Organizing and displaying data
Organizing and displaying data are important in statistics. After data are collected, the data may not make sense just looking at them.
That is why it is important to organize and display the data using tables or graphs.
Here you will learn how to organize and display quantitative and qualitative data using bar graphs, pie graph, histograms, polygons, and stem-and-leaf display.
Raw data
What is raw data? What is quantitative data? What is qualitative data? What is the difference between quantitative data and qualitative data?
Frequency distribution
What is a frequency distribution? Learn how to organize qualitative raw data with a frequency table.
Relative frequency distribution
What is a relative frequency distribution? Learn how to expand the frequency table with the relative frequency distribution.
Display of qualitative data
Learn how to display qualitative data with a bar graph and a pie chart
Frequency distribution of quantitative data
Learn how to organize and display quantitative raw data with a frequency table and a bar graph
Procedure for constructing a frequency table
General guidelines to follow when constructing a frequency table for a set of quantitative data
Constructing a frequency table
Learn how to construct a frequency table for a set of quantitative raw data using the procedure outline in the lesson immediately above
Less than method for writing classes
Learn to use the less than method for writing classes to make frequency tables, calculate relative frequency, and percentage.
Graphs of quantitative data
Learn how to display quantitative data with a bar graph and a pie chart
Shapes of histograms
Learn about the different shapes of histograms. The three most common of these shapes are skewed, symmetric, and uniform.
Cumulative frequency distribution
What is the cumulative frequency distribution? Learn how to find the cumulative frequency distribution for a class using a frequency distribution table
100 Tough Algebra Word Problems.
If you can solve these problems with no help, you must be a genius!
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