Graphing
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Graphs can effectively visualize data relationships but must be designed carefully. Bar charts and pie charts are appropriate for discrete categorical data. Bar charts compare frequencies as proportional bar widths and heights. Pie charts show category proportions through wedge sizes. Histograms and line graphs effectively display continuous and some ordinal data. Histograms use bar widths to represent value ranges. Line graphs connect data points to show trends over intervals. However, graphs can mislead if they distort data scales, use inappropriate or confusing designs, or include non-data elements. Data representation and readability should always be prioritized in graph creation.
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Dissecting How to Choose the Right Chart
Because there can be so many different ways to visualize your data, it helps to have a basic guideline to follow.
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.
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Column, line, pie, bar, area, and surface charts can all be created in Excel using data arranged in columns and rows on a worksheet. Column charts show comparisons over time, line charts show trends at intervals, pie charts show parts of a whole, bar charts compare individual items, area charts emphasize magnitude of change over time, and surface charts find optimum combinations between two data sets.
Graphical representation of Data in Research.pdf
This document provides an overview and examples of different types of graphical data presentations including bar graphs, pie charts, line graphs, and histograms. Bar graphs use rectangular bars to compare discrete data across categories. Pie charts represent data as fractional parts of a whole in a circular graph. Line graphs show changes over continuous time. Histograms use rectangles to display the distribution of continuous data grouped into bins. Each type of graph has advantages for visualizing certain types of data relationships.
Diowane2003
The document discusses different types of graphs such as bar graphs, pie charts, pictographs, and line graphs. It defines what a graph is and provides guidelines for creating good graphs, including determining the target audience, main messages, and appropriate graph type. The purpose of using graphs is to visually represent relationships between variables in a way that facilitates understanding and convinces readers.
Graphical Representation of data
William Playfair, a Scottish engineer and economist, is considered the principal inventor of statistical graphs. In 1786, he published the 'Commercial and Political Atlas' containing 44 charts, which introduced the line graph, bar graph, and pie chart. Graphical representations visually display data using plots and charts to quantify, sort, and present information in an understandable way for various audiences. Common types include bar graphs, line graphs, histograms, and pie charts, which each have specific rules for effectively displaying different types of data.
Different Types of Graphs - Copy.pptx
This document outlines different types of graphs used to display data including circle graphs, bar graphs, pictographs, broken line graphs, continuous line graphs, and scatter plots. It provides brief definitions and examples of each graph type, noting that circle graphs show portions of a whole, bar graphs are used for comparisons, pictographs use pictures to represent data, broken line graphs show trends over time, continuous line graphs have meaning between data points, and scatter plots show a set of plotted points.
Data Visualization Technique - How to Design Charts and Graphs
Here I am sharing the 101 way of Data Analysis technique to how visualize technique give more better data then numerical analysis via HubSpot.
Guía sobre como diseñar graficos para tus informes
This document provides guidance on designing effective data visualizations. It discusses the main types of charts and graphs including bar charts, pie charts, line charts, area charts, scatter plots, bubble charts and heat maps. For each type, it describes best practices for design, such as starting the y-axis at 0, using consistent colors, and limiting the number of data series shown. The goal is to help readers understand their data and tell their intended story through clear, impactful visual representations.
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Data Visualization Technique - How to Design Charts and Graphs
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Guía sobre como diseñar graficos para tus informes
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Location Scores
The document discusses the risks of investing heavily in stocks compared to other investment strategies. It notes that stocks have historically returned around 9.6% annually on average but with significant volatility, ranging from a 12% loss to a 31% gain in any given year. This level of volatility would require an investor to have a strong stomach and tolerance for risk. The article suggests that a major stock position close to retirement could force an investor to reconsider their retirement plans if a bad year for the market coincided with their retirement.
Variability
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This document discusses key concepts in research methods and statistics. It covers variables, levels of measurement, experimental design, operational definitions, types of variables, scales of measurement, and order of operations. The major focus is on measurement, variables, experimental design, and understanding different scales and levels of measurement which are fundamental aspects of research methods and statistics.
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1. Students were randomly assigned to one of three groups and given a math test. The teacher then provided different levels of praise to each group: high praise to Group A, moderate praise to Group B, and no praise to Group C.
2. The next day, all students took another math test, and their scores were analyzed using ANOVA to determine if the different levels of praise had an effect.
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One-Sample Hypothesis Tests
A researcher tested the effectiveness of an herbal supplement on physical fitness using the Marine Physical Fitness Test. 25 college students took the supplement for 6 weeks and averaged a score of 38.68 on the test, compared to the average population score of 35. Using a t-test with α=.05 and df=24, the researcher found the average score of 38.68 was not significantly different than the population mean of 35 (t=2.041, p=.052). Therefore, there is not enough evidence to conclude the supplement had an effect on fitness levels, as the higher average score could be due to chance for this small sample.
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Graphing
- 1. Chapter 2 and Infographics Project Graphs: Good, Bad & Ugly
- 2. A visual presentation of data Relationships & comparisons are visual Less daunting to some than tables of numbers Allows some artistry and creativity Accuracy is important Style of graph must match Scale (level) of measurement of the variable(s) Nature of this particular data set Purpose of a Graph
- 6. Data bars proportional to number of cases in data
- 7. Balanced
- 10. Not complicated
- 12. Area of bars combined is 100% Area of each bar is proportional to its percent of total Bars do not touchbecause categoriesare discrete. Many variations; this is the most simple. BAR CHART: the Good
- 13. The Bad:design hides trends or data
- 14. PICTOGRAPH: the Good bars constructed of equal size simple icons
- 15. PICTOGRAPH: the Ugly Elements of unequal size Just heads of some kids All children are playing except those from China – subtle racism
- 16. BAR CHART – problems to consider:area, color – & why is that jogger there?
- 17. Practice: How many problems can you see in this graph?
- 18. PIE CHART: the Acceptable Area of pie = 100% Wedge is proportional to percentage of cases Labels show count or percent Ten slices is the maximum to remain clear & readable.
- 19. PIE CHART: the Badcharts confuse or obscure the pattern in the data
- 20. What can you detect in this old graph?
- 21. Graphs for Continuous Data (sometimes used for Ordinal data) Graph shows continuity of the construct Histogram: bars that touch at real limits Line graph: covers range (a.k.a. Frequency Polygon) Horizontal axis goes from low to high Intervals shown for Interval or Ratio data Some ordinal data also graphed this way(e.g., strongly agree, agree, slightly agree, etc)
- 22. Bar width is a rangeof scores or the reallimits of scores. Ranges equal width Labels show mid-point or real limits Low scores on left, high scores on right HISTOGRAM: the Good
- 23. HISTOGRAM: the Bad Ranges of data Unequal & indeterminate Spacing of “bars” is unequal. Water, sky, umbrella detract from graph
- 25. Lowest categories are on the right, not left.
- 26. Intervals of income are unequal/indeterminate.
- 28. LINE GRAPH: the Bad Why is the headline “Steady growth” for this graph? Hint: check the axis values If it is growth, is it steady ? Hint: how did each of the three variables change from 1988 to 1989.
- 29. Modern graphs have more options The Future of Food. (2008) WiredMagazine 16:11
- 30. Chapter 2 and Infographics Project Graphs: Good, Bad & Ugly