Search for notes by fellow students, in your own course and all over the country.
Browse our notes for titles which look like what you need, you can preview any of the notes via a sample of the contents. After you're happy these are the notes you're after simply pop them into your shopping cart.
Title: Computing and Data Analysis for Environmental Applications
Description: This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.
Description: This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.
Document Preview
Extracts from the notes are below, to see the PDF you'll receive please use the links above
1
...
010 Class 3
Probability
Conceptual framework
Probability theory provides a conceptual framework for analyzing
uncertain outcomes of experiments
Definitions
An experiment is defined by:
1
...
A collection of events constructed from these outcomes
3
...
Sample space S is set of all possible experimental outcomes
An event is a set of outcomes
...
Probability P(A) of an event A is a number assigned to the event that
meets the following requirements (probability axioms):
0
1
...
P(S) = 1
3
...
+ AN) = P(A1) + P(A2) +
...
P(A1 + A2) = P(A1) + P(A2) - P(A1 A2)
for A1 A2 not mutually exclusive
2
...
Probability of an event is number of times event is observed
1
divided by total number of repetitions
...
Distributional approach – Probability of an event is derived from a
specified probability distribution (more on this later)
Examples:
Experiment: Three successive coin tosses
Outcomes/elementary events: Different sequences of heads and tails
Sample space: 8 possible outcomes/sequences
Typical event: Set of outcomes that yield 2 heads and 1 tail
Assigning Probabilities (conceptual): P(A) is fraction of total outcomes
in event (3/8)
...
Experiment: Toss of a dart onto a square region of side 2
Outcomes/elementary events: Different locations where dart can land
Sample space: Infinite number of possible locations in square
Typical events: Inscribed circle of radius 1
Assigning Probabilities (conceptual): P(A) is fraction of S covered by
circle (pi/4)
...
Experiment: Selection of a student from a hypothetical infinite population
Outcomes/elementary events: Height of any given student
Sample space: Infinite number of all possible heights
Typical event: Student height is between 5 and 6 feet
Assigning Probabilities (distributional): Assume P(A) is partial area
under a specified histogram between 5 and 6 feet
...
Exercise: Virtual experiments
Write a script that repeats the first two experiments described above (3
coin tosses and 1 dart toss) 20 times
...
Some relevant MATLAB functions: rand, unidrnd, ceil, sum, pi
MATLAB function: virtual
...
8, 2003
3
Title: Computing and Data Analysis for Environmental Applications
Description: This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.
Description: This subject is a computer-oriented introduction to probability and data analysis. It is designed to give students the knowledge and practical experience they need to interpret lab and field data. Basic probability concepts are introduced at the outset because they provide a systematic way to describe uncertainty. They form the basis for the analysis of quantitative data in science and engineering. The MATLAB® programming language is used to perform virtual experiments and to analyze real-world data sets, many downloaded from the web. Programming applications include display and assessment of data sets, investigation of hypotheses, and identification of possible casual relationships between variables. This is the first semester that two courses, Computing and Data Analysis for Environmental Applications (1.017) and Uncertainty in Engineering (1.010), are being jointly offered and taught as a single course.