Regression Analysis Project Instructions
Regression Analysis Project Instructions Objective: This project aims to apply regression analysis to a dataset of your choice to uncover relationships between variables and predict outcomes. By selecting a dataset that aligns with your interests or past work, you’ll demonstrate your ability to conduct a meaningful analysis using Excel as your primary tool. Dataset Selection: • • Scope: Choose a dataset that contains one dependent variable (the outcome you want to predict) and one or more independent variables (factors you suspect influence the outcome). Source: The dataset can come from various sources, such as public datasets (e.g., government databases, research institutions, Internet), or you may collect your own, ensuring it’s suitable for regression analysis. Project Steps: 1. Data Preparation: • Clean your data: Ensure your dataset is free of errors, duplicates, and missing values that could distort your analysis. • Format your data: Organize your data in Excel, with rows representing observations and columns for variables. 2. Exploratory Data Analysis (EDA): • Visualize your data: Use Excel’s charting features to explore relationships between variables. Scatter plots can be particularly insightful for regression analysis. • Summarize your data: Utilize Excel functions to calculate descriptive statistics that summarize the central tendency, dispersion, and shape of your dataset’s distribution. 3. Regression Analysis: a. Perform Regression Analysis. • Use the Data Analysis Toolpak in Excel to run your regression. • Select your independent (X) and dependent (Y) variables appropriately. b. Interpretation of Key Outputs: • Coefficients: Indicate the expected change in the dependent variable for a one-unit change in the independent variable. • R-squared (R²): Reflects the percentage of the dependent variable variation that the model explains. • Standard Error: Measures the accuracy of predictions. • F-test P-value: Tests the model’s overall significance. • P-values of Coefficients: Determine the significance of individual predictors. c. Residual Analysis: • Conduct a residual analysis to check the assumptions of your regression model. Report Writing: • • • • Introduction: Briefly introduce your dataset and the objectives of your analysis. Methodology: Describe how you prepared the data and the steps taken in the Excel analysis. Results and Interpretation: Present the key findings from your regression analysis, including the interpretation of coefficients, R² value, and the significance of the model based on the F-test and p-values. Discuss the results of your residual analysis. Conclusion: Summarize the implications of your findings, limitations of the analysis, and suggestions for further research. Submission Guidelines: • • • • • Submit a report detailing your analysis and findings, including copies of the Excel outputs to substantiate your interpretations. Ensure your report is structured according to the sections outlined above. Since this is group work, ensuring that everyone contributes to this work is essential. Each group must consist of at least 2 and no more than 4 students. Deadline is May 16th.
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