I can help you conduct Multiple Linear Regression and Reporting
- 4.3
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Project Details
Why Hire Me?
With over 7 years of experience in regression modeling across academic and industry projects, I deliver precise results and actionable interpretations.
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Proficient in R, Python, SPSS, and Excel for regression model building and diagnostics
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Skilled in working with both quantitative and categorical predictors
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Apply rigorous assumption testing, multicollinearity checks, and outlier treatment
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Deliver easy-to-understand interpretations with strong visual support
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Experience with sector-specific applications in healthcare, marketing, HR, and more
What I Need to Start Your Work
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Project Details
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Clear project aim and your objective for using multiple linear regression
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Specific research questions or hypotheses being tested
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Data and Data Description
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Dataset in
.csv
,.xlsx
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Description of variables and any preprocessing already performed
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Model Requirements
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Predictors to include (quantitative, dummy, interaction terms)
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Any assumptions or diagnostics you want prioritized (e.g., VIF, residuals)
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Reporting Expectations
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Output format (Word, PDF, script + output)
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Inclusion of plots, interpretations, and statistical outputs
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Communication Preferences
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Preferred communication method (email, call, etc.)
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Deadlines and check-in preferences
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Portfolio

Predicting Housing Prices Using Multiple Linear Regression in R
Built a robust regression model to estimate property prices based on square footage, location, number of bedrooms, and age of the property. The insights helped the client in Boston real estate set optimal listing prices and identify undervalued neighborhoods.

Analyzing Employee Salary Drivers with Dummy and Interaction Variables in SPSS
Conducted a multiple linear regression using gender, education, department, and experience as predictors. Included dummy and interaction variables to uncover nuanced pay disparities across departments, driving HR’s data-backed pay equity strategy.

Revenue Forecasting Model for a Subscription-Based Business Using Python
Implemented a multiple regression model to forecast monthly revenue using metrics like active users, average transaction value, and churn rate. Enabled the finance team to improve budgeting accuracy and plan promotional campaigns more strategically.
Process

Customer Reviews
5 reviews for this Gig ★★★★☆ 4.3
i submitted the report to my supervisor and she was happy with the quality the variables and model diagnostics were clearly reported
i had missing values and some weird outliers he cleaned the data and did a proper regression with full explanation i understood everything he did
the charts and interpretation of results were very helpful especially for my presentation overall a clear and detailed report
I needed help with dummy variables and interaction terms for my thesis he handled it smoothly and even corrected some mistakes in my initial model
explained my regression output really well and helped me figure out which variables actually mattered in my dataset thanks for making it simple