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Write clear, well-structured code by following naming conventions, using comments, and avoiding unnecessary complexity. This ensures your code is readable, maintainable, and less prone to errors.
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Assignment Examples
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This assignment focuses on a comprehensive analysis of cushion diamond data, utilizing a range of linear modeling techniques to explore key relationships within the dataset. Various transformations are applied to enhance model interpretability and accuracy, followed by regression analysis to quantify the influence of independent variables. Diagnostic tests are then conducted to rigorously assess the statistical significance and validity of the models, ensuring that findings are both reliable and meaningful. This systematic approach provides a robust framework for understanding underlying trends and making data-driven conclusions regarding the characteristics of cushion diamonds.
This assignment involves a comprehensive analysis of student marks using linear models in RStudio, focusing on the relationship between participation and assignment scores as predictors of exam performance. Key findings highlight the importance of Assignment 2 in predicting exam marks, issues with model fit, heteroscedasticity, and influential observations affecting regression parameters. These insights contribute to a clearer understanding of academic performance dynamics. For RStudio help, please contact a tutor.
This assignment centers on calculating Customer Lifetime Value (CLV) before and after CEO statements, creating a logistic regression model to predict customer churn, and assessing the model’s performance using key metrics such as sensitivity, false positive rate, and accuracy. These analyses offer valuable insights into customer behavior patterns and the effectiveness of predictive modeling techniques. For RStudio help, please contact a tutor.
This assignment focuses on comprehensive dataset analysis using R, encompassing a variety of statistical tasks designed to provide insights and develop predictive capabilities. The tasks include counting rows to assess dataset scope, calculating the total number of graduates, estimating model parameters to understand key factors, predicting survival probabilities to aid in forecasting, and extracting both p-values and standard deviations to evaluate model reliability across multiple analyses. The final results are systematically recorded and entered into an online form, ensuring accurate data representation. For RStudio help, please contact a tutor.
The assignment focuses on applying econometric techniques to real-world data, specifically using simple linear regression to examine the relationship between Gross National Income (GNI) and electric power consumption. The data is sourced from the World Development Indicators by The World Bank. Key variables include GNI (PPP, current international $) as the dependent variable and electric power consumption (kWh per capita) as the independent variable. The project involves data summarization, model fitting, interpretation of coefficients, evaluation of model fit, hypothesis testing, and confidence interval estimation. For RStudio help, please contact a tutor.