Random Walk model

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#### GM Statistics

#### Algebra Introduction

We work with numbers in arithmetic, while in algebra we use numbers as well as Alphabets such as A, B, C, a, b, and c for any numerical values we choose. We can say that algebra is an extension of arithmetic. For example, the arithmetic sum of two numbers means that the sum of numbers […]

#### Absolute Error of Measurement

Absolute error of a measurement is the difference between the measured value of an object and its true value. When we take the measurement of an object, it is possible that the measured value is either a little more or a little lower than its true value, that is, an absolute error has occurred. For […]

#### Rounding off

In most of the everyday situations, we do not need to use highly sensitive measuring devices (instruments). the accuracy of our measurement depends on the purpose for which we use the information. Example: Suppose someone uses a compass as a guide in going from one end of the school to the other. It would not […]

#### Estimation, Approximating a Precise Value

Estimation is very useful especially when someone wishes to know whether he/ she has arrived at a logical solution to a problem under study. It is useful to learn about how to estimate the total sum of a bill to avoid immediate overpayments. For example, one can estimate the total amount of a shop (supermarket) […]

#### Contingency Table | Cross Classification: Introduction

A bivariate relationship is defined by the joint distribution of the two associated random variables. Contingency Tables Let and are two categorical response variables. Let variable have levels and variable have levels. The possible combinations of classifications for both variables are . The response of a subject randomly chosen from some population has a probability […]

#### R Frequently Asked Questions

#### Namespaces in R Language

In R language, the packages can have namespaces, and currently, all of the base and recommended packages do except the dataset packages. Understanding the use of namespaces is vital if one’s plan to submit a package to CRAN because CRAN requires that the package plays nicely with other submitted packages on CRAN. Namespaces ensure that […]

#### Vectors in R Language

In R Language, a vector is the simplest data structure. A vector in R is also an object that contains elements having the same data type. To create a vector (say ‘x’) of the same type (say double) of elements consisting of five elements one can use c() function. For example, Creating a vector in […]

#### Reading and Writing data in R

Reading and Writing Data in R Reading Data in R For reading (importing) data into R following are some functions. read.table(), and read.csv(), for reading tabular data readLines() for reading lines of a text file source() for reading in R code files (inverse of dump) dget() for reading in R code files (inverse of dput) […]

#### List in R Language

In R language, list is an object that consists of an ordered collection of objects known as its components. A list in R Language is a structured data that can have any number of any modes (types) of other structured data. That is, one can put any kind of object (like vector, data frame, character […]

#### R workspace, object and .RData file

The R program’s structure is similar to the programs written in other computer languages such as C or its successors C++ and Java. However, important differences between these languages and R are (i) R has no header files, (ii) most of the declarations are implicit, (iii) there are no pointers in R, and (iv) text […]

#### mctest: An R package for Detection of Collinearity among Regressors

The problem of multicollinearity plagues the numerical stability of regression estimates. It also causes some serious problem in validation and interpretation of the regression model. Consider the usual multiple linear regression model, , where is an vector of observation on dependent variable, is known design matrix of order , having full-column rank , is vector of […]