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"Mahasiswa diharapkan untuk dapat menempuh jenjang pendidikan sarjananya dengan baik dan tentunya selesai tepat waktu. Sebagai mahasiswa, mempunyai aktivitas yang cukup banyak di luar rutinitas kuliah sudah menjadi hal yang lazim, misalnya seperti berorganisasi, berkegiatan di luar kampus, belum lagi ada mahasiswa yang sambil bekerja. Dengan banyaknya rutinitas, mahasiswa seringkali menunda belajar atau menyelesaikan tugas yang diberikan oleh dosennya. Inilah yang disebut dengan prokrastinasi akademik. Prokrastinasi akademik pada mahasiswa dapat berdampak pada penurunan prestasi akademiknya. Tujuan penelitian ini adalah untuk mengetahui variabel-variabel yang menjelaskan tingkat prokrastinasi akademik pada mahasiswa. Metode yang digunakan adalah metode Regresi Linier Berganda, sedangkan untuk mengetahui profil mahasiswa yang mempunyai tingkat prokrastinasi akademik yang tinggi menggunakan metode Classification and Regression Tree (CRT), dan juga ingin mengetahui perbedaan antara Regresi Linier Berganda dan Classification and Regression Tree (CRT) berdasarkan urutan variabel-variabel yang signifikan menjelaskan tingkat prokrastinasi akademik pada mahasiswa FMIPA Universitas Indonesia. Variabel yang diduga menjelaskan tingkat prokrastinasi akademik adalah jenis kelamin, tempat tinggal, kondisi fisik, kondisi psikologis, kondisi lingkungan, motivasi belajar, persepsi mahasiswa, dukungan sosial orang tua, dan dukungan sosial teman sebaya. Penelitian ini menggunakan data primer yaitu 660 mahasiswa FMIPA Universitas Indonesia yang diambil dengan cara purposive sampling. Hasil penelitian menunjukkan bahwa variabel-variabel yang secara signifikan menjelaskan tingkat prokrastinasi akademik mahasiswa FMIPA Universitas Indonesia adalah jenis kelamin, kondisi fisik, kondisi psikologis, motivasi belajar, persepsi mahasiswa, dukungan sosial orang tua, dan dukungan sosial teman sebaya. Profil mahasiswa yang memiliki tingkat prokrastinasi akademik yang tinggi yaitu mahasiswa dengan kondisi fisik dan kondisi psikologis yang buruk, serta dukungan sosial orang tua yang rendah. Selain itu, ada perbedaan dalam urutan variabel-variabel yang signifikan antara metode Regresi Linier Berganda dan CRT, namun keduanya memiliki satu kesamaan yaitu variabel tertinggi adalah kondisi fisik.

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Students are expected to be able to undertake their undergraduate studies satisfactorily and graduate as scheduled. As a students, it is normal having with numerous activities outside academic routine, such as organizations, off-campus activities, not to mention students who are employed. Consequently, students often delay studying and completing the tasks given by their lecturers. This is called academic procrastination. Academic procrastination may lead to a declining academic achievement. This study aimed to determine variables that affect academic procrastination levels and to find out the profile of students with high levels of academic procrastination. The methods used are Multiple Linear Regression and Classification and Regression Tree (CRT), respectively. Furthermore, this study aims to the difference between Multiple Linear Regression and CRT based on the sequence of significant variables explains the level of academic procrastination of FMIPA students of University of Indonesia. The variables considered to affect the level of academic procrastination include gender, residence, physical conditions, psychological conditions, environmental conditions, learning motivation, student perception, parental support, and peer support. This study used primary data, namely 660 FMIPA students of University of Indonesia obtained through purposive sampling. The results showed that the variables that significantly affect the level of academic procrastination of FMIPA students of University of Indonesia include gender, physical conditions, psychological conditions, learning motivation, student perception, parental support, and peer support. Students who demonstrate a high level of academic procrastination are characterized by poor physical and psychological conditions, as well as low parental support. In addition, there is a significant difference in the sequence of variables between the Multiple Linear Regression method and CRT, but both have one thing in common, that is, the highest variable is physical condition."

"This brief provides guidance on how R can be used to facilitate Two-Way ANOVA for data analysis and graphical presentation. Along with instruction on the use of R and R syntax associated with Two-Way ANOVA, this brief will also reinforce the use of descriptive statistics and graphical figures to complement outcomes from parametric Two-Way ANOVA."

"Mathematical statistics with applications, second edition, gives an up-to-date introduction to the theory of statistics with a wealth of real-world applications that will help students approach statistical problem solving in a logical manner. The book introduces many modern statistical computational and simulation concepts that are not covered in other texts; such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. Goodness of fit methods are included to identify the probability distribution that characterizes the probabilistic behavior or a given set of data."

"This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The authors demonstrate how to analyze data--showing code, graphics, and accompanying tabular listings--for all the methods they cover. They emphasize how to construct and interpret graphs. They discuss principles of graphical design. They identify situations where visual impressions from graphs may need confirmation from traditional tabular results. All chapters have exercises.The authors provide and discuss R functions for all the new graphical display formats. All graphs and tabular output in the book were constructed using these functions. Complete R scripts for all examples and figures are provided for readers to use as models for their own analyses.This book can serve as a standalone text for statistics majors at the masters level and for other quantitatively oriented disciplines at the doctoral level, and as a reference book for researchers. In-depth discussions of regression analysis, analysis of variance, and design of experiments are followed by introductions to analysis of discrete bivariate data, nonparametrics, logistic regression, and ARIMA time series modeling. The authors illustrate classical concepts and techniques with a variety of case studies using both newer graphical tools and traditional tabular displays."