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by: Carter Cox

51

1

3

# ST-260 Week 2 Notes ST 260

Carter Cox
UA

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Graphical and Numerical Summaries
COURSE
Statistical Data Analysis
PROF.
Dr. Marcus Perry
TYPE
Class Notes
PAGES
3
WORDS
CONCEPTS
Statistics
KARMA
25 ?

## Popular in Statistical Data Analysis

This 3 page Class Notes was uploaded by Carter Cox on Monday September 5, 2016. The Class Notes belongs to ST 260 at University of Alabama - Tuscaloosa taught by Dr. Marcus Perry in Fall 2016. Since its upload, it has received 51 views. For similar materials see Statistical Data Analysis in Business at University of Alabama - Tuscaloosa.

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Date Created: 09/05/16
Week 2 Graphic Summaries After collection of data - Organize and Summarize (graphically or numerically) Graphs - Single Quantitative Variable o Histogram  Best for large data series o Stem and Leaf Plots  Best for small data set  Preserves actual data values o Dot Plot  Small data plots  Quick and easy - Single Categorical Variable o Bar Chart  Similar to Histogram  Order of bars lacks meaning o Circle Graph/ Pie Chart  Small slices are problematic - Two Quantitative Variables o Scatter Plot o Curvilinear, typical value, spread, outlier o Time Series Plot - Two Categorical Variables o Two Way Table  Organizational tool  Joint frequencies and marginal counts o Stacked Bar Chart  Bar chart with segmented bars - 3D Graph o Often hard to read o Messy and uninformative Key Features of a Graph - Shape - Typical Value - Spread - Outliers Shapes of Data Distributions - Symmetric o Left and right halves are “mirror images” - Skewed Left o Extreme values extend to left or negative direction - Skewed Right o Extreme value extend right in positive direction Numerical Summaries Population of Interest - Things we wish to learn about 1 V Key Characteristics - Typical Value or variation 1 V Parameters - True values for a population First Principle - Numerical summaries should quantify key characteristics of a data set o Location and variation Measure of Location/ Center - Mean - Symmetric Distributions - Median – Distribution with outliers - Mode – Categorical Variable - Trimmed Mean – Distribution with Outliers Measures of Variation - Sample Variance o Sym. distribution - Sample Standard o Sym. Distribution - Range o Distribution without outliers - Interquartile Range o Distribution without outliers

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