# Assessment Template {.unnumbered}

---
title: "ENVS225 Assignment 1"
author: "Anonymous"
date: "`r Sys.Date()`"
format:
  html:
    toc: true
    toc-depth: 3
    number-sections: true
    code-fold: show
    df-print: paged
  pdf:
    toc: true
    number-sections: true
execute:
  echo: true
  warning: false
  message: false
---

**Suggested Report Structure** — This template follows the requested sections. Each section contains guidance text and an empty R code cell for your analysis. Download it from [here ](https://raw.githubusercontent.com/GDSL-UL/stats/main/general/reportTemplate.qmd)and work on it with your own Rstudio.

## Setup

Load commonly used libraries for statistical analysis and data manipulation.

```{r}
# Core tidyverse
library(tidyverse)    # includes ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr, forcats

# Tables & reporting
library(knitr)
library(kableExtra)

# Stats helpers
library(broom)

## These need to be installed on your PC.
```

::: {style="red"}
**Once all the chunks execute correctly (no errors when running all the code, `Ctrl +Alt + R` ) you can render the `qmd` file. Follow the instructions [here](https://gdsl-ul.github.io/stats/general/howSubmit.html) to render the document to a PDF. Remove this message and any irrelevant text from the final submission.**
:::

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## Introduction

**Aim.** State clearly what you want to investigate (e.g., association between X and Y).\
**Relevance.** Explain why the topic matters (theory, policy, practice).\
**Gap & Research Question (RQ).** Identify what is missing in the current knowledge and pose 1 RQ (avoid yes/no questions; ask *how*, *to what extent*, *which factors*).\
**Structure.** Briefly preview how the rest of the article is organized.

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## Literature Review

**What we know.** Summarise key findings from prior studies relevant to your RQ.\
**Predictor rationale.** Justify the inclusion of variables (theory-driven, prior empirical evidence).\
**Remaining gap.** Specify precisely the gap this report addresses (e.g., population, geography, time period, variable, method).\
*Note:* The goal is a clean, sensible analysis grounded in existing ideas—not necessarily novel theory.

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## Methodology

**Data.** Briefly describe the dataset (who collected it, when, sample size, key variables).\
**Transformations.** Describe any recoding/aggregation (e.g., bin income into bands; reduce age groups from 11 to 3). Provide justification.\
**Techniques.** Outline and justify the statistical methods used (e.g., GLM/LM, logistic regression, mixed models, matching).

Important: do not include statistics here, or graphs, or tables.

## Results and Discussion

```{r}
# summary variables, distribution, plots, etc
```

**Present the variables** Start with descriptive statistics for your variables and distribution. If relevant, include discuss one-to-one associations (briefly, e.g. correlation plots). You can provide correlation plots and/or boxplots but do not overload the report with graphs.\*\*\*

```{r}
# run the model, show tables summarising the Regression model.
```

**Results + interpretation.** Present estimates and interpret them in plain language, tying back to the RQ. The Regression model should the core of this section!! **Link to literature.** Compare/contrast with prior findings. **Selective visuals.** Use clear charts/tables to highlight key results only (avoid clutter). ------------------------------------------------------------------------

## Conclusion

**Summary.** Recap the main findings vis‑à‑vis the RQs (do not introduce new results).\
**Limitations.** Offer a brief, honest self‑critique (data, measurement, design, external validity).\
**Implications.** Indicate what the findings suggest for practice, policy, or future research.

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