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Real-time Database Sync
Statistical Programming / Data Science / Business Analytics

Statistical Programming with R: Data Analysis, Probability, Regression and Business Decision-Making

This Statistical Programming assessment evaluates students' ability to apply statistical techniques and R programming to practical data-science and business decision-making problems. The individual assessment combines descriptive statistics, data preparation, visualisation, probability, regression, correlation and sampling, requiring students to demonstrate both conceptual statistical understanding and practical implementation in RStudio. The module learning outcomes emphasise the application of statistical methods to large and real-world datasets, critical evaluation of analytical techniques and awareness of legal, cultural and ethical issues associated with data analysis and dissemination. KL7012 - Statistical Programmin… The early tasks examine fundamental statistical reasoning. Students interpret weight-loss data comparing exercise classes with gym-only workouts using sample size, mean, mode and standard deviation, and explain an appropriate method for dealing with missing data, including its advantages and disadvantages. KL7012 - Statistical Programmin… A substantial practical component uses a cystic fibrosis dataset containing variables such as age, sex, height, weight, body-mass-related measurements, forced expiratory volume, residual volume, functional residual capacity, total lung capacity and maximum expiratory pressure. Students import the data into an R data frame, generate descriptive summaries and interpret the results. They then use scatterplots to investigate relationships between variables and sex-stratified boxplots to identify possible outliers. KL7012 - Statistical Programmin… The assessment also covers major probability models. Students apply probability concepts to healthcare survival, helpdesk email arrivals and fuel-demand scenarios, while also discussing how changing assumptions or real-world conditions can affect interpretation. These exercises assess understanding of statistical distributions and their application to operational and managerial decision-making. KL7012 - Statistical Programmin… Further analytical tasks examine linear regression and correlation. Students analyse the relationship between temperature and converted sugar in a chemical process, use a regression model to estimate the expected response at a specified temperature, and interpret relevant summary statistics. They also calculate and evaluate the suitability of a correlation coefficient for examining the relationship between advertising activity and product purchases. KL7012 - Statistical Programmin… The final and most substantial task involves a real-world M1 traffic-speed investigation for a manufacturing organisation. Students must design an appropriate sampling strategy, collect data from the specified Traffic England source, conduct statistical analysis in RStudio and develop evidence-based conclusions. The statistical report for this task is limited to 1,500 words and should include sampling methodology, collected data, statistical analysis, results, conclusions and relevant background research, supported by appropriate graphs, tables and charts. Raw data and RStudio calculations must be included in an appendix. KL7012 - Statistical Programmin… Overall, the assessment integrates statistical theory with R-based practical analysis, covering descriptive statistics, probability, visualisation, missing-data treatment, regression, correlation, sampling and critical interpretation of results in healthcare, operational and business contexts.

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Principles of Data Science 2,000 words

Principles of Data Science – Data Analysis Portfolio

This portfolio assignment for the Principles of Data Science module at Coventry University requires students to analyse the Global Life-Work Balance Index 2025 dataset using statistical and data science techniques in R. The dataset ranks 60 countries according to life-work balance using factors including statutory annual leave, paid maternity leave, sick leave, healthcare, public safety, public happiness, LGBTQ inclusivity and average working hours per employee. The assignment has a 2,000-word equivalent limit, excluding the reference list and output. The portfolio consists of two main tasks. Task 1 is a group task involving multivariate data analysis. Students must use R to perform Principal Component Analysis (PCA) and Cluster Analysis on the dataset. For PCA, students analyse quantitative variables, produce and interpret relevant visualisations such as screeplots, biplots and loadings plots, and investigate the effects of Region and Healthcare System. The PCA analysis also requires comparison of the overall dataset with countries from Europe. The cluster analysis component requires students to cluster both countries and variables using different distance metrics and hierarchical clustering methods. Students compare methods such as Manhattan and Euclidean distances and single linkage and Ward’s method, present comparisons in compact tables, and interpret relevant dendrograms. They must then compare the conclusions obtained from PCA and Cluster Analysis, identifying common insights and apparent conflicts and discussing the extent to which the results are explainable rather than simply interpretable. Task 2 is an individual task focusing on Exploratory Data Analysis and Linear Models. Students create a scatter matrix using ggpairs(), investigate strongly correlated variables, and identify quantitative variables that may help predict Region for European and Asian countries. They then develop and critically assess linear regression models for predicting Score, including models based on employment variables and broader quantitative predictors. Model comparison and selection use concepts including AIC, while diagnostic plots are used to identify countries requiring further investigation. The individual task also requires students to use European Life-Work Balance Index 2023 data to make predictions for European countries not included in the 2025 dataset and to construct a Residuals versus Fitted Values plot. Finally, students must combine the conclusions from their individual linear modelling work with the PCA and Cluster Analysis findings to identify specific discoveries about the variables and countries in the dataset. R code, output and relevant plots must be included directly within the reports. The assignment encourages use of the R tidyverse and requires appropriate referencing of sources. The brief specifies APA-style referencing for the individual and group work. It also states that generative AI may be used for inspiration but not for generating answers or analysing the datasets, and any permitted AI use must be acknowledged and documented.

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