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Welcome to Probability and Statistics for Engineers
This course is about probability and how it is measured: probability, statistics, reliability and decision with applications in engineering. Probability of events, discrete and continuous random variables, probability density functions and distributions, estimation, regression and correlation techniques, risk and reliability concepts.
Applied Statistics for Civil and Environmental Engineers by 
Probability, Statistics, and Decision for Civil Engineers by In order to get the most out of Ebsco e-textbooks you need to create an account in Ebsco.
To create an Ebsco account start at the library home page, then click the Advanced Search link under the main search box, (the tab is named Article Quick Search).
This takes you to a page with a sign in tab at the upper left.
From here, follow the prompts.
For instance the ebook does not offer highlight functionality unless faculty and students sign in. You can create and save notes by logging into your EBSCOhost folder.
Open the eBook in the eBook Full Text viewer.
Go to the first page of the section of pages you would like to print and click the Print Pages in the tools menu above.
The Print Pages to PDF menu is displayed.
Select to include in the PDF:
Click Print PDF to open your browser's Print dialog box.
Configure your print settings and click OK to print your pages.
Successful completion of this course will enable you to:
apply spreadsheet programs to manipulate and analyze data
o apply formulae, use absolute relative & addressing, plot scatter and bar graphs
apply basic statistical analysis tools to data:
o calculate mean, median, standard deviation, plot histograms, etc
apply basic probability theory (independence, conditional & joint probabilities)
apply Bayes’ theorem for posterior probabilities as applied to engineering and test design
apply basic probability & reliability theory to different engineering situations
apply basic decision theory to assess the expected value of a project
explain and use the basic forms & properties of probability mass & density functions (PMF,
PDF) and cumulative distribution functions (CDF)
explain and apply specific discrete probability functions such as Bernoulli, Binomial,
Geometric
explain and apply specific continuous probability functions such as Normal, lognormal,
Student t, Chi-squared
analyze data to assess the parameters needed for each probability function
utilize appropriate probability functions to estimate probabilities of occurrence for specific
engineering cases
estimate the confidence intervals of the predictions made using probability functions
determine best-fit linear functions for (x,y) data using least-squares regression techniques
