Cumulative Frequency Table,Cumulative Frequency Table with Example,Ogive Curve,Two Types of Ogive Curve,Less than Ogive with Example,Greater than Ogive with Example.
Topic: Population And Sample
Student Name: Sidera Saleem
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
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
Topic: Frequency Polygon
Student Name: Kubra
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Variables describe attributes that can vary between entities. They can be qualitative (categorical) or quantitative (numeric). Common types of variables include continuous, discrete, ordinal, and nominal variables. Data can be presented graphically through bar charts, pie charts, histograms, box plots, and scatter plots to better understand patterns and trends. Key measures used to summarize data include measures of central tendency (mean, median, mode) and measures of variability (range, standard deviation, interquartile range).
This presentation covered the following topics:
1. Definition of Correlation and Regression
2. Meaning of Correlation and Regression
3. Types of Correlation and Regression
4. Karl Pearson's methods of correlation
5. Bivariate Grouped data method
6. Spearman's Rank correlation Method
7. Scattered diagram method
8. Interpretation of correlation coefficient
9. Lines of Regression
10. regression Equations
11. Difference between correlation and regression
12. Related examples
This document discusses frequency polygons, which are a type of graphical representation of data that involves joining midpoints of histogram rectangles or data points with straight lines to form a polygon. Key details include: frequency polygons can be constructed from histograms or without histograms; they show the distribution of data by having point heights represent frequencies; and involve terms like class intervals, frequencies, midpoints, and class marks. Steps for drawing frequency polygons include constructing a histogram, marking midpoints, and joining points with straight lines. Advantages are that they help represent and compare data distributions clearly and simply. A disadvantage is they are less accurate than histograms since each class is represented by a single point.
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. For mean, it is the sum of all values divided by the number of values. Median is the middle value when values are arranged in order. Mode is the most frequently occurring value. The document also discusses requirements for a good measure of central tendency and provides examples calculating the measures from sample medical data sets to illustrate their use and assess skewness.
Cumulative Frequency Table,Cumulative Frequency Table with Example,Ogive Curve,Two Types of Ogive Curve,Less than Ogive with Example,Greater than Ogive with Example.
Topic: Population And Sample
Student Name: Sidera Saleem
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
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
Topic: Frequency Polygon
Student Name: Kubra
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Variables describe attributes that can vary between entities. They can be qualitative (categorical) or quantitative (numeric). Common types of variables include continuous, discrete, ordinal, and nominal variables. Data can be presented graphically through bar charts, pie charts, histograms, box plots, and scatter plots to better understand patterns and trends. Key measures used to summarize data include measures of central tendency (mean, median, mode) and measures of variability (range, standard deviation, interquartile range).
This presentation covered the following topics:
1. Definition of Correlation and Regression
2. Meaning of Correlation and Regression
3. Types of Correlation and Regression
4. Karl Pearson's methods of correlation
5. Bivariate Grouped data method
6. Spearman's Rank correlation Method
7. Scattered diagram method
8. Interpretation of correlation coefficient
9. Lines of Regression
10. regression Equations
11. Difference between correlation and regression
12. Related examples
This document discusses frequency polygons, which are a type of graphical representation of data that involves joining midpoints of histogram rectangles or data points with straight lines to form a polygon. Key details include: frequency polygons can be constructed from histograms or without histograms; they show the distribution of data by having point heights represent frequencies; and involve terms like class intervals, frequencies, midpoints, and class marks. Steps for drawing frequency polygons include constructing a histogram, marking midpoints, and joining points with straight lines. Advantages are that they help represent and compare data distributions clearly and simply. A disadvantage is they are less accurate than histograms since each class is represented by a single point.
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. For mean, it is the sum of all values divided by the number of values. Median is the middle value when values are arranged in order. Mode is the most frequently occurring value. The document also discusses requirements for a good measure of central tendency and provides examples calculating the measures from sample medical data sets to illustrate their use and assess skewness.
Descriptive statistics, central tendency, measures of variability, measures of dispersion, skewness, kurtosis, range, standard deviation, mean, median, mode, variance, normal distribution
The document discusses the normal distribution, which produces a symmetrical bell-shaped curve. It has two key parameters - the mean and standard deviation. According to the empirical rule, about 68% of values in a normal distribution fall within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three standard deviations. The normal distribution is commonly used to model naturally occurring phenomena that tend to cluster around an average value, such as heights or test scores.
The document discusses the normal distribution and some of its key properties. It also discusses the central limit theorem and how the distribution of sample means approaches a normal distribution as the sample size increases. Additionally, it covers how to transform a normally distributed variable into a standard normal variable using z-scores and how the normal distribution can be used to approximate the binomial distribution through a correction for continuity.
The document provides an overview of inferential statistics. It defines inferential statistics as making generalizations about a larger population based on a sample. Key topics covered include hypothesis testing, types of hypotheses, significance tests, critical values, p-values, confidence intervals, z-tests, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document aims to explain these statistical concepts and techniques at a high level.
This document discusses population and sampling concepts for research. It defines a population as the complete set of people or objects with a common characteristic of interest. The target population is the entire group the researcher wishes to generalize to, while the accessible population includes cases that meet criteria and are available. A sample is a representative subset of the target population selected using sampling principles like random selection and large sample sizes to make inferences about the population. The key difference between a population and sample is that a population includes all elements while a sample is a subset used to study characteristics of the larger population.
Cumulative frequency distribution is a table that shows the cumulative totals of a frequency distribution. It is created by adding the frequency of each class to the total of the classes below it. This allows you to see the total frequency up to a certain threshold. There are two types: less than, which cumulates from lowest to highest class, and more than, which cumulates from highest to lowest. You can represent cumulative frequencies graphically using a polygon or ogive curve.
diagrammatic and graphical representation of dataVarun Prem Varu
This document discusses various types of diagrams and graphs that can be used to summarize statistical data. It describes one-dimensional, two-dimensional, and three-dimensional diagrams, as well as pictograms, cartograms, histograms, frequency polygons, frequency curves, ogives, and Lorenz curves. The key points are that diagrams and graphs make data simple and allow for easy comparison, while saving time over presenting raw numbers or text. Different types of diagrams and graphs are suited for different types of data and purposes. Guidelines are provided for properly constructing different diagrams.
The document discusses properties of normal distributions and the standard normal distribution. It provides examples of finding probabilities and values associated with normal distributions. The key points are:
- Normal distributions are continuous and bell-shaped. The mean, median and mode are equal.
- The standard normal distribution has a mean of 0 and standard deviation of 1.
- Probabilities under the normal curve can be found using z-scores and the standard normal table.
- Values like z-scores can be determined by finding the corresponding cumulative area in the standard normal table.
This document discusses measures of central tendency including the mean, median, and mode. It provides examples and definitions for each measure. 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 values are ranked in order. The mode is the most frequent value. The best measure depends on the scale of measurement and shape of the distribution, such as whether it is symmetrical or skewed.
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
This document discusses frequency distributions, which summarize data by arranging it into classes and indicating how many observations fall into each class. It describes the key components of a frequency distribution, including the class, class limits, class mark, class interval, class frequency and tally marks. It provides examples of grouped frequency distributions, where the data is organized into groups, and ungrouped distributions, where the raw individual data points are listed. The objectives of a frequency distribution are to estimate population frequencies, facilitate data analysis, and allow computation of statistical measures.
Contingency tables, or crosstabs, summarize the relationship between categorical variables. They display counts of observations cross-classified by discrete predictors and response variables. Contingency tables are used to assess if factors are related, describe data frequencies and proportions, and test relationships between factors using chi-square tests. They show counts in each cell, and row, column, and total percentages to understand associations between independent variables like exposures, dependent outcome variables, and potential confounders.
This document discusses descriptive statistics and measures of central tendency. It defines raw data and descriptive measures. It describes organizing data through ordered arrays and grouped data using frequency distributions. Methods for determining the number of class intervals and class width are provided. Common measures of central tendency - the mean, median and mode - are defined. The mean is the sum of all values divided by the total number. The median is the middle value of ordered data. Examples are given to demonstrate calculating these statistics.
1. The document discusses descriptive statistics, which is the study of how to collect, organize, analyze, and interpret numerical data.
2. Descriptive statistics can be used to describe data through measures of central tendency like the mean, median, and mode as well as measures of variability like the range.
3. These statistical techniques help summarize and communicate patterns in data in a concise manner.
Measures of dispersion
Absolute measure, relative measures
Range of Coe. of Range
Mean deviation and coe. of mean deviation
Quartile deviation IQR, coefficient of QD
Standard deviation and coefficient of variation
This document discusses various statistical methods used to organize and interpret data. It describes descriptive statistics, which summarize and simplify data through measures of central tendency like mean, median, and mode, and measures of variability like range and standard deviation. Frequency distributions are presented through tables, graphs, and other visual displays to organize raw data into meaningful categories.
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.
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
Bar Diagram (chart) in Statistics presentationsheiblu
This document discusses bar diagrams and their components. It defines a bar diagram as a chart that uses rectangular bars to present qualitative data, with the bar lengths proportional to the values. It notes that qualitative data deals with descriptions that can be observed but not measured, such as colors, textures, smells, tastes, and appearances. The key components of a bar diagram are collecting qualitative data, drawing and labeling the x- and y- axes, and drawing the bars. An example bar diagram and table show the numbers of children who favorite different cartoons. Finally, it lists different types of bar diagrams like horizontal, grouped, and stacked bar charts.
An ogive, or cumulative histogram, is a graph used to determine how many data values lie above or below a particular value. It is constructed by plotting the cumulative frequencies on the y-axis against the class limits on the x-axis. Less than ogives start from the upper class limit and add frequencies to the cumulative total, while more than ogives start from the total frequency and subtract frequencies from the cumulative total. Ogives provide a visual summary of a data set and show the proportion of data points above or below values.
Descriptive statistics, central tendency, measures of variability, measures of dispersion, skewness, kurtosis, range, standard deviation, mean, median, mode, variance, normal distribution
The document discusses the normal distribution, which produces a symmetrical bell-shaped curve. It has two key parameters - the mean and standard deviation. According to the empirical rule, about 68% of values in a normal distribution fall within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three standard deviations. The normal distribution is commonly used to model naturally occurring phenomena that tend to cluster around an average value, such as heights or test scores.
The document discusses the normal distribution and some of its key properties. It also discusses the central limit theorem and how the distribution of sample means approaches a normal distribution as the sample size increases. Additionally, it covers how to transform a normally distributed variable into a standard normal variable using z-scores and how the normal distribution can be used to approximate the binomial distribution through a correction for continuity.
The document provides an overview of inferential statistics. It defines inferential statistics as making generalizations about a larger population based on a sample. Key topics covered include hypothesis testing, types of hypotheses, significance tests, critical values, p-values, confidence intervals, z-tests, t-tests, ANOVA, chi-square tests, correlation, and linear regression. The document aims to explain these statistical concepts and techniques at a high level.
This document discusses population and sampling concepts for research. It defines a population as the complete set of people or objects with a common characteristic of interest. The target population is the entire group the researcher wishes to generalize to, while the accessible population includes cases that meet criteria and are available. A sample is a representative subset of the target population selected using sampling principles like random selection and large sample sizes to make inferences about the population. The key difference between a population and sample is that a population includes all elements while a sample is a subset used to study characteristics of the larger population.
Cumulative frequency distribution is a table that shows the cumulative totals of a frequency distribution. It is created by adding the frequency of each class to the total of the classes below it. This allows you to see the total frequency up to a certain threshold. There are two types: less than, which cumulates from lowest to highest class, and more than, which cumulates from highest to lowest. You can represent cumulative frequencies graphically using a polygon or ogive curve.
diagrammatic and graphical representation of dataVarun Prem Varu
This document discusses various types of diagrams and graphs that can be used to summarize statistical data. It describes one-dimensional, two-dimensional, and three-dimensional diagrams, as well as pictograms, cartograms, histograms, frequency polygons, frequency curves, ogives, and Lorenz curves. The key points are that diagrams and graphs make data simple and allow for easy comparison, while saving time over presenting raw numbers or text. Different types of diagrams and graphs are suited for different types of data and purposes. Guidelines are provided for properly constructing different diagrams.
The document discusses properties of normal distributions and the standard normal distribution. It provides examples of finding probabilities and values associated with normal distributions. The key points are:
- Normal distributions are continuous and bell-shaped. The mean, median and mode are equal.
- The standard normal distribution has a mean of 0 and standard deviation of 1.
- Probabilities under the normal curve can be found using z-scores and the standard normal table.
- Values like z-scores can be determined by finding the corresponding cumulative area in the standard normal table.
This document discusses measures of central tendency including the mean, median, and mode. It provides examples and definitions for each measure. 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 values are ranked in order. The mode is the most frequent value. The best measure depends on the scale of measurement and shape of the distribution, such as whether it is symmetrical or skewed.
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
This document discusses frequency distributions, which summarize data by arranging it into classes and indicating how many observations fall into each class. It describes the key components of a frequency distribution, including the class, class limits, class mark, class interval, class frequency and tally marks. It provides examples of grouped frequency distributions, where the data is organized into groups, and ungrouped distributions, where the raw individual data points are listed. The objectives of a frequency distribution are to estimate population frequencies, facilitate data analysis, and allow computation of statistical measures.
Contingency tables, or crosstabs, summarize the relationship between categorical variables. They display counts of observations cross-classified by discrete predictors and response variables. Contingency tables are used to assess if factors are related, describe data frequencies and proportions, and test relationships between factors using chi-square tests. They show counts in each cell, and row, column, and total percentages to understand associations between independent variables like exposures, dependent outcome variables, and potential confounders.
This document discusses descriptive statistics and measures of central tendency. It defines raw data and descriptive measures. It describes organizing data through ordered arrays and grouped data using frequency distributions. Methods for determining the number of class intervals and class width are provided. Common measures of central tendency - the mean, median and mode - are defined. The mean is the sum of all values divided by the total number. The median is the middle value of ordered data. Examples are given to demonstrate calculating these statistics.
1. The document discusses descriptive statistics, which is the study of how to collect, organize, analyze, and interpret numerical data.
2. Descriptive statistics can be used to describe data through measures of central tendency like the mean, median, and mode as well as measures of variability like the range.
3. These statistical techniques help summarize and communicate patterns in data in a concise manner.
Measures of dispersion
Absolute measure, relative measures
Range of Coe. of Range
Mean deviation and coe. of mean deviation
Quartile deviation IQR, coefficient of QD
Standard deviation and coefficient of variation
This document discusses various statistical methods used to organize and interpret data. It describes descriptive statistics, which summarize and simplify data through measures of central tendency like mean, median, and mode, and measures of variability like range and standard deviation. Frequency distributions are presented through tables, graphs, and other visual displays to organize raw data into meaningful categories.
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.
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
Bar Diagram (chart) in Statistics presentationsheiblu
This document discusses bar diagrams and their components. It defines a bar diagram as a chart that uses rectangular bars to present qualitative data, with the bar lengths proportional to the values. It notes that qualitative data deals with descriptions that can be observed but not measured, such as colors, textures, smells, tastes, and appearances. The key components of a bar diagram are collecting qualitative data, drawing and labeling the x- and y- axes, and drawing the bars. An example bar diagram and table show the numbers of children who favorite different cartoons. Finally, it lists different types of bar diagrams like horizontal, grouped, and stacked bar charts.
An ogive, or cumulative histogram, is a graph used to determine how many data values lie above or below a particular value. It is constructed by plotting the cumulative frequencies on the y-axis against the class limits on the x-axis. Less than ogives start from the upper class limit and add frequencies to the cumulative total, while more than ogives start from the total frequency and subtract frequencies from the cumulative total. Ogives provide a visual summary of a data set and show the proportion of data points above or below values.
An ogive, or cumulative histogram, is a graph used to determine how many data values lie above or below a particular value. It is constructed by plotting the cumulative frequencies on the y-axis against the class limits on the x-axis. Less than ogives start from the upper class limit and add frequencies to the cumulative total, while more than ogives start from the lower limit and subtract frequencies. Ogives provide a visual summary of large data sets and show the proportion of data above or below values.
This document defines and explains ogives, which are cumulative histograms used to determine how many data values lie above or below a particular value. It provides steps for plotting less than and more than type ogive curves, including starting points, plotting points on axes, and joining points with a smooth curve. Advantages are listed as visually summarizing large data sets, delineating intervals, and showing proportions above/below values. Disadvantages include complexity, failing to reflect all data points, and requiring additional explanation. Examples are given of constructing less than and more than cumulative frequency tables and plotting the corresponding ogive curves.
This document discusses ogives, which are cumulative histograms used to determine how many data values lie above or below a particular value. It provides steps for plotting less than and more than type ogive curves, including starting points, plotting points on the x and y axes, and joining points with a smooth curve. Advantages of ogives are that they can summarize large data sets visually and show proportions above or below values. Disadvantages include being complicated to prepare and failing to reveal all details like central tendency. Examples are given of constructing less than and more than cumulative frequency tables and plotting the corresponding ogives.
The document explains how to plot a more than cumulative frequency ogive curve. It involves starting from the lower limit of class intervals and subtracting frequencies to get cumulative frequencies. The points are then plotted on a graph with the upper limit of classes on the x-axis and cumulative frequencies on the y-axis. These points are connected with a smooth curve to show the ogive. An example is provided of data to construct a more than cumulative frequency and plot the corresponding ogive.
The document explains how to plot a more than type ogive curve from a frequency distribution. It involves starting with the cumulative frequency at the lower limit of each class interval, plotting the cumulative frequencies on the y-axis against the upper class limits on the x-axis, and joining the points with a smooth curve. An example is provided of constructing the cumulative frequencies and plotting the points for a sample data set showing marks and their frequencies.
This document provides information on different types of charts and graphs used in statistics. It defines bar graphs, pie charts, histograms, frequency polygons, ogives, pictograms and discusses their uses, advantages and disadvantages. Examples are given for each type of graph to demonstrate how they are constructed and how data is represented visually. Key information on choosing appropriate scales and plotting points for different graphs is also presented.
This document discusses various methods for presenting data, including tabular form, arrays, simple tables, frequency distributions, and stem-and-leaf displays. It provides examples and tasks to practice each method. Specifically, it discusses how to construct frequency distributions and stem-and-leaf displays, including how to determine class limits, boundaries, widths, and marks. The goal is to organize and present data in a meaningful way that allows for easy interpretation and analysis.
Summarizing Data : Listing and Grouping pdfJustynOwen
Introduction
Descriptive Statistics describe basic features of the data gathered from an experimental study in various ways.
They provide simple summaries about the sample via graphs and numbers, mainly measures of center and variation.
Together with graphics analysis (histograms, bar plots, pie-charts), they are the cornerstone of quantitative data analysis.
Tables (frequency distributions, stem-and-leaf plots, …) that summarize the data.
Graphical representations of the data (histograms, bar plots, pie-charts).
Summary statistics (numbers) which summarize the data
The document provides an overview of key concepts in statistics including:
- Descriptive statistics are used to organize and summarize data through measures of central tendency like mean, mode, and median, and measures of spread like interquartile range, variance, and standard deviation.
- Inferential statistics uses samples to make conclusions about populations.
- Variables, observations, and data sets are fundamental components of statistical analysis.
- Data can be either quantitative or qualitative. Quantitative data includes discrete and continuous variables.
- Measures of central tendency and spread help describe the overall patterns in data.
frequency curve and ogive which is used in the quantitative research during survey method which is useful for B.ed and M.ed students. which is helpful for decantation work
The document provides an overview of graphical representation of data. It discusses the key types of graphs used including histograms, frequency polygons, frequency curves, and cumulative frequency curves (ogives). Histograms can be used to represent equal or unequal class intervals. Frequency polygons are constructed by joining the midpoints of histogram rectangles. Frequency curves smooth the lines between points compared to polygons. Cumulative frequency curves plot the running total of frequencies to show the proportion less than or more than each class. Examples are provided for each type of graph.
The document provides information and instructions for analyzing student exam score data. It includes:
1) A table of 80 exam scores ranging from 53 to 97.
2) Instructions to calculate descriptive statistics like minimum, maximum, range, and percentiles of the scores.
3) Directions to construct a frequency distribution table and histogram of the scores binned into intervals of 5.
4) A calculation of measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation) of the scores.
5) An analysis of the distribution's asymmetry and kurtosis.
Descriptive statistics can summarize and graphically present data. Tabular presentations display data in a grid, with tables showing frequencies of categories. Graphical presentations include bar graphs to show frequencies, pie charts to show proportions, and line graphs to show trends over time. Frequency distributions organize raw data into meaningful patterns for analysis by specifying class intervals and calculating frequencies and cumulative frequencies.
This document discusses three methods for graphically displaying data: stem-and-leaf displays, histograms, and box plots. It provides instructions on how to construct each type of graph and describes what each graph conveys visually about a dataset. Stem-and-leaf displays show the distribution of values, histograms summarize frequency distributions of quantitative variables, and box plots simultaneously describe the center, spread, and outliers of a dataset.
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.
LINE AND SCATTER DIAGRAM,FREQUENCY DISTRIBUTIONruhila bhat
The document discusses line graphs and frequency distributions.
1) A line graph uses points connected by lines to show how a value changes over time or as another variable changes. Line graphs are used to track qualitative data and compare changes over time for multiple groups.
2) A frequency distribution organizes data into classes and counts the frequency of observations in each class. It can group or ungroup data. The distribution includes the class, frequency, and tally marks.
This document discusses different methods for presenting data through tables and graphs. It covers descriptive statistics, types of data, purposes of data presentation, frequency distributions, relative frequency distributions, histograms, ogives, bar graphs, pie charts, and choosing the appropriate method based on the type of data. The key goals are to facilitate interpretation of data, effective communication, and displaying patterns and relationships.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
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
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.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
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!
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
4. Cumulative histogram also
known as ogives , are graph
that can be used to determine
how many data values lie above
or below a particular value in a
data set .
8. Following steps are necessary to plot
less than type ogive curve.
1. Start from the upper limit of the
class intervals and then add class
frequency to the cumulative
frequency distribution.
2. Take upper in the x-axis direction
cumulative frequencies along the y-
axis direction.
9. 3.Plot the points ( x , y ) , where
‘x’ is the upper limit of the class
and ‘y’ is the corresponding
cumulative frequency.
4.Join the points by a smooth
curve.
10. Following steps are necessary to plot a more than
type ogive curve ;
1. Starts from the lower limit of the class intervals
and total frequency is subtracted from the
frequency to get the cumulative frequency
distribution .
2. In the graph , consider the lower limit x - axis
direction and cumulative frequencies along y -
axis direction .
11. 3. Plot the points x, y, where ‘x’ is the upper
limit of the class and y is the
corresponding cumulative frequency.
4. Joins the points by a smooth curve.
14. Delineate each interval in the
frequency distribution.
Clarify rates of change between
classes better than other graph.
Provide visual check of
accuracy or reasonableness
of calculations.
15. •Be easily understood due to
widespread use in business and
media.
•Show the number of proportion
or of data point above / below
a particular value.
•Become more smooth as data
points or classes added.
16.
17. Ogive can:
•Fail to reflect all data points in a data set.
•Be somewhat complicated to prepare.
•Reveal little about central
tendency, dispersion , skew
or kurtosis.
18. • Often requires additional written or verbal
explanation.
•Be inadequate to describe to attribute,
behaviour, or condition of
interest.
•Fail to reveal key assumptions.
19.
20. For the data given below , construct a
less than cumulative frequency table
and plot its ogive .
MAR
KS
0-10 10-
20
20-
30
30-
40
40-
50
50-
60
60-
70
70-
80
80-
90
90-
100
FREQ
UENC
Y
3 5 6 7 8 9 10 12 6 4
21.
22. MARKS FREQUENCY LESS THAN
CUMULATIVE
FREQUENCY
0-10 3 3
10-20 5 8
20-30 6 14
30-40 7 21
40-50 8 29
50-60 9 38
60-70 10 48
70-80 12 60
80-90 6 66
90-100 4 70
To plot an ogive we need class boundaries and the cumulative frequencies. For grouped data ,ogive is formed by plotting the cumulative frequency against the upper boundary of the class. For ungrouped data cumulative frequency is plotted on the y-axis against the data which is on the x- axis.
An ogive is a line graph where the bases are the class boundaries and the heights are the <cf for the less than ogive and >cf for the greater than ogive.