Publications by Roel Ceballos
Stat 312 Module 2 Lesson 6 : Lists
Introduction In contrast to a vector, in which all elements must be of the same mode, R’s list structure can combine objects of different types. For those familiar with Python, an R list is similar to a Python dictionary or, for that matter, a Perl hash. C programmers may find it similar to a C struct. The list plays a central role in R, formi...
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Stat 312 Module 2 Lesson 5 : Matrices and Arrays
Introduction A matrix is a vector with two additional attributes: the number of rows and the number of columns. Since matrices are vectors, they also have modes, such as numeric and character. (On the other hand, vectors are not onecolumn or one-row matrices.) Matrices are special cases of a more general R type of object: arrays.Arrays can be mu...
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Module 5 Lesson 12 :R Programming Structures
Introduction R is a block-structured language in the manner of the ALGOL-descendant family, such as C, C++, Python, Perl, and so on. As you’ve seen, blocks are delineated by braces, though braces are optional if the block consists of just a single statement. Statements are separated by newline characters or, optionally, by semicolons. Control...
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Math 204 Foundations of Computer Programming
Environment and Scope Issues A function—formally referred to as a closure in the R documentation—consists not only of its arguments and body but also of its environment. The latter is made up of the collection of objects present at the time the function is created. An understanding of how environments work in R is essential for writing effec...
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Stat 312 Module 4 Lesson 13 Statistical Inference in R
Learning Objectives: At the end of the session, the participants are expected to: understand the basic concepts of statistical inference learn how to use the R functions in performing basic hypothesis testing Introduction Statistical inference is the process through which inferences about a population are made based on certain statistics calcu...
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Stat 312 Module 4 Lesson 14 Regression Analysis in R
Learning Objectives: At the end of the session, the participants are expected to: understand the basic concepts of simple and multiple linear regression learn how to use the R functions in regression analysis Simple Linear Regression What is a linear regression model? Regression Analysis is a statistical modeling tool that is used to explain a...
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Stat 312 Module 4 Lesson 12 Descriptive Statistics in R
Learning Objectives: At the end of the session, the participants are expected to: understand the basic concepts and use of descriptive statistics learn how to use the basic functions in R for generating descriptive measures for central tendency, variability, and frequency tables. Introduction “Statistical Thinking will one day be as necessar...
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Stat 312 Module 3 Lesson 11 : Math and Simulations in R
Math Functions R includes an extensive set of built-in math functions. Here is a partial list: exp(): exponential function, base e log(): natural logarithmic function log10(): Logarithm base 10 sqrt(): Square root abs(): Absolute value sin(), cos(), and so on : Trigonometric functions min(), max(): MInimum value and maximum value within a vector...
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Stat 312 Module 4 Lesson 15: Regression Diagnostics in R
Learning Objectives: At the end of the session, the students are expected to: Perform the different regression diagnostics in R Learn how to use appropriate criteria in choosing for the best model. Regression Diagnostics In the previous part, we learned how to do ordinary linear regression with R. Without verifying that the data have met the as...
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Stat 312 Module 3 Lesson 9 : R Programming Structures 2
Environment and Scope Issues A function—formally referred to as a closure in the R documentation—consists not only of its arguments and body but also of its environment. The latter is made up of the collection of objects present at the time the function is created. An understanding of how environments work in R is essential for writing effec...
33931 sym R (10870 sym/109 pcs)