Publications by Mustafa Arslan
R Lessons 1
R LESSONS 1 Complete the following using R. Copy and paste your results from the console into a document to submit: Create an object named a with the value 5. a <- 5 Create an object named b with the value 7. b <- 7 Add a to b. a+b ## [1] 12 Divide b by a. b/a ## [1] 1.4 Divide 7 by 5 without first assigning 7 and 5 to objects. 7/5 ## [1]...
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Resampling Methods 3
Introduction Over sampling and under sampling are techniques used in data mining and data analytics to modify unequal data classes to create balanced data sets. Over sampling and under sampling are also known as resampling. When one class of data is the underrepresented minority class in the data sample, over sampling techniques maybe used to du...
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Computational Methods
Introduction Most numerical root-finding methods use iteration, producing a sequence of numbers that hopefully converge towards the root as a limit. In this study, I am going to implement three following root finding methods. The goal of finding root is to find the local minimum or maximum points of the function. Setting zero of the first deriva...
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Generalized Linear Models6
Introduction Poisson logistic regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters. A Poisson ...
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Generalized Linear Models 5
Introduction Poisson logistic regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters. A Poisson ...
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Generalized Linear Models 3
Introduction The multinomial (a.k.a. polytomous) logistic regression model is a simple extension of the binomial logistic regression model. They are used when the dependent variable has more than two nominal categories. Dummy coding of independent variables is quite common. In multinomial logistic regression the dependent variable is dummy coded...
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Generalized Linear Models 2
Introduction In statistics, a generalized linear model (GLM) is a flexible generalization of ordinary linear regression. Generalized Linear Model with binomial random component and canonical link (Logit Link=Log Odds) - is called Logistic Regression. In R, glm function are used for the Logistic regression with following parameters. - formula: pr...
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Categorical Data Analysis 4
Data: These data involve a case-control study with ovarian cancer cases (Cancer = Yes) and non-cancer controls (Cancer = No), with covariates given by OralCont (oral contraceptive use with two levels, Ever or Never) and Smoking (two levels, Yes or No). Objective: The goal of this study is to find the Mutual, Joint and Marginal independences using...
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Categorical Data Analysis 3
Introduction ODDS Ratio: This is the perhaps the most commonly used measure of association. We also use Odds ratio for many log-linar and logistic models. If odds equal to 1, “success” and “failure” are equally likely. If odds > 1, then “success” is more likely than “failure”. If odds < 1, then “success” is less likely than ...
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Probability Simulations 3
Expected Value for Discrete Random Variables If the discrete random variable X can take values x1,x2,x3,…, then the expected value is defined as E(x) = \[\sum_{i=1} x_{i} * P(X=x_{i})\] where xi is the outcome and P(X=xi) is the probability. The expected value or mean of a random variable is the center of its distribution. Com = function(n, x) ...
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