# SJFC MSTI 130 - Interpreting Spatial Models (30 pages)

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## Interpreting Spatial Models

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## Interpreting Spatial Models

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Lecture Notes

Pages:
30
School:
St. John Fisher College
Course:
Msti 130 - Mathematical Modeling and Quantitative Analysis
##### Mathematical Modeling and Quantitative Analysis Documents

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Chapter 6 Interpreting Spatial Models1 This chapter aims to do two things Part A focuses on how to estimate statistics particularly the mean and standard deviation from data that is only presented in summary form like a frequency table or a histogram Part B takes this one step further by helping you connect two different ways of picturing data by relating histograms and boxplots Both give a picture of how the data is spread out The difference is that a boxplot takes the data and breaks it into four chunks with the same number of observations in each chunk but with each chunk of data having a different length Histograms are the opposite each chunk has exactly the same length but probably has different numbers of observations in it As a result of this chapter students will learn Why summarized data cannot be used to compute an accurate mean or standard deviation What a percentile is What a cumulative distribution is As a result of this chapter students will be able to Estimate the mean from a set of summarized data Estimate the standard deviation from a set of summarized data Sketch a boxplot of the data underlying a histogram without having the data itself Sketch a rough idea of a histogram of data based only on a boxplot of the data 1 c 2011 Kris H Green and W Allen Emerson 163 164 6 1 CHAPTER 6 INTERPRETING SPATIAL MODELS Estimating Stats from Frequency Data Many times we are presented with data in newspapers magazines the Internet or meetings but these data are rarely presented in its entirety After all in many cases there are thousands of observations of each variable It is therefore more common to present summarized data in the form of tables or charts that show the number or frequency of observations that fall into a certain range or bin In the last chapter we used this idea to create a graphical depiction of the data in the form of a histogram But what if you are starting from the summarized data and what to know something about the original data itself For

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