Exploring data histograms
•
1 like•574 views
Histograms are used to graphically represent the distribution of data by dividing it into intervals and counting the frequency of data points within each interval. To construct a histogram, the data is divided into bins of equal width and a frequency table is made listing the count in each bin. The bins are then graphed on the x-axis with their corresponding frequencies on the y-axis. Patterns in histograms can reveal the center, shape, spread and skewness of the distribution through characteristics like peaks, symmetry, outliers and the range of values.
Report
Share
Report
Share
Download to read offline
Recommended
Histogram
Histograms provide a graphical representation of the distribution of continuous data by dividing it into equal intervals and displaying the frequency of data points within each interval as bars. Each bar represents a range of data values and its height shows how many data points fall within that range. Histograms allow analysis of extremely large datasets by reducing the data into a single graph that shows peaks and valleys in the distribution in a visually clear way.
Histogram
This document contains a presentation on various statistical charts including bivariate frequency, histograms, frequency polygons, and pie charts. It provides definitions and examples of each type of chart. The presentation was created by 6 students for their professor of management studies.
Histogram
A histogram is a graph that uses vertical rectangles to show the frequency distribution of data across ranges of values or intervals. It displays frequencies on the y-axis and class intervals on the x-axis. Each rectangle's width represents a class interval, and its height shows the frequency of observations within that interval. Histograms simplify complex data and provide an easy way to visualize patterns and compare multiple data sets.
Histogram
A histogram is a graphical representation of data using bars of different heights. It summarizes a data set by dividing it into bins and plotting the count of data points in each bin. The bins are usually consecutive, non-overlapping intervals of a variable. A histogram provides a visual summary of distribution and frequency of data values. It allows comparison of measurements to specifications and assists in decision making.
The uses of Tables & graphs
This document discusses how to improve the clarity of tables and graphs in research articles. It provides rules for constructing clear tables, such as splitting large tables and standardizing layout. When presenting tables, common problems to avoid are poorly positioning them on the page and manipulating spacing. Both tables and their contents should be explained in the text. The document also covers best practices for visualizations like pie charts, bar charts, and line graphs, noting tables are best for exact numbers while graphs show trends.
Drawings, Tables, Graphs
Scientific illustrations like drawings, photographs, tables, and graphs can clearly show relationships and details that may be difficult to explain through text alone. Drawings can depict object details and hidden features. Photographs capture objects at a single moment. Tables organize data in rows and columns. Common graphs are line graphs showing relationships between two variables, bar graphs comparing variables, and circle graphs displaying parts of a whole. Careful analysis of scales is important when interpreting graphs.
Graphs and visual aids 11
Graphic and visual aids are used to summarize information for readers. They include graphs, diagrams, charts, tables and other pictorial forms that consolidate and clarify written material. Visual aids allow complex ideas to be explained concretely and abstract concepts to be visualized. They can also present new information alongside text. Effective interpretation of visual aids involves understanding what is being measured, looking for trends or patterns, and drawing important conclusions. Common types are line graphs, bar graphs, pie charts, organizational charts, flowcharts, diagrams and tables.
Stem and-leaf-diagram-ppt.-dfs
This document discusses stem-and-leaf diagrams, line charts, and scatter diagrams. It provides examples and steps for constructing each type of graph. Stem-and-leaf diagrams display and order numerical data using digits in the tens and ones places. Line charts show the relationship between two quantitative variables over time. Scatter diagrams plot the relationship between two quantitative variables to determine if they are correlated. Examples and steps are given for accurately constructing each graph.
Recommended
Histogram
Histograms provide a graphical representation of the distribution of continuous data by dividing it into equal intervals and displaying the frequency of data points within each interval as bars. Each bar represents a range of data values and its height shows how many data points fall within that range. Histograms allow analysis of extremely large datasets by reducing the data into a single graph that shows peaks and valleys in the distribution in a visually clear way.
Histogram
This document contains a presentation on various statistical charts including bivariate frequency, histograms, frequency polygons, and pie charts. It provides definitions and examples of each type of chart. The presentation was created by 6 students for their professor of management studies.
Histogram
A histogram is a graph that uses vertical rectangles to show the frequency distribution of data across ranges of values or intervals. It displays frequencies on the y-axis and class intervals on the x-axis. Each rectangle's width represents a class interval, and its height shows the frequency of observations within that interval. Histograms simplify complex data and provide an easy way to visualize patterns and compare multiple data sets.
Histogram
A histogram is a graphical representation of data using bars of different heights. It summarizes a data set by dividing it into bins and plotting the count of data points in each bin. The bins are usually consecutive, non-overlapping intervals of a variable. A histogram provides a visual summary of distribution and frequency of data values. It allows comparison of measurements to specifications and assists in decision making.
The uses of Tables & graphs
This document discusses how to improve the clarity of tables and graphs in research articles. It provides rules for constructing clear tables, such as splitting large tables and standardizing layout. When presenting tables, common problems to avoid are poorly positioning them on the page and manipulating spacing. Both tables and their contents should be explained in the text. The document also covers best practices for visualizations like pie charts, bar charts, and line graphs, noting tables are best for exact numbers while graphs show trends.
Drawings, Tables, Graphs
Scientific illustrations like drawings, photographs, tables, and graphs can clearly show relationships and details that may be difficult to explain through text alone. Drawings can depict object details and hidden features. Photographs capture objects at a single moment. Tables organize data in rows and columns. Common graphs are line graphs showing relationships between two variables, bar graphs comparing variables, and circle graphs displaying parts of a whole. Careful analysis of scales is important when interpreting graphs.
Graphs and visual aids 11
Graphic and visual aids are used to summarize information for readers. They include graphs, diagrams, charts, tables and other pictorial forms that consolidate and clarify written material. Visual aids allow complex ideas to be explained concretely and abstract concepts to be visualized. They can also present new information alongside text. Effective interpretation of visual aids involves understanding what is being measured, looking for trends or patterns, and drawing important conclusions. Common types are line graphs, bar graphs, pie charts, organizational charts, flowcharts, diagrams and tables.
Stem and-leaf-diagram-ppt.-dfs
This document discusses stem-and-leaf diagrams, line charts, and scatter diagrams. It provides examples and steps for constructing each type of graph. Stem-and-leaf diagrams display and order numerical data using digits in the tens and ones places. Line charts show the relationship between two quantitative variables over time. Scatter diagrams plot the relationship between two quantitative variables to determine if they are correlated. Examples and steps are given for accurately constructing each graph.
Histograms, frequency polygons, and ogives
This document discusses different graphical methods for representing frequency distributions, including histograms, frequency polygons, and ogives. It explains that histograms use contiguous bars to display data from a frequency distribution, with class boundaries on the x-axis and frequencies on the y-axis. Frequency polygons are similar but use class midpoints on the x-axis instead of boundaries and connect data points with line segments. Ogives are cumulative frequency polygons that show the cumulative total of values below each class boundary. The document also outlines different common distribution shapes like normal, uniform, skewed and bimodal and how to analyze histograms to determine the shape of a data set's distribution.
Reading Tables, Diagrams, Charts
This document provides information about different types of graphics used in texts such as diagrams, tables, graphs and illustrations. It explains that these visuals provide important information and should be studied carefully. Different graphic types are used for different purposes, with charts and graphs used to organize and compare data, and tables used to represent numerical data in an organized way. The document provides tips for reading different graphics, such as reading the title, labels, and any explanatory text to understand the information presented. It also provides examples of questions to answer using the facts presented in sample graphics.
Statistics
This document discusses various statistical concepts and graphs used to represent data including histograms, pie charts, bar graphs, line graphs, frequency tables, grouped frequency tables, dot diagrams, stem and leaf diagrams, and discusses calculating the mode, mean, and median averages from data sets. It also mentions using calculators and averages for grouped data.
Interpreting charts and graphs
This document discusses how to interpret charts and graphs. It explains that graphics provide information in a compact way compared to text. It identifies the most common types of graphs as line graphs, bar graphs, and pie charts. It also discusses tables and diagrams. The document emphasizes that graphics contain important information that supports the reading material, so readers should take time to carefully analyze charts, graphs, and tables.
Presentation and-analysis-of-business-data
This document provides information on presenting and analyzing business data. It defines key terms like data, statistics, frequency, and frequency distribution. It explains how to construct a frequency distribution table from a raw data set by tallying the frequency of each value. It also describes how to create a grouped frequency distribution when data covers a wide range, including how to determine the class size and number of classes. Finally, it discusses visualizing frequency distributions through histograms and polygons in Excel.
Section 1
This document introduces different types of graphs used to display data distributions, including dotplots, histograms, bar charts, stemplots, and timeplots. It discusses when each graph is most appropriate and how to interpret distributions using the CUSS method of describing the center, unusual features, spread, and shape. Examples are provided to demonstrate interpreting distributions based on these characteristics. Students are assigned practice problems interpreting graphs and the CUSS method to prepare for an upcoming quiz.
data handling
This document discusses various methods of data handling and representation. It defines bar graphs, pie charts, and pictographs as common ways to visually represent data using bars, circles, or pictures. It also discusses tally marks, which involve using diagonal lines to count frequencies. The document then explains key statistical concepts like mean, median, mode, and range that are used to summarize and analyze data.
Graphic Aids in Making Reports
This document defines and provides examples of various graphic aids that can be used in reports to enhance textual information and understanding. It discusses figures, graphs, insets, charts, clip art, maps, diagrams, pictures, pictograms, side bars and tables. Specific types of graphs like vertical bar graphs, horizontal bar graphs and stacked bar graphs are described. The document also distinguishes between spot tables and reference tables and identifies the typical parts of tables like captions, rows, columns, stubs and spanner heads. The graphic aids aim to improve appreciation and comprehension of the topic being reported.
Graphic communication
Five key types of graphic aids are bar graphs, line graphs, pie charts, flowcharts, and tables, which visually organize and relate different types of data.
Statistics
This document provides an introduction to statistics and key statistical concepts. It defines statistics as the collection, organization, analysis and presentation of numerical data to make meaningful predictions. It discusses how data can be collected from entire populations or samples, and distinguishes between raw and secondary data. It introduces common statistical tools like frequency distribution tables, grouped frequency tables, measures of central tendency (mean, median, mode), graphical representations (bar graphs, histograms, frequency polygons), and class marks.
Types of graphs
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.
Bar chart
done by : ( ABCD'S &G )
alaa ba-jafar
abrar alshahranii
sahab filfilan
nada alharbi
shahd rajab
Ghadeer suwaimil
I hope that you enjoy and you benefit❤
Advantages and Limitations for Diagrams and Graphs
Includes:-
Types of Graphs
Types of Diagrams.
Advantages & Disadvantages of Graphs.
Advantages & Disadvantages of Diagrams.
Statistical graphs
This document discusses different types of graphs used to represent frequency distributions: bar graphs, histograms, frequency polygons, pie charts, and OGIVE charts. It provides instructions on how to construct each graph type, including labeling axes, ensuring proportionality, and adding titles and legends. Examples of each graph type are shown using sample data on family sizes. The document concludes that bar graphs, histograms, frequency polygons and pie charts are common ways to show frequency distributions, while OGIVE charts illustrate less than and greater than cumulative frequencies.
2 3 depicting frequency tables
This document discusses different types of charts used to visualize qualitative and quantitative data, including histograms, bar charts, pie charts, frequency polygons, ogives, dotplots, and stem-and-leaf plots. It provides examples of how to construct each type of chart and compares their uses for different types of data. Key differences between chart types are outlined, such as histograms using bars to show frequencies and pie charts showing relative frequencies as wedges.
Ogive slide share
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.
NCompass Live: Presenting Data in Meaningful and Interesting Ways
You’ve gathered data to support the case you’re making. But how can you present that data so that the audience understands—and more—is grabbed by it? John Felton, Nebraska Library Commission Planning and Data Services Coordinator, offers some tips and techniques to make your data lift off the page and capture your audience.
NCompass Live: January 13, 2010.
Vendija
A 14-year-old girl named Vendija describes her interests which include drawing, painting, fashion and spending time with her perfect friends and family. She enjoys activities common among Latvian girls like riding bikes, various sports, music and winter activities. Her school has nice teachers and students where she recently had a fun art project painting landscapes.
Stat 4 the normal distribution & steps of testing hypothesis
The document discusses the normal distribution and statistical hypothesis testing. It notes that the normal distribution is also called the Gaussian distribution, and has equal mean, median and mode. It then discusses how much of the data falls within standard deviations of the mean for the normal distribution. The document also covers confidence intervals for means, the steps of statistical hypothesis testing including assumptions, hypotheses, significance levels and tests, and different statistical tests used for numerical and categorical data like t-tests, ANOVA, regression and correlation.
Chapter 3: Prsentation of Data
This document discusses different methods for presenting data, including textual, tabular, and graphical presentations. Tabular presentations include frequency distribution tables that are ungrouped, grouped, simple, and complete. Graphical presentations include bar charts, histograms, frequency polygons, pie charts, and pictographs to visually depict quantitative data using bars, rectangles, lines, circles, or pictures. The examples provided demonstrate how to construct different types of tables and graphs for a set of sample data.
Normal Distribution
This document discusses the normal distribution and related concepts. It begins with an introduction to the normal distribution and its properties. It then covers the probability density function and cumulative distribution function of the normal distribution. The rest of the document discusses key properties like the 68-95-99.7 rule, using the standard normal distribution, and how to determine if a data set follows a normal distribution including using a normal probability plot. Examples are provided throughout to illustrate the concepts.
More Related Content
What's hot
Histograms, frequency polygons, and ogives
This document discusses different graphical methods for representing frequency distributions, including histograms, frequency polygons, and ogives. It explains that histograms use contiguous bars to display data from a frequency distribution, with class boundaries on the x-axis and frequencies on the y-axis. Frequency polygons are similar but use class midpoints on the x-axis instead of boundaries and connect data points with line segments. Ogives are cumulative frequency polygons that show the cumulative total of values below each class boundary. The document also outlines different common distribution shapes like normal, uniform, skewed and bimodal and how to analyze histograms to determine the shape of a data set's distribution.
Reading Tables, Diagrams, Charts
This document provides information about different types of graphics used in texts such as diagrams, tables, graphs and illustrations. It explains that these visuals provide important information and should be studied carefully. Different graphic types are used for different purposes, with charts and graphs used to organize and compare data, and tables used to represent numerical data in an organized way. The document provides tips for reading different graphics, such as reading the title, labels, and any explanatory text to understand the information presented. It also provides examples of questions to answer using the facts presented in sample graphics.
Statistics
This document discusses various statistical concepts and graphs used to represent data including histograms, pie charts, bar graphs, line graphs, frequency tables, grouped frequency tables, dot diagrams, stem and leaf diagrams, and discusses calculating the mode, mean, and median averages from data sets. It also mentions using calculators and averages for grouped data.
Interpreting charts and graphs
This document discusses how to interpret charts and graphs. It explains that graphics provide information in a compact way compared to text. It identifies the most common types of graphs as line graphs, bar graphs, and pie charts. It also discusses tables and diagrams. The document emphasizes that graphics contain important information that supports the reading material, so readers should take time to carefully analyze charts, graphs, and tables.
Presentation and-analysis-of-business-data
This document provides information on presenting and analyzing business data. It defines key terms like data, statistics, frequency, and frequency distribution. It explains how to construct a frequency distribution table from a raw data set by tallying the frequency of each value. It also describes how to create a grouped frequency distribution when data covers a wide range, including how to determine the class size and number of classes. Finally, it discusses visualizing frequency distributions through histograms and polygons in Excel.
Section 1
This document introduces different types of graphs used to display data distributions, including dotplots, histograms, bar charts, stemplots, and timeplots. It discusses when each graph is most appropriate and how to interpret distributions using the CUSS method of describing the center, unusual features, spread, and shape. Examples are provided to demonstrate interpreting distributions based on these characteristics. Students are assigned practice problems interpreting graphs and the CUSS method to prepare for an upcoming quiz.
data handling
This document discusses various methods of data handling and representation. It defines bar graphs, pie charts, and pictographs as common ways to visually represent data using bars, circles, or pictures. It also discusses tally marks, which involve using diagonal lines to count frequencies. The document then explains key statistical concepts like mean, median, mode, and range that are used to summarize and analyze data.
Graphic Aids in Making Reports
This document defines and provides examples of various graphic aids that can be used in reports to enhance textual information and understanding. It discusses figures, graphs, insets, charts, clip art, maps, diagrams, pictures, pictograms, side bars and tables. Specific types of graphs like vertical bar graphs, horizontal bar graphs and stacked bar graphs are described. The document also distinguishes between spot tables and reference tables and identifies the typical parts of tables like captions, rows, columns, stubs and spanner heads. The graphic aids aim to improve appreciation and comprehension of the topic being reported.
Graphic communication
Five key types of graphic aids are bar graphs, line graphs, pie charts, flowcharts, and tables, which visually organize and relate different types of data.
Statistics
This document provides an introduction to statistics and key statistical concepts. It defines statistics as the collection, organization, analysis and presentation of numerical data to make meaningful predictions. It discusses how data can be collected from entire populations or samples, and distinguishes between raw and secondary data. It introduces common statistical tools like frequency distribution tables, grouped frequency tables, measures of central tendency (mean, median, mode), graphical representations (bar graphs, histograms, frequency polygons), and class marks.
Types of graphs
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.
Bar chart
done by : ( ABCD'S &G )
alaa ba-jafar
abrar alshahranii
sahab filfilan
nada alharbi
shahd rajab
Ghadeer suwaimil
I hope that you enjoy and you benefit❤
Advantages and Limitations for Diagrams and Graphs
Includes:-
Types of Graphs
Types of Diagrams.
Advantages & Disadvantages of Graphs.
Advantages & Disadvantages of Diagrams.
Statistical graphs
This document discusses different types of graphs used to represent frequency distributions: bar graphs, histograms, frequency polygons, pie charts, and OGIVE charts. It provides instructions on how to construct each graph type, including labeling axes, ensuring proportionality, and adding titles and legends. Examples of each graph type are shown using sample data on family sizes. The document concludes that bar graphs, histograms, frequency polygons and pie charts are common ways to show frequency distributions, while OGIVE charts illustrate less than and greater than cumulative frequencies.
2 3 depicting frequency tables
This document discusses different types of charts used to visualize qualitative and quantitative data, including histograms, bar charts, pie charts, frequency polygons, ogives, dotplots, and stem-and-leaf plots. It provides examples of how to construct each type of chart and compares their uses for different types of data. Key differences between chart types are outlined, such as histograms using bars to show frequencies and pie charts showing relative frequencies as wedges.
Ogive slide share
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.
NCompass Live: Presenting Data in Meaningful and Interesting Ways
You’ve gathered data to support the case you’re making. But how can you present that data so that the audience understands—and more—is grabbed by it? John Felton, Nebraska Library Commission Planning and Data Services Coordinator, offers some tips and techniques to make your data lift off the page and capture your audience.
NCompass Live: January 13, 2010.
What's hot (17)
Advantages and Limitations for Diagrams and Graphs
Advantages and Limitations for Diagrams and Graphs
Â
NCompass Live: Presenting Data in Meaningful and Interesting Ways
NCompass Live: Presenting Data in Meaningful and Interesting Ways
Â
Viewers also liked
Vendija
A 14-year-old girl named Vendija describes her interests which include drawing, painting, fashion and spending time with her perfect friends and family. She enjoys activities common among Latvian girls like riding bikes, various sports, music and winter activities. Her school has nice teachers and students where she recently had a fun art project painting landscapes.
Stat 4 the normal distribution & steps of testing hypothesis
The document discusses the normal distribution and statistical hypothesis testing. It notes that the normal distribution is also called the Gaussian distribution, and has equal mean, median and mode. It then discusses how much of the data falls within standard deviations of the mean for the normal distribution. The document also covers confidence intervals for means, the steps of statistical hypothesis testing including assumptions, hypotheses, significance levels and tests, and different statistical tests used for numerical and categorical data like t-tests, ANOVA, regression and correlation.
Chapter 3: Prsentation of Data
This document discusses different methods for presenting data, including textual, tabular, and graphical presentations. Tabular presentations include frequency distribution tables that are ungrouped, grouped, simple, and complete. Graphical presentations include bar charts, histograms, frequency polygons, pie charts, and pictographs to visually depict quantitative data using bars, rectangles, lines, circles, or pictures. The examples provided demonstrate how to construct different types of tables and graphs for a set of sample data.
Normal Distribution
This document discusses the normal distribution and related concepts. It begins with an introduction to the normal distribution and its properties. It then covers the probability density function and cumulative distribution function of the normal distribution. The rest of the document discusses key properties like the 68-95-99.7 rule, using the standard normal distribution, and how to determine if a data set follows a normal distribution including using a normal probability plot. Examples are provided throughout to illustrate the concepts.
Measures of Dispersion
If everything were the same, we would have no need of statistics. But, people's heights, ages, etc., do vary. We often need to measure the extent to which scores in a dataset differ from each other. Such a measure is called the dispersion of a distribution.
Graphical Representation of Data
Professor Sana Rehman, Assistant Professor, SCMS Pune talks all about GRAPHICAL REPRESENTATION OF DATA.
Contents:
1. Terminology
2. Qualitative and Quantitative Data
3. Continuous and Discrete Data
4. Primary and Secondary Data
5. Meaning of Statistics
6. Importance of the Organization of India
7. Arranging data in a sequence
8. Grouping and Tabulation of Data
9. Tally table
10. Histogram
11. Bar diagram or bar graph
12. Frequency Polygon
13. Cumulative Frequency Curve or Ogive
14.
Demography
Demography is the statistical study of human populations and encompasses the size, structure, and distribution of populations over time and space in response to births, deaths, and migration. Demographic data can be collected directly from vital records like censuses or birth/death records, or indirectly by surveying samples about topics like fertility rates. The scope of demography includes both macro-level trends like economic issues and population growth, as well as micro-level topics like individual families. A balanced view of demography encompasses factors like population size, composition, distribution, labor forces, and population policies.
Statistics-Measures of dispersions
This document discusses various measures of dispersion in statistics including range, mean deviation, variance, and standard deviation. It provides definitions and formulas for calculating each measure along with examples using both ungrouped and grouped frequency distribution data. Box-and-whisker plots are also introduced as a graphical method to display the five number summary of a data set including minimum, quartiles, and maximum values.
Measures of central tendency
- There are three main measures of central tendency: the 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 data is arranged in order. The mode is the most frequently occurring value.
- To determine which measure to use, consider if the data has extreme values and whether the most common value is needed over the average. The mean can be skewed by outliers while the median and mode are more robust. The appropriate measure depends on the characteristics of the specific data set.
Normal distribution stat
1) The document discusses the characteristics and properties of the normal distribution, including that it is bell-shaped and symmetrical about the mean.
2) It defines z-values as a way to standardize normal distributions by transforming data values into standard scores based on the mean and standard deviation.
3) Examples are provided to demonstrate calculating probabilities using the standard normal distribution, such as finding the percentage of observations that fall within a certain number of standard deviations from the mean.
Normal Probability Distribution
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.
Measure of dispersion part I (Range, Quartile Deviation, Interquartile devi...
This tutorial gives the detailed explanation of "Measure of Dispersion" (Range, Quartile Deviation, Interquartile Range, Mean Deviation) with suitable illustrative example with MS Excel Commands of calculation in excel.
Population and demography
Population and demography are major areas of study for social scientists. A population is defined as a group of individuals occupying a particular place at a given time. Key factors in defining a population are group, place, and time. Population growth impacts issues like housing, food security, and the environment. Demography statistically analyzes population characteristics like size, composition by age and sex, birth and death rates, and immigration. Demographic data is important for policymaking and predicting future trends. Sources of demographic data include censuses, vital statistics like birth and death records, and surveys. Population change is influenced by fertility, mortality, and migration. Population pyramids display the age and sex structure of a population.
STATISTICS: Normal Distribution
The document provides an outline and explanation of key concepts related to the normal distribution. It begins with an introduction to probability distributions for continuous random variables and the definition of a density curve. It then defines terms and symbols used in the normal distribution, including mean, standard deviation, and z-scores. The document explains the characteristics of the normal distribution graphically and provides examples of finding areas under the normal curve using z-tables. It concludes with examples of finding unknown z-values and calculating probabilities for specific scenarios involving the normal distribution.
Vital statistics
The document discusses vital statistics, which are numerical records of life events like births, deaths, marriages, and divorces that can be used to study public health trends. Vital statistics are collected through civil registration systems and sample surveys. They provide data to evaluate health programs, plan for disease control, inform legislation and policymaking, and allow comparisons between populations. Important vital statistics include crude death rate, age-specific death rate, infant mortality rate, neonatal mortality rate, post-neonatal mortality rate, and maternal mortality rate.
Demography
Demography is the study of human populations focusing on population size, composition, and distribution. It deals with 5 demographic processes: fertility, mortality, marriage, migration, and social mobility. Demography can be static, focusing on population structure, or dynamic, focusing on changing patterns of mortality, fertility, and migration. India's population growth rate is the net of crude birth and death rates. India's population is young with 34.33% under 15 and growing elderly population above 60 years. Sex ratio is females per 1000 males and is adverse to women in India. Dependency ratio considers under 15 and over 65 as dependent on 15-64 age group. India's age pyramid is broad at the base and tapering at the top.
Normal distribution and sampling distribution
This document provides an overview of Chapter 5 from the textbook, which covers normal probability distributions. Section 5.1 introduces normal distributions and the standard normal distribution, including their key properties and how to interpret related graphs. It describes how any normal distribution can be transformed into a standard normal distribution for calculation purposes. Section 5.1 also shows how to find areas under the standard normal curve using the standard normal table. Section 5.2 discusses how to calculate probabilities for normally distributed variables by relating them to areas under the normal curve. It provides examples of finding probabilities and expected values.
Vital statistics and demography
This document provides an overview of vital statistics and demography. It defines vital statistics as data dealing with human mortality, morbidity and demography. Key sources of population data are identified as censuses, registration of vital events, sample registration surveys, and institutional records. Details are given on census taking methods, uses of censuses, census in Nepal, information collected in censuses, and measurement of population, mortality, fertility, and other demographic indicators.
Graphical presentation of data
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).
Viewers also liked (20)
Stat 4 the normal distribution & steps of testing hypothesis
Stat 4 the normal distribution & steps of testing hypothesis
Â
Measure of dispersion part I (Range, Quartile Deviation, Interquartile devi...
Measure of dispersion part I (Range, Quartile Deviation, Interquartile devi...
Â
Similar to Exploring data histograms
Introduction to Descriptive Statistics
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.
Presentation1.pptx
The Central Limit Theorem states that the distribution of sample means approximates a normal distribution as the sample size increases, regardless of the population distribution. It can be used to estimate the mean height of students in sports teams by taking random samples, calculating the mean of each sample, finding the mean of the sample means, and observing the results form a bell curve. Discrete and continuous data can be summarized using tables, histograms, stem-and-leaf plots, and other graphs depending on whether the values are countable categories or measured on a scale. Standard deviation is commonly used to measure the dispersion of samples from the same population.
Graphs- A tool to present data
This document discusses graphs and histograms. It defines graphs as diagrams that show relationships between variable quantities. The document then defines different types of graphs like histograms, pie charts, and response surface plots. It focuses on histograms, explaining that they use bars of varying heights to show the distribution of data ranges. Different types of histograms are described, like uniform, symmetric, and bimodal histograms. Examples of a histogram are provided. In conclusion, histograms are said to be an easy way to represent and understand data visually.
Visualization-1
Contains different types of Data Visualizations, best practices to follow for each case and what type of visualization should be made for different kinds of datasets.
Displaying Distributions with Graphs
This document provides an introduction to displaying and describing data distributions through graphs. It discusses:
- Categorical variables can be displayed using bar graphs or pie charts, while quantitative variables use histograms or stem plots.
- Histograms show the distribution of a quantitative variable using bars to represent the frequency of observations within intervals.
- Stem plots separate each observation into a stem and leaf and plot them to display the original values while maintaining the distribution.
- Distributions can be described by their shape, center, and spread. Outliers are observations outside the overall pattern and can indicate important deviations.
Ch4 notes for students
This document discusses different ways to summarize and visualize quantitative data, including dotplots, histograms, and stem-and-leaf plots. Histograms are preferred over dotplots for larger data sets, as they group the data into bins to show the distribution in a compact way. Stem-and-leaf plots preserve the individual data values while also showing the distribution. The document provides examples of different types of histograms and stem-and-leaf plots and discusses how to interpret key aspects of a distribution like its shape, center, spread, and unusual values.
graphic representations in statistics
Graphical representations such as charts and graphs are used to display data in a visual format. They allow readers to focus on key aspects of the evidence or data at a higher level rather than getting caught up in minor details. However, some numeric detail and potential relationships within the data may be lost compared to a table. There are many different types of graphs used in statistics including box plots, histograms, pie charts, bar graphs and line graphs. Each graph has strengths for displaying certain types of data such as continuous versus discrete values.
Wynberg girls high-Jade Gibson-maths-data analysis statistics
The document discusses different types of data and methods for analyzing and displaying data. It describes quantitative and qualitative data, discrete and continuous data. It also explains various methods for interpreting data including pictorial methods like graphs and arithmetic methods like measures of central tendency and dispersion. Specific graphs and measures discussed include histograms, bar graphs, mean, median, mode, range, percentiles, quartiles, and interquartile range. The document also cautions about potential ways that graphs and statistics can be misleading.
Plotting histogram in bigdata analytics
This document discusses plotting histograms in big data analytics. It begins with an introduction to histograms and how they show the distribution of a dataset. It then explains how histograms work by using vertical bars divided into bins to show the frequency of data points. The document also includes tips for creating histograms, such as always starting the vertical axis at 0. It describes extensions of histograms, including overlapping histograms to compare distributions, frequency polygons which connect the midpoints of histogram bars, and density plots which use a continuous curve to estimate the probability density function of the data.
DATA HANDLING-meaning, concept ,examples
This document discusses different types of data visualization techniques. It describes pictographs, bar charts, line charts, pie charts, and histograms. Pictographs use images to represent data, bar charts use bars of different heights to show categorical data, line charts connect data points over time, pie charts represent data as slices of a circle proportional to the values, and histograms bin continuous data into ranges and show frequencies. The document provides examples of each type of chart to analyze and draw conclusions from data.
Quality Journey -- Box Plot.pdf
What is a Box Plot ?
In descriptive statistics, a  boxplot is a method for graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending from the boxes (whiskers) indicating variability outside the upper and lower quartiles, hence the terms box-and-whisker plot and box-and-whisker diagram
Box plots are non-parametric: they display variation in samples of a statistical population without making any assumptions of the underlying statistical distribution
The spacings between the different parts of the box indicate the degree of dispersion (spread) and skewness in the data, and show outliers.
Box Plot Requirement
Minimum : the lowest data point excluding any outliers.
Maximum : the largest data point excluding any outliers.
Median (Q2Â / 50th percentile)Â : the middle value of the dataset.
First quartile (Q1 / 25th percentile) : also known as the lower quartile qn(0.25), is the median of the lower half of the dataset.
Third quartile (Q3 / 75th percentile) : also known as the upper quartile qn(0.75), is the median of the upper half of the dataset.
Interquartile range (IQR)Â : is the distance between the upper and lower quartiles
IQR =Q3-Q1= qn(0.75) –qn(0.25)
A boxplot is constructed of two parts, a box and a set of whiskers . The lowest point is the minimum of the data set and the highest point is the maximum of the data set. The box is drawn from Q1 to Q3 with a horizontal line drawn in the middle to denote the median.
Why Box Plot is useful ?
Box plots divide the data into sections that each contain approximately 25% of the data in that set.
When the median is in the middle of the box, and the whiskers are about the same on both sides of the box, then the distribution is symmetric
When the median is closer to the bottom of the box, and if the whisker is shorter on the lower end of the box, then the distribution is positively skewed (skewed right)
When the median is closer to the top of the box, and if the whisker is shorter on the upper end of the box, then the distribution is negatively skewed (skewed left)
How to compare box plot
How to draw box plot on Excel
Step 2: Calculate quartile differences
Next, calculate the differences between each phase. In effect, you have to calculate the differentials between the following:
First quartile and minimum value
Median and first quartile
Third quartile and median
Maximum value and third quartile
Step 3: Create a stacked column chart
The data in the third table is well suited for a box plot, and we'll start by creating a stacked column chart which we'll then modify.
Select all the data from the third table, and click Insert > Insert Column Chart > Stacked Column.
To reverse the chart axes, right-click on the chart, and click Select Data.
Click Switch Row/Column.
Click OK.
The next step is to replace the topmost and second-from-bottom (the deep blue and orange areas in the image) data series with lines, or whiskers.
Select the to
Exploring Data
This document discusses various methods for displaying and summarizing distributions of data, including graphs like bar graphs, dotplots, histograms and time plots, as well as numerical summaries like the mean, median, quartiles, and standard deviation. It provides examples and explanations of how to construct and interpret these different statistical summaries.
Tabulation of data-Development of Research Instrument/ Tool and Analysis
The document discusses tabulation, which is the process of systematically organizing data into a table. The key aspects covered include:
- Tabulation facilitates comparison, statistical analysis, and interpretation of data by arranging related information close together.
- The main components of a statistical table are the title, caption, stub, body, footnotes, and source note. Proper tabulation follows guidelines like using consistent row/column headings and including subtotals.
- Tabulation has benefits like simplifying complex data, enabling easy comparison, economizing space, and facilitating statistical analysis. Well-structured tables clearly present essential data features.
Displaying data using charts and graphs
Bar charts, line graphs, pie charts, scatter plots, and histograms are commonly used types of charts. Each type of chart has distinct characteristics that make it suitable for visualizing certain types of data relationships. Bar charts are useful for comparing discrete categories, line graphs show trends over time, pie charts show proportions, scatter plots reveal correlations between two variables, and histograms display frequency distributions. Proper chart selection and design ensure data is presented clearly and accurately.
Visualization Techniques- Box plot, Line Chart, Scatter plot, Bar chart.
Visualization Techniques- Box plot, Line Chart, Scatter plot, Bar chart.
Visualization Techniques ,Exploratory Data Analysis(EDA), Histogram
Visualization Techniques, Exploratory Data Analysis(EDA), Histogram.
Data presenatation
This document defines and provides examples of different types of data:
- Discrete and categorical data can be counted and sorted into categories.
- Nominal data involves assigning codes to values. Ordinal data allows values to be ranked.
- Interval and continuous data can be measured and ordered on a scale.
- Frequency tables, pie charts, bar charts, dot plots and histograms are used to summarize different types of data. Outliers, symmetry, skewness and scatter plots are also discussed.
Artificial Intelligence - Data Analysis, Creative & Critical Thinking and AI...
Artificial Intelligence - Data Analysis, Creative & Critical Thinking and AI...deboshreechatterjee2
This PPT is about AI Values, Data Analysis, and Creative & Critical Thinking in Artificial Intelligence.Â
3.1 Measures of center
This document provides an overview of measures of central tendency including the mean, median, and mode. It discusses how to calculate and interpret each measure using examples with data sets. The mean is calculated by adding all values and dividing by the total number. The median is the middle value when data is arranged in order. The mode is the value that occurs most frequently. Other measures discussed include the midrange and calculating the mean from a frequency distribution. Proper rounding of measures is also covered.
DATA-PROCESSING.pptx
The document discusses data processing and analysis. It defines data processing as collecting, organizing, and analyzing raw data to extract meaningful information. Key aspects of data processing include data preparation, conversion of raw data into useful information, and outputting results through reports, graphs, and diagrams. Common data processing methods are manual, electronic, and hybrid. The document also outlines steps in data analysis such as compilation, editing, coding, classification, and tabulation. It describes descriptive and inferential statistics used to analyze quantitative and qualitative data. Finally, it discusses various charts, diagrams, tables, and graphs used to visually present analyzed data.
Similar to Exploring data histograms (20)
Wynberg girls high-Jade Gibson-maths-data analysis statistics
Wynberg girls high-Jade Gibson-maths-data analysis statistics
Â
Tabulation of data-Development of Research Instrument/ Tool and Analysis
Tabulation of data-Development of Research Instrument/ Tool and Analysis
Â
Visualization Techniques- Box plot, Line Chart, Scatter plot, Bar chart.
Visualization Techniques- Box plot, Line Chart, Scatter plot, Bar chart.
Â
Visualization Techniques ,Exploratory Data Analysis(EDA), Histogram
Visualization Techniques ,Exploratory Data Analysis(EDA), Histogram
Â
Artificial Intelligence - Data Analysis, Creative & Critical Thinking and AI...
Artificial Intelligence - Data Analysis, Creative & Critical Thinking and AI...
Â
More from Ulster BOCES
Sampling means
This document discusses key concepts related to sampling means, including that bias can occur if sample proportions or means are skewed from the population values, the central limit theorem states that sample means will follow a normal distribution as sample size increases, and the sampling distribution of sample means from any population will be approximately normal in shape with standard deviation equal to the population standard deviation divided by the square root of the sample size.
Sampling distributions
This document defines key terms related to sampling distributions, including population, parameters, statistics, population proportion, and sample proportion. It explains that as more samples are taken from a population, the sample proportions will begin to form a bell-shaped curve centered around the true population proportion. The mean of all sample proportions equals the population proportion. Under certain conditions where the sample size is large enough and neither the sample proportion or population proportion is too close to 0 or 1, the sampling distribution can be approximated as a normal distribution, allowing us to calculate a z-score to determine how many standard deviations a sample proportion is from the population proportion.
Geometric distributions
This document defines and provides examples of a geometric distribution. It explains that a geometric distribution models outcomes of repeated Bernoulli trials where the probability of success is constant and each trial is independent. The key properties are that the trials continue until the first success occurs and there is no fixed number of trials. It gives the mean and standard deviation formulas for a geometric distribution and provides examples calculating probabilities of sequences of outcomes like having 5 boys before the first girl.
Binomial distributions
The document discusses the binomial distribution and its key properties:
1) Outcomes are success or not success, with a fixed probability of success.
2) The number of trials is fixed.
3) Trials are independent.
It provides the mean and standard deviation formulas, and explains how to calculate the probability of a binomial event occurring exactly, at least, or at most a certain number of times using the binomial probability formula. An example calculates the probability of a team winning 6 or more games in 8 tries with a 40% chance of winning each game.
Means and variances of random variables
This document discusses means, variances, and standard deviations of random variables. It provides the formulas for calculating the mean and variance of a random variable. For the mean, it is the sum of each outcome multiplied by its probability. For the variance, it is the sum of the squared differences of each outcome from the mean multiplied by its probability. An example is provided to demonstrate calculating the standard deviation of outcomes from selling cars. The document also outlines rules for how the mean and variance are affected when combining or transforming random variables through addition, subtraction, multiplication, and addition of a constant.
Simulation
1) The document defines simulation as imitating chance behavior based on an accurate model of a random phenomenon.
2) It outlines the steps of simulation: stating the problem, assumptions, assigning digits to represent outcomes, simulating many repetitions, and stating a conclusion based on experimental evidence.
3) As an example, it considers the probability that a female student will be chosen from an AP Stats class to bring candy, assuming 80% of the class is male. It shows how digits could be assigned to represent the male and female outcomes.
General probability rules
This document defines key probability terms and provides 6 general probability rules. The rules state that: 1) A probability is between 0 and 1, 2) The probabilities of all possible outcomes sum to 1, 3) The probability of events A or B is the sum of their individual probabilities minus their intersection, 4) The probability of the complement of an event is 1 minus the original probability, 5) The probability of independent events occurring together is the product of their individual probabilities, and 6) The probability of disjoint events is the sum of their individual probabilities.
Planning and conducting surveys
This document discusses key concepts related to survey sampling including populations, samples, random selection, and sources of bias. It defines a population as the entire group being studied, and a sample as the subset used to make inferences about the population. Random selection is described as a process that gives all members of the population an equal chance of being selected to reduce bias. Common sources of bias like convenience samples and voluntary response samples are discussed. Strategies for reducing bias like simple random sampling, stratified random sampling, and cluster sampling are also outlined.
Overview of data collection methods
This document discusses different methods for collecting data for studies, including censuses, sample surveys, experiments, and observational studies. It notes that the method must be chosen based on clarifying the research question and deciding on a plan for data collection and analysis. It also outlines three main factors to consider when selecting a data collection method: the purpose of the evaluation, the evaluation design, and available project resources.
Exploring bivariate data
This document discusses analyzing and summarizing relationships between two quantitative variables (bivariate data) using scatterplots. It covers key topics like correlation, linear regression lines, residuals, outliers and influential points. Scatterplots display the relationship between two variables and can show positive or negative linear associations or no relationship. Correlation coefficients measure the strength and direction of linear relationships, while regression lines predict variable relationships. Residual plots assess linearity and outliers.
Normal probability plot
A normal probability plot provides an assessment of whether a normal distribution model fits a data set well. It involves arranging data from smallest to largest, finding the percentile and z-score for each value, and plotting the data points against their z-scores. The closer the plot forms a straight line, the better the normal model fits the data.
Exploring data stemplot
This document discusses different graphical methods for displaying categorical and quantitative data distributions, including pie charts, bar graphs, stem-and-leaf plots, back-to-back stem-and-leaf plots, and modifications to stem-and-leaf plots like splitting stems or trimming digits. It provides instructions on how to construct stem-and-leaf plots, including separating data into stems and leaves and ordering them, and notes that stem-and-leaf plots work best for smaller, less spread out data sets.
Exploring data other plots
This document discusses different types of graphs for describing data distributions: relative frequency histograms show the percent of counts in each bin while cumulative relative frequency histograms called ogives show the cumulative percents; time plots display observations over time with the horizontal axis always being time and can reveal trends over multiple observations.
Calculating percentages from z scores
This document provides instructions for calculating percentages from z-scores. It explains how to find a percentile using a normal density curve by first calculating the z-score and then using a table or calculator to find the percentile. It also describes how to find the percent of data between two z-scores by calculating each z-score, finding the percentile value for each, and subtracting the percentiles.
Density curve
A density curve is a smooth curve drawn over a histogram that represents a probability distribution, with an area of 1. It can take different shapes like a normal bell curve or a uniform rectangle. A normal distribution is a bell-shaped density curve where we can use z-scores to determine population properties or the percentile rank of a data point. If data fits a normal distribution, the 68-95-99.7 rule applies, meaning 68%, 95%, and 99.7% of data falls within 1, 2, and 3 standard deviations of the mean, respectively.
Standardizing scores
The document discusses how to calculate z-scores, which measure how many standard deviations a data point is from the mean. It provides examples of calculating z-scores for test scores when given the class mean of 80, standard deviation of 6.07. A score of 89 is 1.482 standard deviations above the mean, while a score of 69 is 1.812 standard deviations below the mean.
Intro to statistics
Statistics is the science and art of learning from data. It involves collecting and analyzing data to describe variables and examine relationships between variables. There are different types of variables, including categorical and quantitative variables, and different ways of collecting data, such as surveys, experiments, and observational studies. Graphs and numerical summaries are used to organize and make sense of the data. Statistical inference allows conclusions to be drawn from a sample to a larger population with some degree of uncertainty.
Describing quantitative data with numbers
1. Quantitative data can be summarized using measures of center (mean, median), spread (range, IQR, standard deviation), and position (quartiles, percentiles, z-scores).
2. The mean is more affected by outliers than the median. The median is more resistant to outliers and a better measure of center for skewed data.
3. Additional summaries like the five-number summary and boxplots provide a graphical view of the distribution and identify potential outliers.
Displaying quantitative data
This document provides guidance on visualizing and interpreting quantitative data using various graph types, including dot plots, stemplots, histograms, ogives, and time plots. It discusses how to construct each graph type and what features to examine, such as shape, outliers, center, and spread. Examples are provided for each graph type along with questions to check understanding.
A.2 se and sd
The document discusses standard error and standard deviation. It defines parameters as describing populations using Greek letters, while statistics describe samples using non-Greek letters. Standard error estimates the standard deviation of a statistic and is used to calculate confidence intervals and margin of error. It relies on sample statistics rather than population parameters. The central limit theorem states that for a large enough sample size from any population, the sampling distribution of the sample mean will be approximately normally distributed.
More from Ulster BOCES (20)
Recently uploaded
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Best 20 SEO Techniques To Improve Website Visibility In SERP
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
This case study explores designing a scalable e-commerce platform, covering key requirements, system components, and best practices.
JavaLand 2024: Application Development Green Masterplan
My presentation slides I used at JavaLand 2024
SAP S/4 HANA sourcing and procurement to Public cloud
SAP S4 HANA to Public cloud data object differences
Introduction of Cybersecurity with OSS at Code Europe 2024
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...Edge AI and Vision Alliance
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.Â
Fueling AI with Great Data with Airbyte Webinar
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
A Comprehensive Guide to DeFi Development Services in 2024
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Trusted Execution Environment for Decentralized Process Mining
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Building Production Ready Search Pipelines with Spark and Milvus
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
AWS Cloud Cost Optimization Presentation.pptx
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und ĂĽberflĂĽssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Recently uploaded (20)
Best 20 SEO Techniques To Improve Website Visibility In SERP
Best 20 SEO Techniques To Improve Website Visibility In SERP
Â
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Digital Banking in the Cloud: How Citizens Bank Unlocked Their Mainframe
Â
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
Â
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Â
JavaLand 2024: Application Development Green Masterplan
JavaLand 2024: Application Development Green Masterplan
Â
SAP S/4 HANA sourcing and procurement to Public cloud
SAP S/4 HANA sourcing and procurement to Public cloud
Â
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Â
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Â
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
“Temporal Event Neural Networks: A More Efficient Alternative to the Transfor...
Â
A Comprehensive Guide to DeFi Development Services in 2024
A Comprehensive Guide to DeFi Development Services in 2024
Â
Trusted Execution Environment for Decentralized Process Mining
Trusted Execution Environment for Decentralized Process Mining
Â
Building Production Ready Search Pipelines with Spark and Milvus
Building Production Ready Search Pipelines with Spark and Milvus
Â
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Â
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Â
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Â
Exploring data histograms
- 2. Histograms Histograms are similar to stemplots They break data into intervals so that you can determine the center, shape and spread of the distribution Unlike stemplots, each individual data point is not included in the histogram only represented in its interval
- 3. Constructing a Histogram Step 1 – Divide the data into bins (intervals) of equal width. Be sure the data will fit into these as nicely as possible Step 2 – Create a frequency table, listing each interval and the count of the number of data in each interval Step 3 – Title and scale your graph and label your axes appropriately (Bins on x-axis, frequencies on y-axis) Step 4 – Graph your histogram. Be sure you DO NOT leave spaces between the bars unless there is an empty bin
- 4. Patterns in Histograms • Center – • Is it skewed? If there is a tail on either side that is longer than the other the side the tail is on is considered to be skewed (think skewer) • Right skew has a few large values, but most values are small • Left skew has a few small values, but most values are large
- 5. More Patterns • Shape Does it have one peak (unimodal)? Does it have two peaks (bimodal)? Is it symmetric? Is it roughly symmetric?
- 6. Deviations • Spread Are there any outliers (numbers by themselves)? Are there any deviations from symmetry? What is the range of values that are being displayed?