It discusses how to calculate and interpret these measures for both grouped and ungrouped data sets. Examples are provided to demonstrate calculating these statistics for different types of data distributions.

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.

3. measures of central tendency

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.

Data Processing and Statistical Treatment.pptx

This document discusses various topics in data processing and statistical treatment. It begins by explaining how data is categorized, coded, and tabulated. It then discusses the importance of statistical treatment and describes descriptive and inferential problems. Specific statistical tests and analyses are defined, including parametric vs non-parametric tests, measures of central tendency, variability, correlation, t-tests, and methods for comparing means. Examples of outputs like frequency tables are provided.

Mod mean quartile

1. The document discusses various measures of central tendency including mode, median, and quartiles.
2. Mode is the most frequent value in a data set. Median divides the data set into two equal halves. Quartiles divide the data set into four equal groups.
3. The document provides formulas and examples for calculating mode, median, and quartiles for both grouped and ungrouped data sets. Advantages and disadvantages of each measure are also discussed.

Measures of Central tendency

This slideshow explains the important measures of central tendency in statistics. It deals with Mean, mode and median; its characteristics, its computation, merits and demerits. This slideshow will be useful to students, teachers and managers.

3 descritive statistics measure of central tendency variatio

This document provides an overview of descriptive statistics and properties of numerical data, including measures of central tendency (mean, median, mode), variation (range, variance, standard deviation), and shape (skewness, kurtosis). It explains how to calculate the 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 in order. The mode is the most frequent value. Extreme values affect the mean more than the median.

Mode

The mode is defined as the score that occurs most frequently in a data set. It is a measure of central tendency that indicates the most common value. For an ungrouped data set, the mode is simply the value that repeats most. For a grouped data set, the mode is calculated using a formula that finds the midpoint of the interval with the highest frequency while accounting for the differences in frequencies on either side of that interval. The mode is useful when wanting a quick approximation of central tendency or when trying to find the most typical value in a data set. Data sets can have one, two, or more modes depending on the number of most frequent values.

LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP

This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.

Hindi varnamala | hindi alphabet PPT.pdf

हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com

2.3

The document discusses various measures of central tendency including the mean, median, and mode. It provides definitions and formulas for calculating each measure, as well as examples of how to find the mean, median, and mode of data sets. It also discusses weighted means, finding the mean of grouped data, and the different shapes distributions can take, such as symmetric, skewed left, and skewed right.

3. Mean__Median__Mode__Range.ppt

This document contains a lesson on calculating and interpreting measures of central tendency (mean, median, mode) and spread (range) from data sets. It includes definitions of these statistical terms, examples of calculating them for various data sets, and discussions of how outliers impact the mean, median and mode. The key lesson is on identifying which measure of central tendency (mean, median or mode) best describes a particular data set and why.

stat.ppt

This document contains a lesson on calculating and interpreting measures of central tendency (mean, median, mode) and range from data sets. It includes definitions of these terms, examples of finding the mean, median, mode and range of various data sets, and discussions of how outliers impact the measures of central tendency. The lesson emphasizes that different measures may be best suited for different data distribution shapes and the presence of outliers.

Mean__Median__Mode__Range.ppt

This document contains a lesson on mean, median, mode, and range. It includes definitions of these statistical terms, examples of calculating them for different data sets, and discussions of how outliers can affect the values. The lesson emphasizes that the mean, median, and mode should be selected based on which measure best describes the distribution of the actual data.

Measures of central tendency

This document discusses measures of central tendency including mean, median, and mode. It provides definitions and formulas for calculating each measure from both grouped and ungrouped data. It compares the key differences between the measures such as their dependence on all data points, influence of outliers, and stability. It indicates that mode would be the most suitable measure to find the film viewed by most people.

Mean, Median, Mode and Range Central Tendency.pptx

This document provides definitions and examples for calculating measures of central tendency (mean, median, mode) and dispersion (range) from numeric data. It defines each concept - mean as the average, median as the middle value, mode as the most frequent value, and range as the difference between highest and lowest values. Formulas for calculating each are presented. Worked examples demonstrate calculating the mean, median, mode, and range for sample data sets. The purpose is to help students understand and apply these statistical concepts to analyze and interpret data in daily life.

Measures in Statistics. kjc.pptx

This document provides information about various measures of central tendency including the mean, median, and mode. It defines each measure and provides examples of how to calculate them using both raw data and grouped data. The mean is the average value and can be influenced by outliers. The median is the middle value and is not affected by outliers. The mode is the most frequent value. Each measure is suited for certain types of data distributions and the document discusses when each should be used.

Stattistic ii - mode, median, mean

This document discusses different measures of central tendency including mode, median, and mean. It provides examples and explanations of how to calculate each measure for a set of data. For mode, it explains that the mode is the most frequent value. For median, it describes that the median is the middle value when values are arranged in order. And for mean, it defines the mean as the sum of all values divided by the number of values.

Problems on Mean,Mode,Median Standard Deviation

Problems on Mean,Mode,Median Standard DeviationNigar Kadar Mujawar,Womens College of Pharmacy,Peth Vadgaon,Kolhapur,416112

This document contains examples of calculating mean, mode, median, and standard deviation from sets of data. It provides step-by-step explanations and solutions for finding these values from various data sets containing numbers. Examples include finding the mean, median, and mode of data sets; calculating standard deviation by determining the mean deviation from the mean, variance, and taking the square root of variance; and calculating the coefficient of standard deviation. The document serves as a guide for solving different statistical measurement problems.Chapter 4

The document discusses various measures of central tendency and variation used in statistics. It defines and provides examples of calculating the mean, median, mode, range, average deviation, variance and standard deviation. The mean is the sum of all values divided by the number of values and is useful when data is symmetric. The median is the middle value when values are arranged in order. The mode is the most frequent value. Range is the difference between highest and lowest values. Variance and standard deviation quantify how spread out values are from the mean.

Module 3 statistics

This module discusses measures of variability such as range and standard deviation. It provides examples of computing the range of various data sets as the difference between the highest and lowest values. Standard deviation is introduced as a more reliable measure that considers how far all values are from the mean. Students learn to calculate standard deviation by finding the deviation of each value from the mean, squaring the deviations, taking the average of the squared deviations, and extracting the square root. They practice computing and interpreting the range and standard deviation of sample data sets.

Measures of central tendency median mode

This document provides information on measures of central tendency, including the median, mode, and mean. It defines these terms, explains how to calculate them, and discusses their advantages and disadvantages. Specifically, it explains that the median is the middle value when values are arranged in order, and the mode is the most frequently occurring value. Formulas are provided for calculating the median and mode from both individual and grouped data sets. The document also discusses different types of averages and provides examples of calculating the median and mode from various data distributions.

Chapter 3 260110 044503

This document provides an overview of key concepts for describing and summarizing data, including measures of central tendency (mean, median, mode), measures of variation (range, variance, standard deviation), and concepts like skewness. It discusses how to calculate and interpret these measures for both grouped and ungrouped data sets. Examples are provided to demonstrate calculating these statistics for different types of data distributions.

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.

3. measures of central tendency

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.

Data Processing and Statistical Treatment.pptx

This document discusses various topics in data processing and statistical treatment. It begins by explaining how data is categorized, coded, and tabulated. It then discusses the importance of statistical treatment and describes descriptive and inferential problems. Specific statistical tests and analyses are defined, including parametric vs non-parametric tests, measures of central tendency, variability, correlation, t-tests, and methods for comparing means. Examples of outputs like frequency tables are provided.

Mod mean quartile

1. The document discusses various measures of central tendency including mode, median, and quartiles.
2. Mode is the most frequent value in a data set. Median divides the data set into two equal halves. Quartiles divide the data set into four equal groups.
3. The document provides formulas and examples for calculating mode, median, and quartiles for both grouped and ungrouped data sets. Advantages and disadvantages of each measure are also discussed.

Measures of Central tendency

This slideshow explains the important measures of central tendency in statistics. It deals with Mean, mode and median; its characteristics, its computation, merits and demerits. This slideshow will be useful to students, teachers and managers.

3 descritive statistics measure of central tendency variatio

This document provides an overview of descriptive statistics and properties of numerical data, including measures of central tendency (mean, median, mode), variation (range, variance, standard deviation), and shape (skewness, kurtosis). It explains how to calculate the 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 in order. The mode is the most frequent value. Extreme values affect the mean more than the median.

Mode

The mode is defined as the score that occurs most frequently in a data set. It is a measure of central tendency that indicates the most common value. For an ungrouped data set, the mode is simply the value that repeats most. For a grouped data set, the mode is calculated using a formula that finds the midpoint of the interval with the highest frequency while accounting for the differences in frequencies on either side of that interval. The mode is useful when wanting a quick approximation of central tendency or when trying to find the most typical value in a data set. Data sets can have one, two, or more modes depending on the number of most frequent values.

2.3

2.3

3. Mean__Median__Mode__Range.ppt

3. Mean__Median__Mode__Range.ppt

stat.ppt

stat.ppt

Mean__Median__Mode__Range.ppt

Mean__Median__Mode__Range.ppt

Measures of central tendency

Measures of central tendency

Mean, Median, Mode and Range Central Tendency.pptx

Mean, Median, Mode and Range Central Tendency.pptx

Measures in Statistics. kjc.pptx

Measures in Statistics. kjc.pptx

Stattistic ii - mode, median, mean

Stattistic ii - mode, median, mean

Problems on Mean,Mode,Median Standard Deviation

Problems on Mean,Mode,Median Standard Deviation

Chapter 4

Chapter 4

Module 3 statistics

Module 3 statistics

Measures of central tendency median mode

Measures of central tendency median mode

Chapter 3 260110 044503

Chapter 3 260110 044503

CABT Math 8 measures of central tendency and dispersion

CABT Math 8 measures of central tendency and dispersion

3. measures of central tendency

3. measures of central tendency

Data Processing and Statistical Treatment.pptx

Data Processing and Statistical Treatment.pptx

Mod mean quartile

Mod mean quartile

Measures of Central tendency

Measures of Central tendency

3 descritive statistics measure of central tendency variatio

3 descritive statistics measure of central tendency variatio

Mode

Mode

LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UP

This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.

Hindi varnamala | hindi alphabet PPT.pdf

हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com

Wound healing PPT

This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.

Chapter wise All Notes of First year Basic Civil Engineering.pptx

Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1

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(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
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𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
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Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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Event Link:- https://meetups.mulesoft.com/events/details/mulesoft-mysore-presents-mule-event-processing-models/
Agenda
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For Upcoming Meetups Join Mysore Meetup Group - https://meetups.mulesoft.com/mysore/YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
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Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
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- 1. Mode: The most repeated or most common or the most frequent Value which occurs in a set of values. The mode is the value in a data set that occur most frequently. A set of data can have more than one mode if two or more values tie for the most frequently occurring value.
- 2. Collect the sample data. a sample of 20 group was selected at random. 2,4,1,2,3,2,4,2,3,6,8,4,2,1,7,4,2,4,4,3. “most repeated value or values” Mode = 2,4 X Frequency 1 2 2 6 3 3 4 6 5 0 6 1 7 1 8 1
- 3. For group data Mode = l + (fm – f1) * h (fm- f1) + (fm –f2) here l = lower limit class boundary of the mode group fm= maximum frequency f1 = frequency preceding the fm f2 = frequency following the fm h = class interval
- 4. Marks F Class boundary 30 - 39 2 29.5 – 39.5 40 – 49 3 39.5 – 49.5 50 – 59 11 49.5 – 59.5 60 – 69 20 f1 59.5 – 69.5 70 – 79 32 fm l 69.5 – 79.5 mode group 80- 89 25 f2 79.5 – 89.5 90 - 99 7 90.5 – 99.5
- 5. Mode = l + (fm – f1) * h (fm- f1) + (fm –f2) = 69.5 + ( 32 – 20) * 10 (32 – 20) + (32 – 25) = 69.5 + 120 12+7 mode = 69.5 + 6.32 = 75.82
- 6. Advantages: 1. It is easy and quick to calculate. 2. It is easy to understand. 3. Extreme values do not effect its values. 4. It can be determined from open end distribution. 5. It can be found by inspection from ungroup data. 6. It can be used for qualitative data.
- 7. Disadvantage: 1. It is not well defined. 2. It is not based on all the observation of a set of data. 3. It can not be used for further mathematical processing. 4. There may be more then one value of the mode in the set of data. 5. There may be no mode, if there is no common value in the data.
- 8. Weighted Mean: weighted mean is a special case of arithmetic mean. The mean value of data values that have been weighted according to their relative importance. when the value are not equal importance, we assign them certain numerical values to express their relative importance. These numerical values are called weights. Weighted mean = WX / W
- 9. The marks obtained by a students in English, Urdu and Statistics were 70, 76 and 82 respectively. Find the suitable average if weights of 5, 4 and 3 are assigned to these subjects. Weighted mean = WX / W = 5*70+4*76+3*82 5+4+3 = 900/12 = 75
- 10. Empirical Relationship among Mean, Median and Mode. 1. If Mean = Median = Mode then distribution is symmetrical. 2. If Mean > Median > Mode then distribution is positively skewed. 3. If Mean < Median < Mode then distribution is negatively Skewed.