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week7_lec1-2_functiondiscovery_s09

# week7_lec1-2_functiondiscovery_s09 - Announcements Prof...

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Announcements Prof. Diefes-Dux Office Hours Regularly: Wed 9:30-11 am, ARMS B098 Exam 2 Tuesday March 10, 6:30-7:30 PM ELLT 116 All students with exam conflicts or needing accommodations MUST see Chell Nyquist ( [email protected] ) Upcoming Postings on Blackboard: Learning Objectives Past Exams and Solutions Assigned Seating for the exam

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Histogram Bins Revisited Determine an appropriate set of bins for the information dataset described below and list the complete set of bins. Show all your calculations and assumptions. Mean: 25; Median: 24 Standard deviation: 3 Minimum: 20; Maximum: 30 # of data points: 140
Learning Objectives: Function Discovery At the end of this topic, you will be able to: Identify function types from graphs of data Linear, power, exponential, or logarithmic Linearize and plot data appropriately Plot data on linear and log scaled graphs Linearize a power, exponential, and logarithmic equation Use Excel and MATLAB to perform functional “discovery” Notes pp. 85-92

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Data Transformations data equation graph words regression interpreting speculating evaluation plotting reading function discovery sketch
Data Transformations data equation graph

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Plotting Guidelines Identify the independent variables vs. dependent variables Which set of data belongs on the x-axis? Why? If time is involved, it is almost always the independent variable Which set of data belongs on the y-axis? Why? What is measured? That’s the dependent variable. data graph
Plotting Guidelines Identify the plot type: XY scatter versus bar or line chart Want XY scatter since x-axis data is not categorical Technical presentation quality Create clear axis labels with units Create a clear plot title with unique conditions Generally, do use data markers for raw data data graph

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Plotting Data Sets P1 = (x1, y1), P2 = (x2, y2), ... , Pn = (xn, yn) Data may provide information on: Spatial profile (e.g., temperature versus distance along a piece of wire ) Cause-and effect relationships (e.g., distance versus
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