Advanced Statistical AnalysisJosip Novak
This service involves high-level statistical analysis, incorporating rigorous best practices and advanced multivariate techniques that are often beyond the reach of many analysts and researchers. What makes me unique is my ability to blend advanced statistical skills and expertise in psychometrics with domain expertise in psychology, enabling me to provide nuanced insights across various domains, particularly in understanding human behavior.
What's included
Report (.html, .docx, etc.)
A comprehensive, structured report detailing the statistical analysis process. It includes:
1. Problem Definition – A clear statement of the business or research problem. This section includes the objectives of the analysis and the key variables involved.
2. Data Preparation – Overview of the initial data quality assessment, including any cleaning, transformation, or normalization steps taken. This section also includes a description of any data preprocessing methods, such as handling missing values or outliers.
3. Methodology – Overview of the statistical techniques employed (e.g., regression analysis, hierarchical linear modeling, latent variable modeling), including the rationale for choosing these methods. Also, an explanation of the statistical techniques used to assess model fit (e.g., AIC, BIC, RMSE).
4. Results & Interpretation – Detailed presentation of the analysis outcomes, including point estimates, confidence intervals, effect sizes, and any significant relationships identified.
5. Summary of Insights – A concise overview of the most important findings, including any actionable insights uncovered from the statistical analysis. This section will also highlight potential risks or opportunities based on the results.
The Prepared Dataset (.csv, .xlsx, etc.) (Optional)
If required, a cleaned and pre-processed version of the dataset will be delivered alongside the report. This dataset will be formatted for easy use and further analysis, including:
1. Data Cleaning – Any issues such as missing values, duplicates, or outliers will have been addressed to ensure the dataset is tidy.
2. Normalization & Transformation – If necessary, the variables will be scaled, normalized, or transformed to ensure consistency and compatibility with specific techniques.
3. Feature Engineering – Relevant new features/variables (if applicable) will be created to enhance the dataset’s usability for mining.
4. Format & Structure – The dataset will be provided in a clean, structured format (e.g., .csv, .xlsx) with clear labeling of variables and standardized data types for ease of use.
Actionable Recommendations (Optional)
A focused section that translates key findings from the statistical analysis into practical implications. It includes:
1. Strategic Recommendations – Data-driven suggestions on how to leverage insights for optimization, problem-solving, or future planning.
2. Potential Risks & Considerations – A discussion of any limitations, uncertainties, or risks associated with the findings and how they might be mitigated.
3. Implementation – Suggested next steps tailored to your specific context to help integrate insights into actionable plans.
FAQs
Advanced statistical analysis is widely used for:
* Risk assessment - Evaluating financial risks or quantifying uncertainties in market conditions, allowing businesses to make informed decisions.
* Customer behavior analysis - Finding strongest predictors of purchasing choices.
* Pricing strategies - Analyzing factors that affect pricing models to optimize pricing strategy.
* Disease risk identification - Analyzing various factors (genetic, environmental, and behavioral) to identify which are most associated with disease risks and health outcomes.
* Treatment effectiveness: Evaluating the effectiveness of medical treatments by analyzing patient outcomes in relation to various intervention strategies.
* Intervention impact measurement - Assessing the effects of social or psychological interventions, measuring changes in behavior or mental health using statistical techniques that isolate the impact of specific treatments or policies.
* Quality control: Ensuring the consistency and reliability of production processes by analyzing variability and identifying sources of defects or inefficiencies
* Supply chain analysis - Evaluating the complexities of supply chains, identifying bottlenecks, risks, and inefficiencies, and providing data-driven strategies for more efficient operations.
* Marketing campaign evaluation - Assessing the effectiveness of advertising campaigns, promotions, and other marketing efforts by measuring their impact on customer engagement, brand perception, and sales.
* Environmental impact assessments - Assessing how human activities, like urbanization or industrialization, affect the environment, and predict long-term consequences for ecosystems and species.
* Sustainability modeling - Exploring the relationships between human activities and environmental sustainability, helping to design better policies for resource use, waste management, and energy consumption.
* Policy impact evaluation - Using statistical analysis to assess the effects of public policies, understanding how they influence different groups and whether they achieve the intended outcomes.
* Public opinion analysis - Analyzing the public’s views on political issues, candidates, or policies, identifying trends and shifts over time and understanding the factors influencing public opinion.
Not necessarily. If you already have relevant data, that’s great! However, if you don’t, I can help identify relevant data sources or suggest ways to collect the necessary information.
Yes! If you don’t have the necessary data, I can assist in various ways, including:
* Web Scraping – Collecting publicly available time series data while ensuring compliance with legal and ethical guidelines.
* API Integration – Extracting data from online services, financial markets, social media, or other platforms via APIs.
* Public Databases – Identifying and utilizing open datasets from government sources, research institutions, and industry reports.
* Custom Data Pipelines – Setting up automated processes to continuously collect and structure incoming data.
No worries! As part of the process, I will clean and preprocess your data to handle missing values, outliers, inconsistencies, etc. Techniques like imputation and transformation will be applied to ensure the dataset is suitable for modeling.
I am committed to data ethics and understand the importance of protecting sensitive information. Your data will be used solely for the purpose of completing your requested analysis. It will not be shared with any third parties and will be deleted upon completion of the task.
Not necessarily. If you have specific hypotheses, they can help guide the analysis. However, if you only have general aims or need specific insights without defined hypotheses, that's fine too. Just let me know what you want to achieve or find in the dataset, and I will adjust the analysis accordingly.
For advanced statistical analysis, R is my primary tool due to its comprehensive coverage of various techniques and versatility. However, I am also proficient in other software and can adapt to use the most suitable tool if specific techniques are better supported elsewhere or if you prefer the analysis to be done using other tools.
The advanced statistical tools I use depend on the complexity of your data and the specific problem you're trying to solve. I use a variety of techniques, including:
* Advanced Regression Analysis: Quantile regression, robust regression, partial least squares regression, ridge regression, lasso regression, elastic net regression
* Generalized Linear Models & Extensions: Logistic regression, Poisson regression, negative binomial regression, zero-inflated models, hurdle models, beta regression
* Path Analysis & Mediation/Moderation: Mediated mediation, moderated moderation, nonlinear moderation, moderated path analysis
* Latent Variable Modeling: Latent class analysis, latent profile analysis, exploratory and confirmatory factor analysis, multigroup and multilevel factor analysis, hierarchical factor analysis, full structural equation modeling (SEM), multigroup SEM, multilevel SEM, latent growth curve modeling
* Categorical Data Analysis: Correspondence analysis, multiple correspondence analysis
* Survival Analysis: Cox proportional hazards models, Kaplan-Meier estimation, accelerated failure time models, competing risks analysis
* Mixture Models: Finite mixture models and multilevel mixture models, such as Gaussian mixture models, latent class mixture models, and hierarchical mixture models
The timeline for the project depends on factors such as the data quality, the complexity of the predictive modeling task, and the methods required. Generally, it can take anywhere from a week to a month. A more complex modeling with additional model tuning may take longer.
Example work
Contact for pricing
Tags
IBM SPSS
Jupyter
Python
R
RStudio
Data Analyst
Data Scientist
Statistician
Service provided by
Josip Novak Vukovar, Croatia
- 1
- Followers
Advanced Statistical AnalysisJosip Novak
Contact for pricing
Tags
IBM SPSS
Jupyter
Python
R
RStudio
Data Analyst
Data Scientist
Statistician
This service involves high-level statistical analysis, incorporating rigorous best practices and advanced multivariate techniques that are often beyond the reach of many analysts and researchers. What makes me unique is my ability to blend advanced statistical skills and expertise in psychometrics with domain expertise in psychology, enabling me to provide nuanced insights across various domains, particularly in understanding human behavior.
What's included
Report (.html, .docx, etc.)
A comprehensive, structured report detailing the statistical analysis process. It includes:
1. Problem Definition – A clear statement of the business or research problem. This section includes the objectives of the analysis and the key variables involved.
2. Data Preparation – Overview of the initial data quality assessment, including any cleaning, transformation, or normalization steps taken. This section also includes a description of any data preprocessing methods, such as handling missing values or outliers.
3. Methodology – Overview of the statistical techniques employed (e.g., regression analysis, hierarchical linear modeling, latent variable modeling), including the rationale for choosing these methods. Also, an explanation of the statistical techniques used to assess model fit (e.g., AIC, BIC, RMSE).
4. Results & Interpretation – Detailed presentation of the analysis outcomes, including point estimates, confidence intervals, effect sizes, and any significant relationships identified.
5. Summary of Insights – A concise overview of the most important findings, including any actionable insights uncovered from the statistical analysis. This section will also highlight potential risks or opportunities based on the results.
The Prepared Dataset (.csv, .xlsx, etc.) (Optional)
If required, a cleaned and pre-processed version of the dataset will be delivered alongside the report. This dataset will be formatted for easy use and further analysis, including:
1. Data Cleaning – Any issues such as missing values, duplicates, or outliers will have been addressed to ensure the dataset is tidy.
2. Normalization & Transformation – If necessary, the variables will be scaled, normalized, or transformed to ensure consistency and compatibility with specific techniques.
3. Feature Engineering – Relevant new features/variables (if applicable) will be created to enhance the dataset’s usability for mining.
4. Format & Structure – The dataset will be provided in a clean, structured format (e.g., .csv, .xlsx) with clear labeling of variables and standardized data types for ease of use.
Actionable Recommendations (Optional)
A focused section that translates key findings from the statistical analysis into practical implications. It includes:
1. Strategic Recommendations – Data-driven suggestions on how to leverage insights for optimization, problem-solving, or future planning.
2. Potential Risks & Considerations – A discussion of any limitations, uncertainties, or risks associated with the findings and how they might be mitigated.
3. Implementation – Suggested next steps tailored to your specific context to help integrate insights into actionable plans.
FAQs
Advanced statistical analysis is widely used for:
* Risk assessment - Evaluating financial risks or quantifying uncertainties in market conditions, allowing businesses to make informed decisions.
* Customer behavior analysis - Finding strongest predictors of purchasing choices.
* Pricing strategies - Analyzing factors that affect pricing models to optimize pricing strategy.
* Disease risk identification - Analyzing various factors (genetic, environmental, and behavioral) to identify which are most associated with disease risks and health outcomes.
* Treatment effectiveness: Evaluating the effectiveness of medical treatments by analyzing patient outcomes in relation to various intervention strategies.
* Intervention impact measurement - Assessing the effects of social or psychological interventions, measuring changes in behavior or mental health using statistical techniques that isolate the impact of specific treatments or policies.
* Quality control: Ensuring the consistency and reliability of production processes by analyzing variability and identifying sources of defects or inefficiencies
* Supply chain analysis - Evaluating the complexities of supply chains, identifying bottlenecks, risks, and inefficiencies, and providing data-driven strategies for more efficient operations.
* Marketing campaign evaluation - Assessing the effectiveness of advertising campaigns, promotions, and other marketing efforts by measuring their impact on customer engagement, brand perception, and sales.
* Environmental impact assessments - Assessing how human activities, like urbanization or industrialization, affect the environment, and predict long-term consequences for ecosystems and species.
* Sustainability modeling - Exploring the relationships between human activities and environmental sustainability, helping to design better policies for resource use, waste management, and energy consumption.
* Policy impact evaluation - Using statistical analysis to assess the effects of public policies, understanding how they influence different groups and whether they achieve the intended outcomes.
* Public opinion analysis - Analyzing the public’s views on political issues, candidates, or policies, identifying trends and shifts over time and understanding the factors influencing public opinion.
Not necessarily. If you already have relevant data, that’s great! However, if you don’t, I can help identify relevant data sources or suggest ways to collect the necessary information.
Yes! If you don’t have the necessary data, I can assist in various ways, including:
* Web Scraping – Collecting publicly available time series data while ensuring compliance with legal and ethical guidelines.
* API Integration – Extracting data from online services, financial markets, social media, or other platforms via APIs.
* Public Databases – Identifying and utilizing open datasets from government sources, research institutions, and industry reports.
* Custom Data Pipelines – Setting up automated processes to continuously collect and structure incoming data.
No worries! As part of the process, I will clean and preprocess your data to handle missing values, outliers, inconsistencies, etc. Techniques like imputation and transformation will be applied to ensure the dataset is suitable for modeling.
I am committed to data ethics and understand the importance of protecting sensitive information. Your data will be used solely for the purpose of completing your requested analysis. It will not be shared with any third parties and will be deleted upon completion of the task.
Not necessarily. If you have specific hypotheses, they can help guide the analysis. However, if you only have general aims or need specific insights without defined hypotheses, that's fine too. Just let me know what you want to achieve or find in the dataset, and I will adjust the analysis accordingly.
For advanced statistical analysis, R is my primary tool due to its comprehensive coverage of various techniques and versatility. However, I am also proficient in other software and can adapt to use the most suitable tool if specific techniques are better supported elsewhere or if you prefer the analysis to be done using other tools.
The advanced statistical tools I use depend on the complexity of your data and the specific problem you're trying to solve. I use a variety of techniques, including:
* Advanced Regression Analysis: Quantile regression, robust regression, partial least squares regression, ridge regression, lasso regression, elastic net regression
* Generalized Linear Models & Extensions: Logistic regression, Poisson regression, negative binomial regression, zero-inflated models, hurdle models, beta regression
* Path Analysis & Mediation/Moderation: Mediated mediation, moderated moderation, nonlinear moderation, moderated path analysis
* Latent Variable Modeling: Latent class analysis, latent profile analysis, exploratory and confirmatory factor analysis, multigroup and multilevel factor analysis, hierarchical factor analysis, full structural equation modeling (SEM), multigroup SEM, multilevel SEM, latent growth curve modeling
* Categorical Data Analysis: Correspondence analysis, multiple correspondence analysis
* Survival Analysis: Cox proportional hazards models, Kaplan-Meier estimation, accelerated failure time models, competing risks analysis
* Mixture Models: Finite mixture models and multilevel mixture models, such as Gaussian mixture models, latent class mixture models, and hierarchical mixture models
The timeline for the project depends on factors such as the data quality, the complexity of the predictive modeling task, and the methods required. Generally, it can take anywhere from a week to a month. A more complex modeling with additional model tuning may take longer.
Example work
Contact for pricing