Kunal Khatri
PhD Statistician | Data Analysis & Research Expert
Ready for work
Kunal is ready for their next project!
🧠 Title: Statistical Analysis of 325 Survey Responses for Research Study
📌 Problem: The client required a complete statistical analysis of survey data (325 respondents) to examine relationships between multiple factors such as organizational support, job involvement, and employee engagement.
They needed:
1. Correct statistical test selection
2. Accurate analysis
3. Clear interpretation for research reporting
⚙️ Approach: I followed a structured analytical process:
1. Data cleaning and preprocessing
2. Reliability testing (Cronbach’s Alpha)
3. Descriptive statistics
4. Correlation analysis
5. Regression analysis
6. Hypothesis testing using SPSS
🛠 Tools Used: SPSS, Excel, R, Python
📊 Results
1. Identified significant relationships between key variables
2. Established impact of organizational support on engagement
3. Generated clear statistical outputs with interpretation
🎯 Outcome: The client successfully completed their research project with strong statistical backing and well-structured results for their report/dissertation.
💡 Key Value Delivered
1. Accurate and reliable statistical analysis
2. Easy-to-understand interpretation
3. Research-ready outputs
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🚀 Available for Statistical Analysis & Research Projects
I’m a PhD-qualified Statistician with 14+ years of experience in data analysis and academic research.
I help clients with:
• Survey data analysis (SPSS, R, Python)
• Hypothesis testing, regression, factor analysis
• Thesis, dissertation & research support
• Data visualization & interpretation
I focus on delivering accurate results, clear insights, and professional reports.
📍 Time Zone: IST (UTC +5:30)
🌍 Available for global clients
If you’re working on a research or data-driven project, feel free to connect!
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This project involved statistical revision and analysis of a Social and Environmental Science survey dataset containing responses from 325 participants. The aim was to examine awareness levels, environmental attitudes, community participation, and socio-demographic influences on sustainable behavior.
Advanced statistical techniques were used to clean the data, validate responses, generate meaningful insights, and prepare publication-quality outputs including tables, graphs, interpretations, and discussion sections.
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This HR analytics project focuses on analyzing workforce data to uncover insights into employee attrition, satisfaction levels, income distribution, and key correlations among HR metrics. The analysis aims to help HR departments make informed decisions for retention and workforce planning.
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This customer analytics project focuses on understanding customer behavior, purchasing patterns, and retention factors. The dataset includes transaction history, demographic attributes, and engagement metrics. Using R-Studio and SAS Studio, advanced statistical analysis and predictive modeling techniques were applied to enhance customer segmentation, identify churn risk, and optimize marketing strategies.
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Medical Health Data Analysis Report (Statistical and Predictive)
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