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Mathematics and Statistics Theses and Dissertations
Theses/dissertations from 2024 2024.
The Effect of Fixed Time Delays on the Synchronization Phase Transition , Shaizat Bakhytzhan
Optimal Selection of Good Polynomials and Constructions of Locally Recoverable Codes via Galois Theory , Austin Dukes
Improvements in Computational Techniques For Determining Ideal Class Groups and Class Numbers , Muhammed Rashad Erukulangara
On the Subelliptic and Subparabolic Infinity Laplacian in Grushin-Type Spaces , Zachary Forrest
Utilizing Machine Learning Techniques for Accurate Diagnosis of Breast Cancer and Comprehensive Statistical Analysis of Clinical Data , Myat Ei Ei Phyo
Quandle Rings, Idempotents and Cocycle Invariants of Knots , Dipali Swain
Comparative Analysis of Time Series Models on U.S. Stock and Exchange Rates: Bayesian Estimation of Time Series Error Term Model Versus Machine Learning Approaches , Young Keun Yang
Theses/Dissertations from 2023 2023
Statistical Analysis of Ribonucleotide Incorporation in Human Cells , Tejasvi Channagiri
Matrix Models of 2D Critical Phenomena , Nathan Hayford
Data-Driven Learning Algorithm Via Densely-Defined Multiplication Operators and Occupation Kernels. , John Kyei
Classification of Finite Topological Quandles and Shelves via Posets , Hitakshi Lahrani
Applied Analysis for Learning Architectures , Himanshu Singh
Rational Functions of Degree Five That Permute the Projective Line Over a Finite Field , Christopher Sze
Recovering generators of principal ideals using subfield structure and applications to cryptography , William Youmans
Theses/Dissertations from 2022 2022
Application of the Riemann-Hilbert method to soliton solutions of a nonlocal reverse-spacetime Sasa-Satsuma equation and a higher-order reverse-time NLS-type equation , Ahmed Ahmed
New Developments in Statistical Optimal Designs for Physical and Computer Experiments , Damola M. Akinlana
Advances and Applications of Optimal Polynomial Approximants , Raymond Centner
Data-Driven Analytical Predictive Modeling for Pancreatic Cancer, Financial & Social Systems , Aditya Chakraborty
On Simultaneous Similarity of d-tuples of Commuting Square Matrices , Corey Connelly
Methods in Discrete Mathematics to Study DNA Rearrangement Processes , Lina Fajardo Gómez
Symbolic Computation of Lump Solutions to a Combined (2+1)-dimensional Nonlinear Evolution Equation , Jingwei He
Adversarial and Data Poisoning Attacks against Deep Learning , Jing Lin
Exploring the Vulnerability of A Neural Tangent Generalization Attack (NTGA) - Generated Unlearnable CIFAR-10 Dataset , Gitte Ost
Statistical Methods for Reliability Test planning and Data Analysis , Oluwaseun Elizabeth Otunuga
Boundary behavior of analytic functions and Approximation Theory , Spyros Pasias
Effective Statistical and Machine Learning Methods to Analyze Children's Vocabulary Learning , Houston T. Sanders
Stability Analysis of Delay-Driven Coupled Cantilevers Using the Lambert W-Function , Daniel Siebel-Cortopassi
A Functional Optimization Approach to Stochastic Process Sampling , Ryan Matthew Thurman
Theses/Dissertations from 2021 2021
Riemann-Hilbert Problems for Nonlocal Reverse-Time Nonlinear Second-order and Fourth-order AKNS Systems of Multiple Components and Exact Soliton Solutions , Alle Adjiri
Zeros of Harmonic Polynomials and Related Applications , Azizah Alrajhi
Combination of Time Series Analysis and Sentiment Analysis for Stock Market Forecasting , Hsiao-Chuan Chou
Uncertainty Quantification in Deep and Statistical Learning with applications in Bio-Medical Image Analysis , K. Ruwani M. Fernando
Data-Driven Analytical Modeling of Multiple Myeloma Cancer, U.S. Crop Production and Monitoring Process , Lohuwa Mamudu
Long-time Asymptotics for mKdV Type Reduced Equations of the AKNS Hierarchy in Weighted L 2 Sobolev Spaces , Fudong Wang
Online and Adjusted Human Activities Recognition with Statistical Learning , Yanjia Zhang
Theses/Dissertations from 2020 2020
Bayesian Reliability Analysis of The Power Law Process and Statistical Modeling of Computer and Network Vulnerabilities with Cybersecurity Application , Freeh N. Alenezi
Discrete Models and Algorithms for Analyzing DNA Rearrangements , Jasper Braun
Bayesian Reliability Analysis for Optical Media Using Accelerated Degradation Test Data , Kun Bu
On the p(x)-Laplace equation in Carnot groups , Robert D. Freeman
Clustering methods for gene expression data of Oxytricha trifallax , Kyle Houfek
Gradient Boosting for Survival Analysis with Applications in Oncology , Nam Phuong Nguyen
Global and Stochastic Dynamics of Diffusive Hindmarsh-Rose Equations in Neurodynamics , Chi Phan
Restricted Isometric Projections for Differentiable Manifolds and Applications , Vasile Pop
On Some Problems on Polynomial Interpolation in Several Variables , Brian Jon Tuesink
Numerical Study of Gap Distributions in Determinantal Point Process on Low Dimensional Spheres: L -Ensemble of O ( n ) Model Type for n = 2 and n = 3 , Xiankui Yang
Non-Associative Algebraic Structures in Knot Theory , Emanuele Zappala
Theses/Dissertations from 2019 2019
Field Quantization for Radiative Decay of Plasmons in Finite and Infinite Geometries , Maryam Bagherian
Probabilistic Modeling of Democracy, Corruption, Hemophilia A and Prediabetes Data , A. K. M. Raquibul Bashar
Generalized Derivations of Ternary Lie Algebras and n-BiHom-Lie Algebras , Amine Ben Abdeljelil
Fractional Random Weighted Bootstrapping for Classification on Imbalanced Data with Ensemble Decision Tree Methods , Sean Charles Carter
Hierarchical Self-Assembly and Substitution Rules , Daniel Alejandro Cruz
Statistical Learning of Biomedical Non-Stationary Signals and Quality of Life Modeling , Mahdi Goudarzi
Probabilistic and Statistical Prediction Models for Alzheimer’s Disease and Statistical Analysis of Global Warming , Maryam Ibrahim Habadi
Essays on Time Series and Machine Learning Techniques for Risk Management , Michael Kotarinos
The Systems of Post and Post Algebras: A Demonstration of an Obvious Fact , Daviel Leyva
Reconstruction of Radar Images by Using Spherical Mean and Regular Radon Transforms , Ozan Pirbudak
Analyses of Unorthodox Overlapping Gene Segments in Oxytricha Trifallax , Shannon Stich
An Optimal Medium-Strength Regularity Algorithm for 3-uniform Hypergraphs , John Theado
Power Graphs of Quasigroups , DayVon L. Walker
Theses/Dissertations from 2018 2018
Groups Generated by Automata Arising from Transformations of the Boundaries of Rooted Trees , Elsayed Ahmed
Non-equilibrium Phase Transitions in Interacting Diffusions , Wael Al-Sawai
A Hybrid Dynamic Modeling of Time-to-event Processes and Applications , Emmanuel A. Appiah
Lump Solutions and Riemann-Hilbert Approach to Soliton Equations , Sumayah A. Batwa
Developing a Model to Predict Prevalence of Compulsive Behavior in Individuals with OCD , Lindsay D. Fields
Generalizations of Quandles and their cohomologies , Matthew J. Green
Hamiltonian structures and Riemann-Hilbert problems of integrable systems , Xiang Gu
Optimal Latin Hypercube Designs for Computer Experiments Based on Multiple Objectives , Ruizhe Hou
Human Activity Recognition Based on Transfer Learning , Jinyong Pang
Signal Detection of Adverse Drug Reaction using the Adverse Event Reporting System: Literature Review and Novel Methods , Minh H. Pham
Statistical Analysis and Modeling of Cyber Security and Health Sciences , Nawa Raj Pokhrel
Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems , Zheni Svetoslavova Stefanova
Orthogonal Polynomials With Respect to the Measure Supported Over the Whole Complex Plane , Meng Yang
Theses/Dissertations from 2017 2017
Modeling in Finance and Insurance With Levy-It'o Driven Dynamic Processes under Semi Markov-type Switching Regimes and Time Domains , Patrick Armand Assonken Tonfack
Prevalence of Typical Images in High School Geometry Textbooks , Megan N. Cannon
On Extending Hansel's Theorem to Hypergraphs , Gregory Sutton Churchill
Contributions to Quandle Theory: A Study of f-Quandles, Extensions, and Cohomology , Indu Rasika U. Churchill
Linear Extremal Problems in the Hardy Space H p for 0 p , Robert Christopher Connelly
Statistical Analysis and Modeling of Ovarian and Breast Cancer , Muditha V. Devamitta Perera
Statistical Analysis and Modeling of Stomach Cancer Data , Chao Gao
Structural Analysis of Poloidal and Toroidal Plasmons and Fields of Multilayer Nanorings , Kumar Vijay Garapati
Dynamics of Multicultural Social Networks , Kristina B. Hilton
Cybersecurity: Stochastic Analysis and Modelling of Vulnerabilities to Determine the Network Security and Attackers Behavior , Pubudu Kalpani Kaluarachchi
Generalized D-Kaup-Newell integrable systems and their integrable couplings and Darboux transformations , Morgan Ashley McAnally
Patterns in Words Related to DNA Rearrangements , Lukas Nabergall
Time Series Online Empirical Bayesian Kernel Density Segmentation: Applications in Real Time Activity Recognition Using Smartphone Accelerometer , Shuang Na
Schreier Graphs of Thompson's Group T , Allen Pennington
Cybersecurity: Probabilistic Behavior of Vulnerability and Life Cycle , Sasith Maduranga Rajasooriya
Bayesian Artificial Neural Networks in Health and Cybersecurity , Hansapani Sarasepa Rodrigo
Real-time Classification of Biomedical Signals, Parkinson’s Analytical Model , Abolfazl Saghafi
Lump, complexiton and algebro-geometric solutions to soliton equations , Yuan Zhou
Theses/Dissertations from 2016 2016
A Statistical Analysis of Hurricanes in the Atlantic Basin and Sinkholes in Florida , Joy Marie D'andrea
Statistical Analysis of a Risk Factor in Finance and Environmental Models for Belize , Sherlene Enriquez-Savery
Putnam's Inequality and Analytic Content in the Bergman Space , Matthew Fleeman
On the Number of Colors in Quandle Knot Colorings , Jeremy William Kerr
Statistical Modeling of Carbon Dioxide and Cluster Analysis of Time Dependent Information: Lag Target Time Series Clustering, Multi-Factor Time Series Clustering, and Multi-Level Time Series Clustering , Doo Young Kim
Some Results Concerning Permutation Polynomials over Finite Fields , Stephen Lappano
Hamiltonian Formulations and Symmetry Constraints of Soliton Hierarchies of (1+1)-Dimensional Nonlinear Evolution Equations , Solomon Manukure
Modeling and Survival Analysis of Breast Cancer: A Statistical, Artificial Neural Network, and Decision Tree Approach , Venkateswara Rao Mudunuru
Generalized Phase Retrieval: Isometries in Vector Spaces , Josiah Park
Leonard Systems and their Friends , Jonathan Spiewak
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Top 3 Techniques in Thesis Statistical Analysis for PhD
Starting your journey into thesis statistical analysis for a PhD might feel a bit overwhelming, but fear not! We're here to guide you through the basics in simple terms. This blog is your go-to beginner's guide for understanding dissertation statistics and thesis statistics. We'll break down the top 3 techniques you need to know to ace your statistical analysis for that PhD journey.
Thesis data analysis and interpretation for PhD involves applying statistical methods to interpret and draw meaningful conclusions from research data. It plays a vital role in validating hypotheses, making informed decisions, and ensuring the robustness of a doctoral thesis, contributing to the overall quality and credibility of the research.
No need for complicated jargon – we'll make it easy to grasp with thesis data analysis examples. Whether you're figuring out data interpretation, testing hypotheses, or selecting the right statistical tools, we've got your back. Join us as we unravel the mysteries, making the statistical side of your research more manageable and less intimidating. Let's dive in and make your thesis statistical analysis a breeze!
How to Conduct Thesis Data Analysis and Interpretation
1. Understand Your Data:
- Familiarize yourself with the dataset, its variables, and its structure.
- Identify outliers, missing values, and patterns.
2. Choose Appropriate Methods:
- Select statistical techniques aligned with your research questions.
- Utilize descriptive statistics, inferential statistics, or other relevant methods.
3. Conduct Thesis Data Analysis:
- Apply chosen methods to the dataset.
- Present results through tables, graphs, or charts.
4. Interpret Findings:
- Analyze the implications of your results.
- Relate findings to your research questions and hypothesis.
Now let us dive into the top 3 techniques in thesis statistical analysis for PhD which can jumpstart your research.
# Descriptive Statistics
i) Summarizes Data: Descriptive statistics, such as mean, median, and mode, offer a concise overview of central tendencies, providing a snapshot of the dataset's key features.
ii) Identifies Patterns: Through measures like standard deviation and range, it highlights the dispersion of data points, aiding in the recognition of patterns or variations within the dataset.
iii) Informs Initial Understanding: In a thesis data analysis example, descriptive statistics play a crucial role in the initial stages of thesis data analysis and interpretation. They help researchers grasp the fundamental characteristics of their data, setting the foundation for more in-depth analysis.
iv) Facilitates Data Presentation: Results from descriptive statistics can be effectively presented through tables, graphs, or charts, enhancing the visual representation of complex data sets in the context of thesis data analysis for PhD research.
v) Guides Further Analysis: Insights gained from descriptive statistics guide researchers in formulating hypotheses and determining the appropriate statistical techniques for subsequent stages of analysis.
# Hypothesis Testing
i) Validates Research Hypotheses:
- Hypothesis testing is instrumental in confirming or refuting research hypotheses established during the initial stages of the PhD research process.
ii) Ensures Statistical Significance:
- It provides a systematic approach to assess the statistical significance of relationships within the data, validating the reliability and credibility of research findings.
iii) Guides Research Methodology Consultation:
- Hypothesis testing outcomes contribute to the refinement of the overall research methodology, ensuring that the chosen methods align with the study's objectives and provide meaningful insights.
iv) Informs Decision-Making:
- Results from hypothesis testing guide researchers in making informed decisions about the acceptance or rejection of specific hypotheses, influencing the overall direction of the research.
v) Enhances Survey Questionnaire Design Service:
Findings from hypothesis testing contribute valuable insights for improving survey questionnaire services. It helps identify key variables to measure and informs the development of survey questions that align with the study's objectives.
# Regression Analysis
i) Identifies Relationships Between Variables:
- Regression analysis allows researchers to explore and quantify the relationships between variables, providing insights into the nature and strength of these connections.
ii) Predicts Outcomes:
- It enables the prediction of outcomes based on the values of independent variables, adding a predictive dimension to the analysis in PhD thesis research.
iii) Informs Qualitative Research Data Analysis Help for Thesis:
- Insights gained from regression analysis complement qualitative research data analysis help for the thesis. It assists in understanding the quantitative aspects of data, offering a comprehensive perspective for a more holistic analysis.
iv) Enhances Results Chapter Writing Service for Dissertation:
- Results obtained through regression analysis contribute valuable information for the results chapter writing service for the dissertation. The interpretation of these results adds depth and context to the findings, making the results chapter more comprehensive.
v) Supports Results with Interpretation Report:
- Regression analysis results are essential for constructing a results chapter with an interpretation report. The statistical findings help in drawing meaningful conclusions and presenting results in a format that is accessible to a broad audience.
Final Thoughts
To sum it up, delving into the top 3 techniques for thesis statistical analysis for PhD is like having a powerful toolkit for your research adventure. We've learned that descriptive stats give us a quick peek at the data, hypothesis testing helps us decide what's truly important, and regression analysis lets us predict outcomes.
Just like in our thesis data analysis example, these methods are the storytellers of your research. They aren't just about crunching numbers; they help you narrate a compelling tale with your data. So, whether you're just starting out or refining your skills, these techniques are your pals for a strong and credible PhD journey. Cheers to making sense of the numbers and telling your research story well!
Oliver Statistics is a research consultancy firm based in Malaysia that provides comprehensive research services to support doctoral candidates in their academic pursuits. Their team of expert statisticians and academic professionals assists in developing robust research methodologies and utilizes top-notch software for effective data analysis .
They offer a range of services, including research guidance, data analysis, and statistical consulting, to help PhD researchers in various subject domains. Oliver Statistics has created research tools and offers support services for data analytics to academics and PhD candidates worldwide. They have a team of elite academicians and expert technical researchers with subject matter experts from a variety of fields to assist PhD candidates.
1. Why statistical analysis is important in research?
Ans. Statistical analysis is crucial in research to uncover patterns, and relationships, and draw meaningful conclusions from data.
2. What is SPSS in research methodology?
Ans. SPSS in research methodology is a statistical software used for data analysis and interpretation.
3. Is it necessary to use SPSS for data analysis?
Ans. The use of SPSS for data analysis is not mandatory, but it offers a powerful tool for researchers due to its versatility and statistical capabilities.
4. How do you present data analysis in a thesis?
Ans. Present data analysis in a thesis by utilizing clear visuals, such as tables and graphs, and providing a narrative that explains key findings and their significance.
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