This document discusses various measures of central tendency and variation. It defines central tendency as indicating where the center of a distribution tends to be, and mentions that measures of central tendency answer whether scores are generally high or low. It then discusses specific measures of central tendency - the mean, median, and mode - and how to calculate each one for both ungrouped and grouped data. It also discusses other measures of variation like range and standard deviation, and how to compute standard deviation for ungrouped and grouped data.
Mean, Median, Mode: Measures of Central Tendency Jan Nah
There are three common measures of central tendency: mean, median, and mode. The mean is the average value found by dividing the sum of all values by the total number of values. The median is the middle value when values are arranged from lowest to highest. The mode is the value that occurs most frequently. Each measure provides a single number to represent the central or typical value in a data set.
This document discusses measures of central tendency, including the mean, median, and mode. It provides examples of calculating each measure using sample data sets. The mean is the average value calculated by summing all values and dividing by the number of data points. The median is the middle value when data is ordered from lowest to highest. The mode is the most frequently occurring value. Examples are given to demonstrate calculating the mean, median, and mode from sets of numeric data.
Determining measures of central tendency for grouped dataAlona Hall
This document discusses measures of central tendency (mean, median, mode) using grouped data from a sample of 40 students' heights. It provides an example to calculate each measure. The mean height is estimated as 151.25 cm using a frequency table and calculating the mid-point of each height range. The modal class is 155-159 cm as it has the highest frequency. A cumulative frequency table and ogive curve allow estimating the median height as 153.5 cm.
Here are the modes for the three examples:
1. The mode is 3. This value occurs most frequently among the number of errors committed by the typists.
2. The mode is 82. This value occurs most frequently among the number of fruits yielded by the mango trees.
3. The mode is 12 and 15. These values occur most frequently among the students' quiz scores.
This document defines and provides examples for calculating the mean, median, mode, and range of data sets. It explains that the mean is the average found by adding all values and dividing by the number of values. The median is the middle number when values are ordered from lowest to highest. The mode is the most frequently occurring value. The range is the difference between the highest and lowest values. Step-by-step instructions and examples are given for calculating each measure using various data sets.
This document defines and provides examples of mean, median, and mode - three common measures of central tendency. The mean is the average and is calculated by summing all values and dividing by the number of values. The median is the middle value when values are arranged in order. The mode is the most frequent value in the data set. Examples are provided to demonstrate calculating each measure for sample data sets.
The document discusses how to calculate standard deviation and variance for both ungrouped and grouped data. It provides step-by-step instructions for finding the mean, deviations from the mean, summing the squared deviations, and using these values to calculate standard deviation and variance through standard formulas. Standard deviation measures how spread out numbers are from the mean, while variance is the square of the standard deviation.
This document provides information about various statistical measures of central tendency including the median, mode, and quartiles. It defines each measure and provides examples of how to calculate them from both grouped and ungrouped data sets. Formulas are given for calculating the median, quartiles, deciles, and percentiles for grouped data. The mode is defined as the value that occurs most frequently in a data set, and a formula is provided for calculating it from grouped frequency distributions.
Mean, Median, Mode: Measures of Central Tendency Jan Nah
There are three common measures of central tendency: mean, median, and mode. The mean is the average value found by dividing the sum of all values by the total number of values. The median is the middle value when values are arranged from lowest to highest. The mode is the value that occurs most frequently. Each measure provides a single number to represent the central or typical value in a data set.
This document discusses measures of central tendency, including the mean, median, and mode. It provides examples of calculating each measure using sample data sets. The mean is the average value calculated by summing all values and dividing by the number of data points. The median is the middle value when data is ordered from lowest to highest. The mode is the most frequently occurring value. Examples are given to demonstrate calculating the mean, median, and mode from sets of numeric data.
Determining measures of central tendency for grouped dataAlona Hall
This document discusses measures of central tendency (mean, median, mode) using grouped data from a sample of 40 students' heights. It provides an example to calculate each measure. The mean height is estimated as 151.25 cm using a frequency table and calculating the mid-point of each height range. The modal class is 155-159 cm as it has the highest frequency. A cumulative frequency table and ogive curve allow estimating the median height as 153.5 cm.
Here are the modes for the three examples:
1. The mode is 3. This value occurs most frequently among the number of errors committed by the typists.
2. The mode is 82. This value occurs most frequently among the number of fruits yielded by the mango trees.
3. The mode is 12 and 15. These values occur most frequently among the students' quiz scores.
This document defines and provides examples for calculating the mean, median, mode, and range of data sets. It explains that the mean is the average found by adding all values and dividing by the number of values. The median is the middle number when values are ordered from lowest to highest. The mode is the most frequently occurring value. The range is the difference between the highest and lowest values. Step-by-step instructions and examples are given for calculating each measure using various data sets.
This document defines and provides examples of mean, median, and mode - three common measures of central tendency. The mean is the average and is calculated by summing all values and dividing by the number of values. The median is the middle value when values are arranged in order. The mode is the most frequent value in the data set. Examples are provided to demonstrate calculating each measure for sample data sets.
The document discusses how to calculate standard deviation and variance for both ungrouped and grouped data. It provides step-by-step instructions for finding the mean, deviations from the mean, summing the squared deviations, and using these values to calculate standard deviation and variance through standard formulas. Standard deviation measures how spread out numbers are from the mean, while variance is the square of the standard deviation.
This document provides information about various statistical measures of central tendency including the median, mode, and quartiles. It defines each measure and provides examples of how to calculate them from both grouped and ungrouped data sets. Formulas are given for calculating the median, quartiles, deciles, and percentiles for grouped data. The mode is defined as the value that occurs most frequently in a data set, and a formula is provided for calculating it from grouped frequency distributions.
The document defines and provides examples for calculating the mean, median, mode, and range of a data set. The mean is the average found by summing all values and dividing by the number of values. The median is the middle number after sorting values. The mode is the most frequently occurring value. The range is the difference between the highest and lowest values. An example data set at the end is used to demonstrate calculating all four measures.
Chapter 2: Frequency Distribution and GraphsMong Mara
This document discusses different types of graphs and charts that can be used to represent frequency distributions of data, including histograms, frequency polygons, ogives, bar charts, pie charts, and stem-and-leaf plots. It provides examples of how to construct each graph or chart using sample data sets and discusses key aspects of each type such as class intervals, relative frequencies, and ordering of data. Guidelines are given for determining the optimal number of classes and class widths for grouped data. Exercises at the end provide practice applying these techniques to additional data sets.
The document discusses the remainder theorem for polynomials. It defines the division algorithm for polynomials which divides a polynomial P(x) by (x-c) to get a unique quotient polynomial Q(x) and remainder R. The remainder theorem then states that the remainder R is equal to the value of P(c). The document proves the theorem and provides examples of using it to find the remainder when one polynomial is divided by another. It also provides exercises for students to find remainders using the theorem.
This document discusses frequency distributions and methods for graphically presenting frequency distribution data. It defines a frequency distribution as a tabulation or grouping of data into categories showing the number of observations in each group. The document outlines the parts of a frequency table as class limits, class size, class boundaries, and class marks. It then provides steps for constructing a frequency distribution table from a set of data. Finally, it discusses histograms and frequency polygons as methods for graphically presenting frequency distribution data, and provides examples of how to construct these graphs in Excel.
This document explains how to create and interpret stem-and-leaf plots, which organize numeric data into place values to facilitate analysis. It provides examples of creating stem-and-leaf plots from sets of test scores and Olympic speed skating times. Key aspects covered include using stems for tens places and leaves for ones, adding a title and key, and how stem-and-leaf plots can be used to find the median, mode, and minimum/maximum values from a data set.
Normal Distribution
Properties of Normal Distribution
Empirical rule of normal distribution
Normality limits
Standard normal distribution(z-score/ SND)
Properties of SND
Use of z/normal table
Solved examples
quantitative aptitude, maths
applicable to
Common Aptitude Test (CAT)
Bank Competitive Exam
UPSC Competitive Exams
SSC Competitive Exams
Defence Competitive Exams
L.I.C/ G. I.C Competitive Exams
Railway Competitive Exam
University Grants Commission (UGC)
Career Aptitude Test (IT Companies) and etc.
This document discusses frequency distribution, which is a tabular arrangement that shows the frequency of different variable values. It organizes disorganized data into classes or intervals and summarizes the distribution. A frequency distribution specifies the class intervals or boundaries, class marks or midpoints, and class frequencies or number of observations in each interval. It is used to rank and organize data. The document then provides an example problem of constructing a frequency distribution table for the weights of 50 boys using 8 equal class intervals, and calculating the class boundaries and midpoints.
This document discusses measures of central tendency, specifically how to calculate the mean of grouped data. It provides the formula for calculating the mean of grouped data and walks through an example of finding the mean test scores of students. The document demonstrates how to find the midpoint of each score group, multiply by the frequency, sum the results, and divide by the total frequency to determine the mean.
Detailed Lesson Plan on Measures of Variability of Grouped and Ungrouped DataJunila Tejada
The document provides a detailed lesson plan for teaching measures of variability of grouped and ungrouped data to 7th grade mathematics students. The objectives are for students to be able to identify and calculate measures of variability, apply the concepts to real-life contexts, and solve problems involving grouped and ungrouped data. The lesson plan outlines teacher and student activities including an introductory activity to review key concepts, a lesson on different measures of variability, and a group activity for students to practice calculating various measures of variability from tables of grouped and ungrouped data.
This document discusses frequency distributions and how to construct them from raw data. It provides examples of creating stem-and-leaf displays, frequency tables, relative frequency tables, and cumulative frequency tables from various data sets. Key concepts covered include class width, class boundaries, tallying data, and calculating relative frequencies and percentages. Overall, the document serves as a tutorial on how to organize and summarize data using various types of frequency distributions.
The document defines and provides examples to calculate mean, median, mode, and range for sets of numerical data. Mean is the average, calculated by summing the values and dividing by the count. Median is the middle number when values are ordered from lowest to highest. Mode is the number that occurs most frequently. Range is the difference between the highest and lowest values. Examples are provided to demonstrate calculating each measure using sample data sets.
This document discusses measures of central tendency including the mean, median, and mode. It defines each measure and provides examples of calculating them using individual, discrete, and continuous data series. The mean is the average value obtained by dividing the sum of all values by the total number of values. The median is the middle value when data is arranged in order. The mode is the value that occurs most frequently. Formulas and examples are given for calculating each measure using different types of data sets. Merits and demerits of each measure are also outlined.
Topic: Frequency Distribution
Student Name: Abdul Hafeez
Class: B.Ed. (Hons) Elementary
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
This document defines and provides examples for calculating the mean, median, mode, and range of a data set. It explains that the mean is calculated by adding all values and dividing by the number of values, the median involves ordering values and selecting the middle one, the mode is the most frequent value, and the range is the difference between the highest and lowest values. Examples are given for each statistical measure.
This document defines and provides examples of measures of central tendency, which are methods used to determine how scores are distributed around the average score. It discusses the normal distribution and how measures like mean, median, and mode are used. Specifically, it shows how to calculate the mean by adding all scores and dividing by the total number. To find the median, the scores are ordered from lowest to highest and the middle number is identified. The mode is the number that occurs most frequently. Examples are provided to demonstrate calculating the mean, median, and mode of sample test score data sets.
Analysis and interpretation of Assessment.pptxAeonneFlux
The document provides information on statistics, frequency distributions, measures of central tendency (mean, median, mode), and how to calculate and interpret them. It defines statistics, descriptive and inferential statistics, and frequency distributions. It outlines the steps to construct a frequency distribution and calculate the mean, median, and mode for both ungrouped and grouped data. Examples are provided to demonstrate calculating each measure of central tendency.
The document discusses various measures of central tendency including the mean, median, and mode. It provides definitions and formulas for calculating each measure. The mean is the average and is calculated by adding all values and dividing by the total number of values. The median is the middle value when values are arranged from lowest to highest. The mode is the value that occurs most frequently in the data set. Examples are provided to demonstrate calculating each measure using both raw and grouped data.
The document defines and provides examples for calculating the mean, median, mode, and range of a data set. The mean is the average found by summing all values and dividing by the number of values. The median is the middle number after sorting values. The mode is the most frequently occurring value. The range is the difference between the highest and lowest values. An example data set at the end is used to demonstrate calculating all four measures.
Chapter 2: Frequency Distribution and GraphsMong Mara
This document discusses different types of graphs and charts that can be used to represent frequency distributions of data, including histograms, frequency polygons, ogives, bar charts, pie charts, and stem-and-leaf plots. It provides examples of how to construct each graph or chart using sample data sets and discusses key aspects of each type such as class intervals, relative frequencies, and ordering of data. Guidelines are given for determining the optimal number of classes and class widths for grouped data. Exercises at the end provide practice applying these techniques to additional data sets.
The document discusses the remainder theorem for polynomials. It defines the division algorithm for polynomials which divides a polynomial P(x) by (x-c) to get a unique quotient polynomial Q(x) and remainder R. The remainder theorem then states that the remainder R is equal to the value of P(c). The document proves the theorem and provides examples of using it to find the remainder when one polynomial is divided by another. It also provides exercises for students to find remainders using the theorem.
This document discusses frequency distributions and methods for graphically presenting frequency distribution data. It defines a frequency distribution as a tabulation or grouping of data into categories showing the number of observations in each group. The document outlines the parts of a frequency table as class limits, class size, class boundaries, and class marks. It then provides steps for constructing a frequency distribution table from a set of data. Finally, it discusses histograms and frequency polygons as methods for graphically presenting frequency distribution data, and provides examples of how to construct these graphs in Excel.
This document explains how to create and interpret stem-and-leaf plots, which organize numeric data into place values to facilitate analysis. It provides examples of creating stem-and-leaf plots from sets of test scores and Olympic speed skating times. Key aspects covered include using stems for tens places and leaves for ones, adding a title and key, and how stem-and-leaf plots can be used to find the median, mode, and minimum/maximum values from a data set.
Normal Distribution
Properties of Normal Distribution
Empirical rule of normal distribution
Normality limits
Standard normal distribution(z-score/ SND)
Properties of SND
Use of z/normal table
Solved examples
quantitative aptitude, maths
applicable to
Common Aptitude Test (CAT)
Bank Competitive Exam
UPSC Competitive Exams
SSC Competitive Exams
Defence Competitive Exams
L.I.C/ G. I.C Competitive Exams
Railway Competitive Exam
University Grants Commission (UGC)
Career Aptitude Test (IT Companies) and etc.
This document discusses frequency distribution, which is a tabular arrangement that shows the frequency of different variable values. It organizes disorganized data into classes or intervals and summarizes the distribution. A frequency distribution specifies the class intervals or boundaries, class marks or midpoints, and class frequencies or number of observations in each interval. It is used to rank and organize data. The document then provides an example problem of constructing a frequency distribution table for the weights of 50 boys using 8 equal class intervals, and calculating the class boundaries and midpoints.
This document discusses measures of central tendency, specifically how to calculate the mean of grouped data. It provides the formula for calculating the mean of grouped data and walks through an example of finding the mean test scores of students. The document demonstrates how to find the midpoint of each score group, multiply by the frequency, sum the results, and divide by the total frequency to determine the mean.
Detailed Lesson Plan on Measures of Variability of Grouped and Ungrouped DataJunila Tejada
The document provides a detailed lesson plan for teaching measures of variability of grouped and ungrouped data to 7th grade mathematics students. The objectives are for students to be able to identify and calculate measures of variability, apply the concepts to real-life contexts, and solve problems involving grouped and ungrouped data. The lesson plan outlines teacher and student activities including an introductory activity to review key concepts, a lesson on different measures of variability, and a group activity for students to practice calculating various measures of variability from tables of grouped and ungrouped data.
This document discusses frequency distributions and how to construct them from raw data. It provides examples of creating stem-and-leaf displays, frequency tables, relative frequency tables, and cumulative frequency tables from various data sets. Key concepts covered include class width, class boundaries, tallying data, and calculating relative frequencies and percentages. Overall, the document serves as a tutorial on how to organize and summarize data using various types of frequency distributions.
The document defines and provides examples to calculate mean, median, mode, and range for sets of numerical data. Mean is the average, calculated by summing the values and dividing by the count. Median is the middle number when values are ordered from lowest to highest. Mode is the number that occurs most frequently. Range is the difference between the highest and lowest values. Examples are provided to demonstrate calculating each measure using sample data sets.
This document discusses measures of central tendency including the mean, median, and mode. It defines each measure and provides examples of calculating them using individual, discrete, and continuous data series. The mean is the average value obtained by dividing the sum of all values by the total number of values. The median is the middle value when data is arranged in order. The mode is the value that occurs most frequently. Formulas and examples are given for calculating each measure using different types of data sets. Merits and demerits of each measure are also outlined.
Topic: Frequency Distribution
Student Name: Abdul Hafeez
Class: B.Ed. (Hons) Elementary
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
This document defines and provides examples for calculating the mean, median, mode, and range of a data set. It explains that the mean is calculated by adding all values and dividing by the number of values, the median involves ordering values and selecting the middle one, the mode is the most frequent value, and the range is the difference between the highest and lowest values. Examples are given for each statistical measure.
This document defines and provides examples of measures of central tendency, which are methods used to determine how scores are distributed around the average score. It discusses the normal distribution and how measures like mean, median, and mode are used. Specifically, it shows how to calculate the mean by adding all scores and dividing by the total number. To find the median, the scores are ordered from lowest to highest and the middle number is identified. The mode is the number that occurs most frequently. Examples are provided to demonstrate calculating the mean, median, and mode of sample test score data sets.
Analysis and interpretation of Assessment.pptxAeonneFlux
The document provides information on statistics, frequency distributions, measures of central tendency (mean, median, mode), and how to calculate and interpret them. It defines statistics, descriptive and inferential statistics, and frequency distributions. It outlines the steps to construct a frequency distribution and calculate the mean, median, and mode for both ungrouped and grouped data. Examples are provided to demonstrate calculating each measure of central tendency.
The document discusses various measures of central tendency including the mean, median, and mode. It provides definitions and formulas for calculating each measure. The mean is the average and is calculated by adding all values and dividing by the total number of values. The median is the middle value when values are arranged from lowest to highest. The mode is the value that occurs most frequently in the data set. Examples are provided to demonstrate calculating each measure using both raw and grouped data.
This document discusses various measures of central tendency and variability. It provides details on calculating the median, mean, and mode from both raw data and grouped data. The median is the middle value of a data set and is not affected by outliers. The mean is the average and is more affected by outliers. The mode is the most frequent value. Formulas and step-by-step processes are provided to compute each measure from ungrouped and grouped data using methods like class intervals and frequency distributions.
- The mean, median, and mode are measures of central tendency that provide a single value to represent the central or typical value in a data set.
- The mean is the average and is calculated by adding all values and dividing by the number of values. The median is the middle value when values are arranged from lowest to highest. The mode is the value that occurs most frequently.
- These measures can be used to describe and analyze data sets to understand the central performance or typical value compared to other values in the data.
The document discusses various methods for organizing and analyzing test score data, including:
1) Organizing scores in ascending or descending order. Ranking scores from highest to lowest.
2) Creating a stem-and-leaf plot to separate scores into "stems" and "leaves".
3) Calculating measures of central tendency (mean, median, mode) and using frequency distributions to analyze grouped score data.
There are three common measures of central tendency: mean, median, and mode. The mean is the average value found by dividing the sum of all values by the total number of values. The median is the middle value when values are arranged from lowest to highest. The mode is the value that occurs most frequently. Each measure provides a single number to represent the central or typical value in a data set.
The median is the middlemost score when values are arranged from lowest to highest. It divides the data set into two equal groups, with scores above and below the median. The median is not affected by extreme values and can be used when the mean would be skewed. To find the median of ungrouped data, arrange values from highest to lowest and take the middle value. For grouped data, use the formula Median = Ll + cfb/f, where Ll is the lower limit of the class containing N/2, cfb is the cumulative frequency below the assumed median, and f is the corresponding frequency.
This document discusses analytical representation of data through descriptive statistics. It begins by showing raw, unorganized data on movie genre ratings. It then demonstrates organizing this data into a frequency distribution table and bar graph to better analyze and describe the data. It also calculates averages for each movie genre. The document then discusses additional descriptive statistics measures like the mean, median, mode, and percentiles to further analyze data through measures of central tendency and dispersion.
This document discusses three common measures of central tendency: mean, median, and mode. It provides definitions and formulas for calculating each, along with examples. The mean is the average and is calculated by adding all values and dividing by the total number. The median is the middle value when values are arranged from lowest to highest. The mode is the value that occurs most frequently. Each measure is used in different situations depending on the type of data and what aspect of central tendency is most relevant.
There are three commonly used measures of central tendency: mean, median, and mode. The mean is the average and is calculated by adding all values and dividing by the total number. The median is the middle value when values are arranged from lowest to highest. The mode is the value that occurs most frequently. Each measure provides a single number to describe the central or typical performance of a group.
This document defines and provides examples of frequency distributions and measures of central tendency. It discusses array, frequency distribution, class intervals, class boundaries, class marks, relative frequency distributions, and cumulative frequency distributions. It also covers calculating the mean, median, and mode of both ungrouped and grouped data. Formulas are provided for determining the mean, median, and mode of grouped data using class marks, frequencies, and boundaries.
This document discusses analyzing and interpreting test data using measures of central tendency, variability, position, and co-variability. It defines these statistical measures and how to calculate and apply them. Key points covered include calculating the mean, median, and mode of a data set; measures of variability like standard deviation; and how these measures can be used to analyze test scores and interpret results to improve teaching. Formulas and examples are provided to demonstrate calculating and applying these statistical concepts to educational test data.
This document discusses descriptive statistics concepts including measures of center (mean, median, mode), measures of variation (range, standard deviation, variance), and properties of distributions (symmetric, skewed). Frequency tables are presented as a method to summarize data, including guidelines for construction and different types (relative frequency and cumulative frequency). Common notation and formulas are provided.
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.
This presentation includes the following subtopics
• Norm- Referenced and Criterion Referenced Assessment
• Measures of Central Tendency
• Measures of Location/Point Measures
• Measures of Variability
• Standard Scores
• Skewness and Kurtosis
• Correlation
The document discusses statistical concepts used in analyzing assessment data. It defines statistics as the science of collecting, organizing, summarizing, and interpreting data. Descriptive statistics are used to describe data through measures of central tendency like the mean, median, and mode, while inferential statistics make predictions about a larger data set based on a sample. The document outlines steps for constructing frequency distributions and calculating the mean, including determining class limits and sizes. Graphs like histograms and frequency polygons can be used to visually represent grouped assessment data.
The document discusses different measures of central tendency including the mean, median, and mode. It provides formulas and examples for calculating each measure. The mean is the average value and is calculated by summing all values and dividing by the total number. The median is the middle value when values are arranged in order. The mode is the value that appears most frequently in a data set.
There are three main measures of central tendency: mean, median, and mode. The mean is the average value and is calculated by adding all values and dividing by the total number. The median is the middle value when values are arranged from lowest to highest. The mode is the value that occurs most frequently. Each measure provides a single number to represent the central or typical value in a data set.
Frequency Distribution (Class-interval- Tally).pptxAlwinCAsuncion
The document defines various measures of central tendency including mean, median, and mode for both ungrouped and grouped data. It also defines key terms related to frequency distributions such as lower class limit, upper class limit, class boundaries, class marks, class width, and cumulative frequency. An example is provided to illustrate the construction of a grouped frequency distribution table involving 7 classes with a class width of 7 using data on exam scores of 40 students.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
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.
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
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.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
2.
Measures of Central
Tendency and Variation
Central tendency: indicates where the center of
the distribution tends to be
(88% grade)
(compact, disperse)
Measures of central tendency answers whether
the scores are generally high or general low.
4.
Use of Central Tendency
Simplification: one number
Prediction: Predict other scores
Chairs, Classrooms, Books, School needs.
Determining which off central
tendency to use depends on:
Scale of measurement
Nominal, Ordinal, interval,
Ratio
(NOIR)
Shape of Distribution
Skew
Kurtosis
Average Children: 2.3
7.
Median is what divides the scores in
the distribution into two equal parts.
Fifty percent (50%) lies below the
median value and 50% lies above the
median value.
It is also known as the middle score or
the 50th percentile.
MEDIAN
8.
The median is a single value from the data
set that measures the central item in the data.
This single value is the middlemost or most
central item in the set of scores. Half of the
scores lie above this point and other half lie
below it.
MEDIAN
9.
MEDIAN OF UNGROUPED DATA
1. Arrange the scores(from lowest to highest or highest to
lowest).
2. If the data has odd numbered items, the median is middle
item of the array. However, if it has even number of items,
the median is the average of the two middle items.
MEDIAN
12.
MEDIAN OF GROUPED DATA
FORMULA:
𝑥 = 𝐿B +
𝑛
2
− 𝑐𝑓<
𝑓𝑚
× c.i
MEDIAN
𝒙 = median value
MC = median class is a category
containing
𝑛
2
LB = lower boundary of the
median class (MC)
cf< = cumulative frequency
before the median class if
the scores are arranged from
lowest to highest value
Fm = frequency of the median
class
c.i = size of the class interval
n= number of scores
13.
1. Complete the table for <cf.
2. Get
𝑛
2
of the scores in the distribution
so that you can identify MC.
3. Determine LB, cfp, fm and c.i.
4. Solve the median using the formula.
Steps in Solving Median
for Grouped Data
14. X F <cf
10-14 5 5
15-19 2 7
20-24 3 10
25-29 5 15
30-34 2 17
35-39 9 26
40-44 6 32
45-49 3 35
50-54 5 40
n=40
Example:
Scores of 40 students in a science class consist of 60 items
and they are tabulated below. The highest score is 54
and the lowest score is 10.
17.
Mode or Modal Score
is the measure of central tendency
that identifies the category or score
that occurs the most frequently
within the distribution of data
18.
Classification of Mode
Unimodal- is a distribution of scores
that consist of only one mode.
Bimodal- is a distribution of scores
that consists of two modes.
Trimodal- is a distribution of scores
that consist of three modes or
multimodal a distribution of scores
that consists of more than two modes.
19.
Example: Score of 10
students in Section
A,B and C (
Ungrouped Data)
A B C
25 25 25
24 24 25
24 24 25
20 20 22
20 18 21
20 18 21
16 17 21
12 10 18
10 9 18
7 7 18
20.
Sample (Grouped Data)
x f
10-14 5
15-19 2
20-24 3
25-29 5
30-34 2
35-39 9
40-44 6
45-49 3
50-54 5
N=40
Scores of 40 students in a students in a science
class consist of 60 items and they are tabulated
below:
21.
Terms
Lb= Lower boundary of the modal
class
Modal Class (MC)= is a category
containing the highest frequency
D1= difference between the frequency
of the modal class and the frequency
above it, when the scores are arranged
from lowest to highest.
22.
D2= difference between the frequency
of the modal class and the frequency
below it, when the scores are arranged
from lowest to highest.
C.i size of class interval
= LB +
d1
d1 + d2
𝑥 𝐶. 𝑖X̂
23. Range
It is the difference between the
lowest and highest values.
Example: In 4,6,9,3,7 the lowest
value is 3, and the highest is 9.
Solution:
9 - 3 = 6
24.
Standard deviation
It is a number used to tell how measurements
for a group are spread out from the average
(mean) or expected value.
Low standard deviation means that most of
the numbers are close to the average.
High standard deviation means that the
numbers are more spread out .
25.
Standard deviation for
ungrouped data
SD= Standard deviation
∑= sum of
X= each value in the data set
X= mean of all values in the data set
n= number of value in the data set
26.
How to Calculate the
Standard Deviation for
Ungrouped Data
1.Find the Mean.
2.Calculate the difference between each score
and the mean.
3.Square the difference between each score and
the mean.
27.
How to Calculate the
Standard Deviation for
Ungrouped Data
4.Add up all the squares of the difference
between each score and the mean.
5.Divide the obtained sum by n – 1.
6.Extract the positive square root of the
obtained quotient.
31.
How to Calculate the
Standard Deviation for
Grouped Data
1.Calculate the mean.
2.Get the deviations by finding the difference of
each midpoint from the mean.
3.Square the deviations and find its summation.
4.Substitute in the formula.
33.
How to get class
intervals
Class Limits/ Interval
Range= Highest – Lowest
= 127- 72
= 55
34.
How to get number of
class
Number of Class= ( Range ÷ Class Size) +
1
= (55 ÷ 5 )
+ 1
= 12
Class Limits/ Interval
125- 129
120- 124
115- 119
110-114
105- 109
100- 104
95- 99
90- 94
85-89
80-84
75- 79
70- 74
44.
Variance
is the square of the standard deviation.
It is a measure of how dispersed or spread out the
set is, something that the “average” (mean or
median) is not designed to do.
46.
How to Calculate the
Variance for Ungrouped
Data
1.Find the Mean.
2.Calculate the difference between each score and the
mean.
3.Square the difference between each score and the
mean.
4.Add up all the squares of the difference between
each score and the mean.
5.Divide the obtained sum by n – 1
51.
How to Calculate the
Variance for Grouped Data
1.Calculate the mean.
2. Get the deviations by finding the
difference of each midpoint from the
mean.
3.Square the deviations and find its
summation.
4.Substitute in the formula.