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What is Exploratory Data Analysis

Basic Idea of EDA. Model = Smooth RoughVisual techniques can often tease more smooth" out of the rough. Classic vs Exploratory. Classical sequence: Problem > Data > Model > Analysis > ConclusionsExploratory: Problem > Data > Analysis > Model > Conclusions. Data Treatment. Classical uses me

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What is Exploratory Data Analysis

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    1. What is Exploratory Data Analysis? An Approach/Philosophy for data analysis Employs a variety of techniques (mostly graphical)…we will look at 3 of these: scatter plot stem and leaf boxplot (box and whisker)

    2. Basic Idea of EDA Model = Smooth + Rough Visual techniques can often tease more “smooth” out of the rough

    3. Classic vs Exploratory Classical sequence: Problem > Data > Model > Analysis > Conclusions Exploratory: Problem > Data > Analysis > Model > Conclusions

    4. Data Treatment Classical uses mean and standard deviation = point estimates Measure of variance explained - Pearson r Exploratory uses 5-Number Summary: Min, Q1, Median, Q3, Max all (most) data=visual summaries scatterplot stem and leaf boxplot (box and whisker) needs 5 Number Summary

    5. 5-Number summary Arrange data in descending order Find Q1=1/4 data lies below this point Find Median= 1/2 data lies below this point Find Q3=3/4 data lies below this point Find Max score and Min score

    6. Try it with Kings and Queens First make a stem and leaf Then find 5-Number summary Then create a box plot

    7. A correlation measure - Pearson r Measures the strength and direction of two ratio/interval variables Strength is normalized to be between -1 and +1, where zero means there is no relationship The sign + or - indicates the direction of the relationship + means that as one variable goes up in size, so does the other - means that as one variable increases in size, the other decreases

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