ST-260 Week 2 Notes
ST-260 Week 2 Notes ST 260
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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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