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## Organizing and Summarizing Data

by: Andrew Isbell

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0

2

# Organizing and Summarizing Data MAT 120

Marketplace > Tri-County Technical College > Mathmatics > MAT 120 > Organizing and Summarizing Data
Andrew Isbell
TCTC

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These notes cover methods of organizing and summarizing Quantitative and Qualitative Data.
COURSE
Probability and Statistics
PROF.
Merle Glick
TYPE
Class Notes
PAGES
2
WORDS
CONCEPTS
Mathematics, Probability, Statistics
KARMA
25 ?

## Popular in Mathmatics

This 2 page Class Notes was uploaded by Andrew Isbell on Thursday August 25, 2016. The Class Notes belongs to MAT 120 at Tri-County Technical College taught by Merle Glick in Fall 2016. Since its upload, it has received 7 views. For similar materials see Probability and Statistics in Mathmatics at Tri-County Technical College.

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Date Created: 08/25/16
Organizing and Summarizing Data  Andrew Isbell  *Organizing Qualitative data: ­Data which is not yet organized is referred to as “Raw Data” ­Frequency distribution­ the frequency of each category and the number of times this category occurs. ­Relative frequency­ the proportion/percent of observations within a category ad is found using the  formula, Frequency/Sum of all frequencies. ­Pareto chart­ a bar graph in which the bars are in decreasing order, from tallest to shortest  ­Pie chart­ a circle divided into sections which each represents a category of data by utilizing relative  frequency ­Bar graphs display either frequency or relative frequency while pie charts can only display relative  frequency *Organizing Quantitative data: ­Two types: Discrete (counted) and Continuous (measured)  ­With discrete, categories are formed and values are placed according to their category ­With continuous, histograms may be made. Unlike a bar graph, a histogram has bars which are touching,  however, like a bar graph, either frequency or relative frequency may be shown  ­Classes are categories in which continuous data is classified in. These are made when the values of data  are large. For example, instead of 0, 1, 2 & 3, there may be 0­50,50­100 and so on ­Lower class limit­ the lowest value in a data set  ­High class limit­ the highest value in a data set  ­Class width­ the difference between the high class limit and the lower class limit ­Generally, there are between five and twenty classes ­Class width is equal to the highest class limit­ the lower class limit/ the number of classes ­Stem­leaf plots are also an option  ­Stem­ all digits besides the last digit  ­Leaf­the last digit not included in the stem  ­Stem and Leaf plots do not lend themselves well to large data sets, however one notable  advantage is how specific they are  ­S and L plots are always in ascending (smallest to largest) order  ­Split stems may be used as well which will reveal the distribution of data better  ­In a split stem and leaf plot, each value has its own stem and leaf  ­Raw data may be retrieved with both a stem and leaf plot and a split stem and leaf plot ­Dot plots may be drawn although these are not common in most cases  ­Dot plots look just like bar graphs, but with dots instead of bars ­No label is needed on the y­axis, the number of dots are only counted to find its value  ­Dot plots are used for discrete quantitative data  ­Shapes of Distributions­ how data is spread out: ­Uniform distribution (symmetric)­ the frequency of each value of the variable is evenly spread out cross  the values of the variable  ­Bell­shaped (symmetric)­ highest frequency in the middle with tail offs at beginning and ends of graphs  ­Skewed right­ most frequency values on the left of the graph  ­Skewed left­most frequency values on the right of the graph

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