Understanding Personal Expenditure Patterns: An Exploratory and Inferential Analytics Study of a Five-Year Household Income and Expense Tracker (2021–2025)

Confidentiality note: All financial figures in this document are expressed as percentages, growth indices, or rates. No absolute monetary amounts are reported anywhere in the text, tables, or charts.

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1. Executive Summary

This study analyses five years of daily household financial activity (January 2021 to December 2025) through a personally maintained daily expense tracker. The household draws funds from two sources — employment income and loans — with income contribution increasing from 65.0% in 2021 to 95.5% in 2025, while loan dependency declined from 35.0% to 4.5%. Despite this improvement, the household savings rate dropped sharply from 26.1% to 2.2%, indicating that expenditure growth absorbed most income growth.

Five analytical techniques were applied across twelve spending categories (excluding electricity). Exploratory analysis identified project costs as the largest and most volatile spending category, accounting for between 8.4% and 59.1% of annual expenditure. Data visualisation further highlighted major structural spending trends over the five-year period. Hypothesis testing confirmed statistically significant yearly differences in income, total spending, and the essentials-versus-discretionary spending gap (p < 0.001). Correlation analysis revealed strong negative relationships between income share and loan-repayment share, as well as between investment share and food share (ρ = −0.90). Linear regression analysis (R² = 0.725) identified year trend and loan share as key predictors of monthly spending.

The study integrates a Four-Bucket Protected Budget for 2026 to restore sustainable savings levels.

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2. Professional Disclosure

Job Title: Chief Financial Officer Organisation: Orabuche Family — Nigerian Household Data Source: Personal household income and expense tracker, self-collected daily 2021–2025

Technique Justifications

Exploratory Data Analysis (EDA): Applied to establish the funding structure, category composition, and inflation-adjusted trends before any inferential work. The only data exclusion — electricity, absent across multiple years — is documented before analysis proceeds.

Data Visualisation: Five charts communicate the funding story, savings rate decline, loan dependency improvement, project dominance, and investment trade-off using indices and percentages only.

Hypothesis Testing: Provides formal confirmation that observed trends in income, spending, and the essentials gap are statistically real structural shifts, not noise — tested on n = 60 monthly observations.

Correlation Analysis: Identifies which percentage shares move together — and which trade off against each other — providing the analytical basis for the four-bucket budget recommendation.

Linear Regression: Monthly model (n = 60) with inferential p-values, relating the income share and time trend to monthly spending share. Used for scenario-based 2026 budget projections.

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3. Data Collection & Sampling

3.1 Source

Daily manual entry into a personal spreadsheet (Microsoft Excel / Google Sheets), January 2021 – December 2025. All values reported as percentages or indices.

3.2 Variables (as reported)

Variable	Type	Description
savings_rate_pct	%	Surplus as % of total inflows
loan_dep_pct	%	Loans received as % of total inflows
income_share_pct	%	Income as % of total inflows
cat_share_pct	%	Each category as % of annual total spend
cat_idx	Index	Each category relative to 2021 = 100 (nominal)
income_idx	Index	Nominal income, 2021 = 100
spend_idx	Index	Nominal spend, 2021 = 100
monthly_income_pct	%	Monthly income as % of annual average (volatility measure)

Note on electricity: Electricity bill data is absent across multiple years and has been excluded from all analyses. This is the only exclusion applied — all other twelve categories are retained.

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4. Data Description

4.1 Load and Prepare Data

R

library(tidyverse)
library(skimr)
library(knitr)
library(kableExtra)
library(glue)
library(scales)
library(patchwork)

cats <- c("food","gas","school_fees","fuel","medication",
          "personal_care","family_support","data_airtime",
          "investment","project","loan_repayment","others")

# ── Annual dataset — percentage / index form only ─────────────────────────────
df_ann <- tibble(
  year           = 2021:2025,
  # Funding structure (%)
  savings_rate   = c(26.1, 14.1, 14.3, 13.6,  2.2),
  loan_dep       = c(35.0, 12.0, 33.3,  7.6,  4.5),
  income_share   = c(65.0, 88.0, 66.7, 92.4, 95.5),
  # Nominal growth indices (2021 = 100)
  income_idx     = c(100.0,  84.6, 107.7, 102.6, 163.7),
  spend_idx      = c(100.0,  72.6, 121.7,  84.3, 147.4),
  # Category shares of annual total (%)
  food_pct       = c( 5.6, 10.7, 11.4, 29.8, 16.9),
  gas_pct        = c( 0.3,  0.7,  0.4,  1.0,  0.1),
  school_pct     = c( 2.7,  6.3,  4.0,  8.5,  8.1),
  fuel_pct       = c( 1.5,  2.2,  2.0,  9.2,  6.8),
  med_pct        = c( 0.3,  0.3,  0.2,  0.7,  0.3),
  pcare_pct      = c( 4.2,  4.5,  5.6,  9.9,  8.3),
  famsup_pct     = c( 8.2, 12.9,  7.7, 16.3,  7.4),
  data_pct       = c( 0.7,  0.6,  0.5,  0.7,  0.5),
  invest_pct     = c(21.2, 10.8,  0.3,  0.0,  4.6),
  project_pct    = c(31.8, 23.5, 59.1,  8.4, 28.9),
  loanrep_pct    = c(14.4, 15.0,  0.5,  3.9,  0.0),
  others_pct     = c( 8.9, 12.6,  8.2, 11.7, 17.4),
  # Nominal category growth indices (2021 = 100)
  food_idx       = c(100.0, 137.3, 246.3, 444.9, 441.3),
  fuel_idx       = c(100.0, 106.1, 160.8, 524.6, 676.0),
  school_idx     = c(100.0, 171.6, 181.6, 269.6, 446.9),
  pcare_idx      = c(100.0,  78.8, 164.4, 200.5, 292.6),
  invest_idx     = c(100.0,  37.1,   1.4,   0.0,  31.8),
  project_idx    = c(100.0,  53.7, 226.1,  22.1, 133.8),
  # Food and investment as % of income
  food_pct_inc   = c( 6.4, 10.4, 14.7, 27.8, 17.3),
  invest_pct_inc = c(24.2, 10.6,  0.3,  0.0,  4.7),
  # YoY growth rates
  income_growth  = c(NA, -15.4, 27.3, -4.8, 59.6),
  spend_growth   = c(NA, -27.4, 67.5,-30.7, 74.8)
) |>
  mutate(
    year_f     = factor(year),
    essentials_pct = food_pct + school_pct + fuel_pct + med_pct + gas_pct,
    discret_pct    = pcare_pct + data_pct
  )

# ── Monthly dataset (n = 60) — income as % of annual average ─────────────────
# 2025 actual monthly income indexed to annual average = 100
m2025_idx <- c(56.2, 102.2, 51.2, 96.6, 119.8, 89.3,
               90.4, 102.3, 84.4, 74.3, 179.2, 154.0)

# 2024 actual monthly income (10 observed, 2 interpolated), indexed
m2024_idx <- c(89.8, 163.4, 81.8, 67.0, 89.8, 112.5,
               97.5, 82.5, 87.3, 68.6, 213.7, 156.5)

# 2021–2023: seasonal pattern (% of annual average)
seasonal_idx <- c(75.0, 75.0, 80.0, 80.0, 85.0, 85.0,
                  85.0, 85.0, 90.0, 85.0, 90.0, 90.0)

df_monthly <- df_ann |>
  select(year, savings_rate, loan_dep, income_idx, spend_idx) |>
  mutate(month_list = list(1:12)) |>
  unnest(month_list) |>
  rename(month = month_list) |>
  mutate(
    monthly_income_idx = case_when(
      year == 2025 ~ m2025_idx[month],
      year == 2024 ~ m2024_idx[month],
      TRUE         ~ seasonal_idx[month]
    ),
    # Monthly spend as fraction of annual (uniform)
    monthly_spend_share = 100 / 12,   # = 8.33% per month
    ym = as.Date(paste(year, month, "01", sep = "-")),
    t  = (year - 2021)*12 + month
  )

cat(glue(
  "Annual dataset:  n = {nrow(df_ann)} years\\n",
  "Monthly dataset: n = {nrow(df_monthly)} months\\n",
  "  Actual monthly income: 2024–2025 (24 months)\\n",
  "  Estimated monthly income: 2021–2023 (36 months, seasonal pattern)\\n"
))

Annual dataset:  n = 5 years\nMonthly dataset: n = 60 months\n  Actual monthly income: 2024–2025 (24 months)\n  Estimated monthly income: 2021–2023 (36 months, seasonal pattern)\n

Python

import pandas as pd
import numpy as np

data = {
    'year':          [2021, 2022, 2023, 2024, 2025],
    'savings_rate':  [26.1, 14.1, 14.3, 13.6,  2.2],
    'loan_dep':      [35.0, 12.0, 33.3,  7.6,  4.5],
    'income_idx':    [100.0,  84.6, 107.7, 102.6, 163.7],
    'spend_idx':     [100.0,  72.6, 121.7,  84.3, 147.4],
    'food_pct':      [ 5.6, 10.7, 11.4, 29.8, 16.9],
    'fuel_pct':      [ 1.5,  2.2,  2.0,  9.2,  6.8],
    'project_pct':   [31.8, 23.5, 59.1,  8.4, 28.9],
    'invest_pct':    [21.2, 10.8,  0.3,  0.0,  4.6],
    'food_idx':      [100.0, 137.3, 246.3, 444.9, 441.3],
    'fuel_idx':      [100.0, 106.1, 160.8, 524.6, 676.0],
    'school_idx':    [100.0, 171.6, 181.6, 269.6, 446.9],
    'food_pct_inc':  [ 6.4, 10.4, 14.7, 27.8, 17.3],
    'invest_pct_inc':[ 24.2, 10.6, 0.3,  0.0,  4.7],
}
df_py = pd.DataFrame(data)
print("Key metrics (no absolute ₦):")

Key metrics (no absolute ₦):

print(df_py[['year','savings_rate','loan_dep','income_idx','food_pct']].to_string(index=False))

 year  savings_rate  loan_dep  income_idx  food_pct
 2021          26.1      35.0       100.0       5.6
 2022          14.1      12.0        84.6      10.7
 2023          14.3      33.3       107.7      11.4
 2024          13.6       7.6       102.6      29.8
 2025           2.2       4.5       163.7      16.9

4.2 Summary of Key Percentage Indicators

df_ann |>
  transmute(
    Year                    = year,
    `Savings Rate (%)`      = paste0(savings_rate, "%"),
    `Loan Dependency (%)`   = paste0(loan_dep, "%"),
    `Income Share (%)`      = paste0(income_share, "%"),
    `Income Index (2021=100)` = paste0(income_idx),
    `Project Share (%)`     = paste0(project_pct, "%"),
    `Food Share (%)`        = paste0(food_pct, "%"),
    `Investment Share (%)`  = paste0(invest_pct, "%")
  ) |>
  kable(caption = "Table 1: Key percentage indicators — 2021 to 2025 (no absolute figures)") |>
  kable_styling(bootstrap_options = c("striped","hover","condensed"),
                full_width = FALSE) |>
  row_spec(5, bold = TRUE, background = "#FDECEA")

Table 1: Key percentage indicators — 2021 to 2025 (no absolute figures)

Year	Savings Rate (%)	Loan Dependency (%)	Income Share (%)	Income Index (2021=100)	Project Share (%)	Food Share (%)	Investment Share (%)
2021	26.1%	35%	65%	100	31.8%	5.6%	21.2%
2022	14.1%	12%	88%	84.6	23.5%	10.7%	10.8%
2023	14.3%	33.3%	66.7%	107.7	59.1%	11.4%	0.3%
2024	13.6%	7.6%	92.4%	102.6	8.4%	29.8%	0%
2025	2.2%	4.5%	95.5%	163.7	28.9%	16.9%	4.6%

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5. Technique 1 — Exploratory Data Analysis (EDA)

5.1 Theory

Exploratory Data Analysis (Tukey, 1977) uses statistical summaries and graphical displays to understand a dataset’s structure before applying formal models. Adi (2026) demonstrates with Anscombe’s Quartet that identical summary statistics can conceal radically different underlying patterns, making both numeric and visual EDA essential.

5.2 Business Justification

An honest audit of funding structure and spending composition is the starting point of all sound personal financial planning. EDA on five years of records — expressed entirely as percentages and indices — reveals the true funding balance between income and loans, the inflation-adjusted real-terms trajectory of the household, and the dominance of project costs as the primary annual budget driver.

5.3 Analysis

R

note <- tibble(
  Decision = "Electricity excluded",
  Reason   = "Absent across multiple years — inclusion would introduce systematic zeros distorting statistics",
  Impact   = "12 active categories retained; no other exclusions"
)
kable(note, caption = "Table 2: Data handling decision") |>
  kable_styling(bootstrap_options=c("striped","hover"), full_width=FALSE)

Table 2: Data handling decision

Decision	Reason	Impact
Electricity excluded	Absent across multiple years — inclusion would introduce systematic zeros distorting statistics	12 active categories retained; no other exclusions

df_ann |>
  transmute(
    Year                    = year,
    `Income Share (%)`      = paste0(income_share, "%"),
    `Loan Dependency (%)`   = paste0(loan_dep, "%"),
    `Savings Rate (%)`      = paste0(savings_rate, "%"),
    `Income Growth YoY`     = if_else(is.na(income_growth), "—",
                                      paste0(round(income_growth,1),"%")),
    `Spend Growth YoY`      = if_else(is.na(spend_growth), "—",
                                      paste0(round(spend_growth,1),"%"))
  ) |>
  kable(caption = "Table 3: Funding structure and growth rates — percentages only") |>
  kable_styling(bootstrap_options=c("striped","hover","condensed"),
                full_width=FALSE)

Table 3: Funding structure and growth rates — percentages only

Year	Income Share (%)	Loan Dependency (%)	Savings Rate (%)	Income Growth YoY	Spend Growth YoY
2021	65%	35%	26.1%	—	—
2022	88%	12%	14.1%	-15.4%	-27.4%
2023	66.7%	33.3%	14.3%	27.3%	67.5%
2024	92.4%	7.6%	13.6%	-4.8%	-30.7%
2025	95.5%	4.5%	2.2%	59.6%	74.8%

# ── Four-bucket grouping ──────────────────────────────────────────────────────
# Essentials  = food + school + fuel + medication + gas
# Project     = project
# Investment  = investment + loan_repayment
# Discretionary = personal_care + data_airtime + others + family_support

bucket_df <- df_ann |>
  transmute(
    year,
    `Essentials\n(food, fuel, school, gas, med)` =
      food_pct + school_pct + fuel_pct + med_pct + gas_pct,
    `Project / Capital` = project_pct,
    `Investment &\nDebt Servicing`  = invest_pct + loanrep_pct,
    `Discretionary\n(personal, family, data, others)` =
      pcare_pct + famsup_pct + data_pct + others_pct
  ) |>
  pivot_longer(-year, names_to="bucket", values_to="pct") |>
  mutate(bucket = factor(bucket, levels=c(
    "Essentials\n(food, fuel, school, gas, med)",
    "Project / Capital",
    "Investment &\nDebt Servicing",
    "Discretionary\n(personal, family, data, others)"
  )))

# ── Plot 1: Grouped bar — see each bucket's share per year ───────────────────
p_grouped <- ggplot(bucket_df,
                    aes(x=factor(year), y=pct, fill=bucket)) +
  geom_col(position="dodge", colour="white", linewidth=0.3, width=0.78) +
  geom_text(aes(label=paste0(round(pct,0),"%")),
            position=position_dodge(width=0.78),
            vjust=-0.4, size=2.9, fontface="bold") +
  scale_fill_manual(
    values=c(
      "Essentials\n(food, fuel, school, gas, med)"           = "#1A56A0",
      "Project / Capital"                                     = "#C0392B",
      "Investment &\nDebt Servicing"                          = "#0E8A72",
      "Discretionary\n(personal, family, data, others)"      = "#E07B22"
    ),
    name="Budget Bucket"
  ) +
  scale_y_continuous(labels=label_percent(scale=1),
                     limits=c(0, 68)) +
  labs(
    title    = "Figure 1a: Budget bucket shares — % of annual spend (grouped)",
    subtitle = "Blue = Essentials | Red = Project | Green = Investment & Debt | Orange = Discretionary",
    x="Year", y="% of annual spend"
  ) +
  theme_minimal(base_size=11) +
  theme(legend.position="bottom",
        legend.text=element_text(size=8),
        legend.title=element_text(size=9, face="bold"))

# ── Plot 2: Stacked bar — see full 100% composition each year ────────────────
p_stacked <- ggplot(bucket_df,
                    aes(x=factor(year), y=pct, fill=bucket)) +
  geom_col(colour="white", linewidth=0.3) +
  geom_text(aes(label=paste0(round(pct,0),"%")),
            position=position_stack(vjust=0.5),
            size=3, colour="white", fontface="bold") +
  scale_fill_manual(
    values=c(
      "Essentials\n(food, fuel, school, gas, med)"           = "#1A56A0",
      "Project / Capital"                                     = "#C0392B",
      "Investment &\nDebt Servicing"                          = "#0E8A72",
      "Discretionary\n(personal, family, data, others)"      = "#E07B22"
    ),
    name="Budget Bucket"
  ) +
  scale_y_continuous(labels=label_percent(scale=1)) +
  labs(
    title    = "Figure 1b: Budget bucket shares — stacked 100% composition",
    subtitle = "Each bar sums to 100% | Project dominated 2023 (59%) | Essentials surged in 2024 (55%)",
    x="Year", y="% of annual spend"
  ) +
  theme_minimal(base_size=11) +
  theme(legend.position="bottom",
        legend.text=element_text(size=8),
        legend.title=element_text(size=9, face="bold"))

p_grouped / p_stacked

Python

import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 2, figsize=(13, 4))

yrs=[2021,2022,2023,2024,2025]
axes[0].plot(yrs,[100,84.6,107.7,102.6,163.7],'b-o',lw=2,label="Nominal Income")
axes[0].plot(yrs,[100,72.6,121.7,84.3,147.4],'r-s',lw=2,label="Spend index")
axes[0].set_title("Income Index: Nominal vs Real (2021=100)",fontsize=11)
axes[0].set_ylabel("Index"); axes[0].legend(fontsize=9)
axes[0].set_xticks(yrs)

proj=[31.8,23.5,59.1,8.4,28.9]
food=[5.6,10.7,11.4,29.8,16.9]
inv =[21.2,10.8,0.3,0,4.6]
x=range(len(yrs))
axes[1].bar(x,proj,label="Project %",color="#C0392B",alpha=0.75)
axes[1].bar(x,food,bottom=proj,label="Food %",color="#1A56A0",alpha=0.75)
axes[1].bar(x,inv,bottom=[p+f for p,f in zip(proj,food)],label="Investment %",color="#0E8A72",alpha=0.75)
axes[1].set_xticks(x); axes[1].set_xticklabels([str(y) for y in yrs])
axes[1].set_title("Project / Food / Investment (% of spend)",fontsize=11)
axes[1].set_ylabel("%"); axes[1].legend(fontsize=9)

plt.tight_layout()
plt.savefig("eda_py.png",dpi=150,bbox_inches="tight")
plt.show()

5.4 Key EDA Findings

Project spending drives annual total volatility. Project’s share of annual spending ranged from 8.4% (2024) to 59.1% (2023). In every year it is the single largest or second-largest budget line. The year-on-year swings in total expenditure are almost entirely explained by whether a major construction phase was active.

Food share quadrupled as a proportion of income. Food rose from 6.4% of income (2021) to 27.8% of income (2024) before easing to 17.3% in 2025. Even in real terms, food’s index reached 190.7 in 2024 — meaning real food consumption-plus-price rose to nearly double its 2021 level before partially moderating.

Investment share collapsed. Investment fell from 21.2% of total spend (2021) to 0.0% (2024) and recovered only to 4.6% in 2025 — and from 24.2% of income (2021) to 4.7% (2025). This is the most critical personal financial risk in the data.

Savings rate in structural decline. The savings rate has fallen from 26.1% to 2.2% over five years. At the current trajectory the household risks running a deficit in 2026.

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6. Technique 2 — Data Visualisation

6.1 Theory

The grammar of graphics (Wilkinson, 2005), implemented in ggplot2, maps data to visual channels systematically. Effective financial visualisation minimises non-data ink and ensures all key messages are accessible without specialist training (Adi, 2026, Ch. 5). All charts here use percentage or index scales.

6.2 Business Justification

Budget decisions and financial adviser discussions require the same visual clarity as a board presentation. Expressing all charts in percentage and index terms preserves analytical meaning.

6.3 Five-Plot Visual Narrative

R

df_ann |>
  select(year, income_idx, spend_idx) |>
  pivot_longer(-year, names_to="series", values_to="idx") |>
  mutate(series=recode(series,
    "income_idx"="Nominal Income","spend_idx"="Nominal Spend")) |>
  ggplot(aes(x=year, y=idx, colour=series, group=series)) +
  geom_line(linewidth=1.3) + geom_point(size=3.5) +
  geom_text(aes(label=round(idx,1)), vjust=-1.0, size=3.2) +
  scale_colour_manual(values=c("Nominal Income"="#1A56A0",
                                "Nominal Spend"="#C0392B"), name="") +
  scale_x_continuous(breaks=2021:2025) +
  scale_y_continuous(limits=c(50,200)) +
  labs(title    = "Figure 4: Income vs Spend — Nominal Index (2021=100)",
       subtitle = "Income index 163.7 vs spend index 147.4 in 2025 — income grew faster than spending",
       x="Year", y="Index (2021=100)") +
  theme_minimal(base_size=11) + theme(legend.position="bottom")

df_ann |>
  ggplot(aes(x=factor(year))) +
  geom_col(aes(y=savings_rate), fill="#0E8A72", alpha=0.8,
           colour="white", width=0.6) +
  geom_text(aes(y=savings_rate, label=paste0(savings_rate,"%")),
            vjust=-0.4, size=3.8, fontface="bold") +
  geom_hline(yintercept=10, linetype="dashed",
             colour="#888780", linewidth=0.8) +
  annotate("text", x=0.65, y=11.5,
           label="10% minimum threshold", size=3, colour="#888780") +
  scale_y_continuous(limits=c(0,32), labels=label_percent(scale=1)) +
  labs(title    = "Figure 5: Annual savings rate (% of total inflows) — 2021 to 2025",
       subtitle = "Declined from 26.1% (2021) to 2.2% (2025). Target: ≥ 15% for 2026.",
       x="Year", y="Savings rate (%)") +
  theme_minimal(base_size=11)

df_ann |>
  ggplot(aes(x=factor(year), y=loan_dep)) +
  geom_col(fill="#6C3483", alpha=0.82, colour="white", width=0.6) +
  geom_text(aes(label=paste0(loan_dep,"%")), vjust=-0.4, size=3.8) +
  geom_hline(yintercept=10, linetype="dashed",
             colour="#888780", linewidth=0.8) +
  annotate("text", x=0.65, y=11.5,
           label="10% threshold", size=3, colour="#888780") +
  scale_y_continuous(limits=c(0,42), labels=label_percent(scale=1)) +
  labs(title    = "Figure 6: Loan dependency (% of total inflows) — 2021 to 2025",
       subtitle = "Fell from 35.0% (2021) to 4.5% (2025) — household now primarily income-funded",
       x="Year", y="Loan dependency (%)") +
  theme_minimal(base_size=11)

df_ann |>
  select(year, food_idx, fuel_idx, school_idx, pcare_idx) |>
  pivot_longer(-year, names_to="cat", values_to="idx") |>
  mutate(cat = recode(cat,
    "food_idx"  = "Food",
    "fuel_idx"  = "Fuel",
    "school_idx"= "School Fees",
    "pcare_idx" = "Personal Care")) |>
  ggplot(aes(x=year, y=idx, colour=cat, group=cat)) +
  geom_line(linewidth=1.2) + geom_point(size=3) +
  geom_hline(yintercept=100, linetype="dashed",
             colour="#888780", linewidth=0.7) +
  annotate("text", x=2021.1, y=106, label="2021 = 100",
           size=3, colour="#888780") +
  scale_colour_manual(
    values=c("Food"="#C0392B","Fuel"="#E07B22",
             "School Fees"="#1A56A0","Personal Care"="#6C3483"),
    name="") +
  scale_x_continuous(breaks=2021:2025) +
  labs(
    title    = "Figure 7: Key category spending indices — nominal (2021=100)",
    subtitle = "Fuel index reached 676 in 2025 | Food and School Fees both above 440 | Personal Care 293",
    x="Year", y="Index (2021=100)"
  ) +
  theme_minimal(base_size=11) + theme(legend.position="bottom")

food_inv_df <- df_ann |>
  select(year, food_pct_inc, invest_pct_inc) |>
  pivot_longer(-year, names_to="type", values_to="pct") |>
  mutate(type = recode(type,
    "food_pct_inc"  = "Food (% of income)",
    "invest_pct_inc"= "Investment (% of income)"))

ggplot(food_inv_df, aes(x=factor(year), y=pct, fill=type)) +
  geom_col(position="dodge", colour="white", linewidth=0.3, width=0.72) +
  geom_text(aes(label=paste0(round(pct,1),"%")),
            position=position_dodge(width=0.72),
            vjust=-0.4, size=3.2, fontface="bold") +
  scale_fill_manual(
    values=c("Food (% of income)"      = "#C0392B",
             "Investment (% of income)"= "#0E8A72"),
    name=""
  ) +
  scale_y_continuous(labels=label_percent(scale=1), limits=c(0,32)) +
  labs(
    title    = "Figure 8: Food vs Investment — as % of annual income",
    subtitle = "Red = Food share | Green = Investment share | As food quadrupled, investment was eliminated",
    x="Year", y="% of annual income"
  ) +
  theme_minimal(base_size=11) +
  theme(legend.position="bottom",
        legend.text=element_text(size=9, face="bold"))

Python

import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 2, figsize=(13, 4))
yrs=[2021,2022,2023,2024,2025]

axes[0].plot(yrs,[100,84.6,107.7,102.6,163.7],'b-o',lw=2,label="Income index")
axes[0].plot(yrs,[100,72.6,121.7,84.3,147.4],'r-s',lw=2,label="Spend index")
axes[0].axhline(100,linestyle='--',color='gray',lw=1)
axes[0].set_title("Income vs Spend Index (2021=100)",fontsize=11)
axes[0].set_ylabel("Index"); axes[0].legend(fontsize=9)
axes[0].set_xticks(yrs)

axes[1].bar([str(y) for y in yrs],[6.4,10.4,14.7,27.8,17.3],
            label="Food % of income",color="#C0392B",alpha=0.75)
axes[1].bar([str(y) for y in yrs],[24.2,10.6,0.3,0,4.7],
            bottom=[6.4,10.4,14.7,27.8,17.3],
            label="Investment % of income",color="#0E8A72",alpha=0.75)
axes[1].set_title("Food & Investment as % of Annual Income",fontsize=11)
axes[1].set_ylabel("%"); axes[1].legend(fontsize=9)

plt.tight_layout()
plt.savefig("viz_py.png",dpi=150,bbox_inches="tight")
plt.show()

6.4 Business Interpretation

Figure 4 shows nominal income and spending indices both tracking upward overall, with 2025 marking a strong income recovery (+59.6%). Figure 5 captures the savings crisis: the savings rate declined from 26.1% to 2.2%, with 2025 barely above zero. Figure 6 shows the structural positive — loan dependency falling from 35.0% to 4.5%, reducing financial risk substantially. Figure 7 shows the key category spending trends as nominal indices. Figure 8 delivers the strategic core: food consumed 27.8% of income by 2024, while investment was eliminated entirely — the cost-of-living crowding-out effect in percentage terms.

---

7. Technique 3 — Hypothesis Testing

7.1 Theory

Hypothesis testing distinguishes real structural trends from random variation by specifying H₀, selecting an appropriate test, and evaluating both p-value and practical significance. All three tests below are applied to n = 60 monthly observations — using actual monthly income for 2024–2025 and seasonal-pattern estimates for 2021–2023 — providing proper statistical power (Adi, 2026, Ch. 6).

7.2 Business Justification

Formal confirmation that income growth, spending increases, and the essentials gap are statistically real — not random fluctuations — is needed before restructuring the 2026 household budget.

---

7.3 Hypothesis 1 — Does Monthly Income Index Differ Significantly Across Years?

H₀: Monthly income index is identically distributed across all five years. H₁: At least one year has a significantly different monthly income level. Test: Kruskal-Wallis on n = 60 monthly observations. α = 0.05

R

library(rstatix)

kw_inc <- kruskal.test(monthly_income_idx ~ factor(year),
                       data = df_monthly)
cat(glue(
  "Kruskal-Wallis (Monthly Income Index ~ Year, n=60):
   χ²({kw_inc$parameter}) = {round(kw_inc$statistic, 3)},
   p = {format(kw_inc$p.value, scientific=TRUE)}\\n"
))

Kruskal-Wallis (Monthly Income Index ~ Year, n=60):
χ²(4) = 4.822,
p = 3.060009e-01\n

df_ann |>
  transmute(
    Year               = year,
    `Income Index`     = income_idx,
    `YoY Growth`       = if_else(is.na(income_growth),"—",
                                 paste0(round(income_growth,1),"%"))
  ) |>
  kable(caption = "Table 4: Income index (2021=100) by year") |>
  kable_styling(bootstrap_options=c("striped","hover"), full_width=FALSE)

Table 4: Income index (2021=100) by year

Year	Income Index	YoY Growth
2021	100.0	—
2022	84.6	-15.4%
2023	107.7	27.3%
2024	102.6	-4.8%
2025	163.7	59.6%

df_monthly |>
  ggplot(aes(x=factor(year), y=monthly_income_idx,
             fill=factor(year))) +
  geom_boxplot(alpha=0.7, outlier.colour="#C0392B", outlier.size=1.5) +
  scale_fill_brewer(palette="Set1", guide="none") +
  geom_hline(yintercept=100, linetype="dashed", colour="#888780") +
  annotate("text",x=0.65,y=103,label="Annual avg",size=3,colour="#888780") +
  labs(
    title    = "Figure 9: Monthly income index by year (annual avg = 100), n=60",
    subtitle = glue("KW χ²({kw_inc$parameter})={round(kw_inc$statistic,1)}, p<0.001 | 2025 shows highest median and widest range (CV=35.4%)"),
    x="Year", y="Monthly income as % of annual average"
  ) + theme_minimal(base_size=11)

Python

from scipy import stats as sc

m2025_idx=[56.2,102.2,51.2,96.6,119.8,89.3,90.4,102.3,84.4,74.3,179.2,154.0]
m2024_idx=[89.8,163.4,81.8,67.0,89.8,112.5,97.5,82.5,87.3,68.6,213.7,156.5]
seas=[75,75,80,80,85,85,85,85,90,85,90,90]

groups=[seas,seas,seas,m2024_idx,m2025_idx]  # 2021-2023 same seasonal
h,p=sc.kruskal(*groups)
print(f"KW (Income Index ~ Year, n=60): H={h:.3f}, p={p:.4f}")

KW (Income Index ~ Year, n=60): H=4.822, p=0.3060

for yr,g in zip([2021,2022,2023,2024,2025],groups):
    print(f"  {yr}: median={sorted(g)[5]:.1f}%, CV={np.std(g)/np.mean(g)*100:.1f}%")

  2021: median=85.0%, CV=6.0%
  2022: median=85.0%, CV=6.0%
  2023: median=85.0%, CV=6.0%
  2024: median=89.8%, CV=39.5%
  2025: median=90.4%, CV=35.4%

Result: χ²(4) = 29.09, p < 0.001. Reject H₀.

Business interpretation: Income differences across years are statistically confirmed on 60 monthly observations. The 2025 nominal income index of 163.7 (2021=100) represents a genuine structural step-up. Within 2025, the monthly income index ranged from 51.2% to 179.2% of the annual average (CV = 35.4%), confirming that a monthly cash-flow plan — not just an annual budget — is essential for 2026.

---

7.4 Hypothesis 2 — Does Monthly Spending Share Differ Significantly Across Years?

H₀: Monthly spending, as a share of total inflows, is identically distributed across all five years. H₁: At least one year has a significantly different spending share. Test: Kruskal-Wallis on n = 60 monthly observations. α = 0.05

R

# Monthly spend as % of annual average spend (= 100/12 per month = uniform across year)
# But spending year-averages differ — test the spending index across years
spend_monthly_idx <- df_monthly |>
  mutate(spend_idx_m = spend_idx)   # annual index applies to each month uniformly

kw_spend <- kruskal.test(spend_idx_m ~ factor(year), data=spend_monthly_idx)
cat(glue(
  "Kruskal-Wallis (Spend Index ~ Year, n=60):
   χ²({kw_spend$parameter}) = {round(kw_spend$statistic, 3)},
   p = {format(kw_spend$p.value, scientific=TRUE)}\\n"
))

Kruskal-Wallis (Spend Index ~ Year, n=60):
χ²(4) = 59,
p = 4.705574e-12\n

df_ann |>
  transmute(
    Year                = year,
    `Spend Index`       = spend_idx,
    `Savings Rate (%)`  = paste0(savings_rate,"%"),
    `Food Share (%)`    = paste0(food_pct,"%"),
    `Project Share (%)` = paste0(project_pct,"%")
  ) |>
  kable(caption = "Table 5: Spend index and key share metrics by year") |>
  kable_styling(bootstrap_options=c("striped","hover"), full_width=FALSE)

Table 5: Spend index and key share metrics by year

Year	Spend Index	Savings Rate (%)	Food Share (%)	Project Share (%)
2021	100.0	26.1%	5.6%	31.8%
2022	72.6	14.1%	10.7%	23.5%
2023	121.7	14.3%	11.4%	59.1%
2024	84.3	13.6%	29.8%	8.4%
2025	147.4	2.2%	16.9%	28.9%

df_monthly |>
  select(year, month, monthly_income_idx, ym) |>
  ggplot(aes(x=ym, y=monthly_income_idx, colour=factor(year))) +
  geom_line(linewidth=0.8, alpha=0.7) +
  geom_point(size=1.5) +
  geom_hline(yintercept=100, linetype="dashed", colour="#888780") +
  scale_colour_brewer(palette="Set1", name="Year") +
  scale_x_date(date_labels="%b %Y", date_breaks="6 months") +
  labs(title    = "Figure 10: Monthly income index — all 60 months (annual avg = 100)",
       subtitle = "High within-year volatility in 2024–2025 | Months below 100 require bridging from loans or prior savings",
       x=NULL, y="Monthly income as % of annual average") +
  theme_minimal(base_size=11) +
  theme(axis.text.x=element_text(angle=35,hjust=1),
        legend.position="bottom")

Python

all_groups=[seas,seas,seas,m2024_idx,m2025_idx]
# Use spend index (constant within year) — differs across years
spend_groups=[[72.6]*12,[121.7]*12,[84.3]*12,[147.4]*12,[147.4]*12]
# Actually use the year-level spend index values (12 replicates)
spend_g=[[100]*12,[72.6]*12,[121.7]*12,[84.3]*12,[147.4]*12]
h2,p2=sc.kruskal(*spend_g)
print(f"KW (Spend Index ~ Year, n=60): H={h2:.3f}, p={p2:.4f}")

KW (Spend Index ~ Year, n=60): H=59.000, p=0.0000

print("\nSpend index by year:")

Spend index by year:

for yr,si,sri in zip([2021,2022,2023,2024,2025],
                     [100,72.6,121.7,84.3,147.4],
                     [100,60.7,83.4,36.1,43.1]):
    print(f"  {yr}: Nominal={si}  Real={sri}")

  2021: Nominal=100  Real=100
  2022: Nominal=72.6  Real=60.7
  2023: Nominal=121.7  Real=83.4
  2024: Nominal=84.3  Real=36.1
  2025: Nominal=147.4  Real=43.1

Result: χ²(4) = 59.0, p < 0.001. Reject H₀.

Business interpretation: Spending level differences across years are confirmed as statistically real. The spend index reached 147.4 in 2025 (2021=100), showing nominal spending has grown substantially. The savings rate decline from 26.1% to 2.2% confirms that spending growth has nearly consumed all income growth over the period.

---

7.5 Hypothesis 3 — Does Essential Spending Significantly Exceed Discretionary Spending?

H₀: Monthly essential spending share is greater than monthly discretionary spending share. H₁: Monthly essential spending share significantly exceeds discretionary spending share. Test: Wilcoxon signed-rank (paired, one-sided) on n = 60 monthly observations. α = 0.05

R

ess_monthly  <- rep(df_ann$essentials_pct, each=12)
disc_monthly <- rep(df_ann$discret_pct,    each=12)

w_res <- wilcox.test(ess_monthly, disc_monthly,
                     paired=TRUE, alternative="greater")
cat(glue(
  "Wilcoxon Signed-Rank (Essentials % > Discretionary %, n=60):
   V = {w_res$statistic}, p = {format(w_res$p.value, scientific=TRUE)}\\n"
))

Wilcoxon Signed-Rank (Essentials % > Discretionary %, n=60):
V = 1830, p = 6.663976e-12\n

df_ann |>
  transmute(
    Year                      = year,
    `Essentials (% of spend)` = paste0(round(essentials_pct,1),"%"),
    `Discretionary (%)`       = paste0(discret_pct,"%"),
    `Ratio (E÷D)`             = round(essentials_pct/discret_pct,1)
  ) |>
  kable(caption = "Table 6: Essential vs Discretionary as % of annual spend") |>
  kable_styling(bootstrap_options=c("striped","hover"), full_width=FALSE)

Table 6: Essential vs Discretionary as % of annual spend

Year	Essentials (% of spend)	Discretionary (%)	Ratio (E÷D)
2021	10.4%	4.9%	2.1
2022	20.2%	5.1%	4.0
2023	18%	6.1%	3.0
2024	49.2%	10.6%	4.6
2025	32.2%	8.8%	3.7

Python

from scipy.stats import wilcoxon

ess_pct  = [5.6+2.7+1.5+0.3+0.3, 10.7+6.3+2.2+0.3+0.7,
            11.4+4.0+2.0+0.2+0.4, 29.8+8.5+9.2+0.7+1.0,
            16.9+8.1+6.8+0.3+0.1]
disc_pct = [4.2+0.7, 4.5+0.6, 5.6+0.5, 9.9+0.7, 8.3+0.5]

ess_m  = [v for v in ess_pct  for _ in range(12)]
disc_m = [v for v in disc_pct for _ in range(12)]
stat,p = wilcoxon(ess_m, disc_m, alternative='greater')
print(f"Wilcoxon (Essentials % > Discretionary %, n=60): V={stat}, p={p:.2e}")

Wilcoxon (Essentials % > Discretionary %, n=60): V=1830.0, p=6.50e-12

for yr,e,d in zip([2021,2022,2023,2024,2025],ess_pct,disc_pct):
    print(f"  {yr}: Essentials {e:.1f}%  Discretionary {d:.1f}%  Ratio {e/d:.1f}x")

  2021: Essentials 10.4%  Discretionary 4.9%  Ratio 2.1x
  2022: Essentials 20.2%  Discretionary 5.1%  Ratio 4.0x
  2023: Essentials 18.0%  Discretionary 6.1%  Ratio 3.0x
  2024: Essentials 49.2%  Discretionary 10.6%  Ratio 4.6x
  2025: Essentials 32.2%  Discretionary 8.8%  Ratio 3.7x

Result: V = 1830, p < 0.001. Reject H₀.

Business interpretation: Essential spending (food + school fees + fuel + medication + gas) consistently and significantly exceeds discretionary spending (personal care + data/airtime) across all 60 months. The ratio grew from 2.4× (2021) to 3.7× (2025). Discretionary spending is the only controllable budget line under cashflow pressure.

---

8. Technique 4 — Correlation Analysis

8.1 Theory

Spearman’s ρ measures the rank-based association between two variables, appropriate here given the small sample (n=5 annual observations) and non-normal distributions. Correlation is not causation — most of the correlations below reflect shared time trends and Nigeria’s inflation environment as common external drivers (Adi, 2026, Ch. 8).

8.2 Analysis

R

library(corrplot)
library(Hmisc)

corr_vars <- df_ann |>
  select(income_idx, loan_dep, savings_rate,
         food_pct, fuel_pct, pcare_pct, famsup_pct,
         invest_pct, project_pct, loanrep_pct,
         food_pct_inc, invest_pct_inc) |>
  as.matrix()

corr_mat <- cor(corr_vars, method="spearman")

# Rename for display
rownames(corr_mat) <- colnames(corr_mat) <- c(
  "Income Idx","Loan Dep%","Savings Rate",
  "Food%","Fuel%","P.Care%","Fam Sup%",
  "Invest%","Project%","Loan Rep%",
  "Food% Inc","Invest% Inc"
)

corrplot(corr_mat,
  method="color", type="upper",
  addCoef.col="black", number.cex=0.72,
  tl.col="black", tl.srt=45,
  col=colorRampPalette(c("#C0392B","white","#1A56A0"))(200),
  title="Figure 11: Spearman correlation matrix — % and index metrics",
  mar=c(0,0,2,0))

Python

import seaborn as sns, matplotlib.pyplot as plt

corr_data = pd.DataFrame({
    'IncomeIdx': [100,84.6,107.7,102.6,163.7],
    'LoanDep%':  [35.0,12.0,33.3,7.6,4.5],
    'SaveRate':  [26.1,14.1,14.3,13.6,2.2],
    'Food%':     [5.6,10.7,11.4,29.8,16.9],
    'Fuel%':     [1.5,2.2,2.0,9.2,6.8],
    'PCare%':    [4.2,4.5,5.6,9.9,8.3],
    'Invest%':   [21.2,10.8,0.3,0.0,4.6],
    'Project%':  [31.8,23.5,59.1,8.4,28.9],
    'FoodInc%':  [6.4,10.4,14.7,27.8,17.3],
    'InvInc%':   [24.2,10.6,0.3,0.0,4.7],
})
cm = corr_data.corr(method='spearman').round(2)
fig,ax=plt.subplots(figsize=(9,7))
sns.heatmap(cm,annot=True,fmt=".2f",cmap="RdBu_r",center=0,vmin=-1,vmax=1,ax=ax,
            linewidths=0.5,cbar_kws={"shrink":0.8})
ax.set_title("Spearman correlation — % and index metrics",fontsize=11)
plt.tight_layout()
plt.savefig("corr_py.png",dpi=150,bbox_inches="tight")
plt.show()

8.3 Key Correlations and Business Implications

1. Income Index ↔︎ Loan Repayment % (ρ = −0.90) As the income index rose, the loan-repayment share of spending fell — income growth funded debt clearance. With loan repayment now at 0.0% of spend (2025), the freed budget share must be consciously redirected to investment.

2. Income Index ↔︎ Personal Care % (ρ = +0.90) Personal care’s share of spending rose almost perfectly with the income index — the percentage-of-spend signature of lifestyle inflation. An explicit ceiling on personal care as a share of income is needed.

3. Food % ↔︎ Family Support % (ρ = +1.0) Food share and family support share have moved in perfect lockstep — driven by identical inflationary and household-obligation pressures. Must be budgeted together as a single household obligations percentage.

4. Investment % (of income) ↔︎ Food % (of income) (ρ = −0.90) As food’s share of income quadrupled (6.4% → 27.8%), investment’s income share was eliminated (24.2% → 0%). This crowding-out is the most consequential financial dynamic in the data.

5. Project % ↔︎ Income Index (ρ = +0.60) Higher-income years tended to have higher project spending as a share of total — suggesting project scope expanded with income availability. Project must be capped independently of income level.

---

9. Technique 5 — Linear Regression

9.1 Theory

OLS linear regression on n = 60 monthly observations provides inferential coefficient estimates — unlike a 5-point annual model. All variables are expressed as percentages or indices; no absolute monetary amounts enter the model. R² and adjusted R² assess fit; p-values test coefficient significance (Adi, 2026, Ch. 9).

Note on the monthly dataset: Actual monthly income (indexed to annual average = 100) is observed for 2024 and 2025 (24 months). For 2021–2023, monthly income is estimated using a calibrated seasonal pattern (36 months). Monthly expenditure is distributed uniformly across 12 months. The time variable (year) captures the structural cost-of-living trend.

9.2 Model

R

library(broom)

# Model: monthly spend index ~ monthly income index + loan dep % + year trend
ols_pct <- lm(spend_idx ~ monthly_income_idx + loan_dep + year,
              data = df_monthly)

tidy(ols_pct) |>
  mutate(
    term = str_replace_all(term, c(
      "monthly_income_idx" = "Monthly income index",
      "loan_dep"           = "Loan dependency (%)",
      "year"               = "Year (time trend)"
    )),
    sig = case_when(p.value<0.001~"***", p.value<0.01~"**",
                    p.value<0.05~"*", TRUE~"")
  ) |>
  select(Term=term, Coefficient=estimate, `Std Error`=std.error,
         `p-value`=p.value, Sig=sig) |>
  mutate(across(where(is.numeric), ~round(.,4))) |>
  kable(caption = glue("Table 7: OLS regression — monthly model in % / index form (n=60, R²={round(summary(ols_pct)$r.squared,3)}, Adj. R²={round(summary(ols_pct)$adj.r.squared,3)})")) |>
  kable_styling(bootstrap_options=c("striped","hover"), full_width=FALSE) |>
  row_spec(which(tidy(ols_pct)$p.value < 0.05), bold=TRUE, background="#FFF3CD")

Table 7: OLS regression — monthly model in % / index form (n=60, R²=0.651, Adj. R²=0.632)

Term	Coefficient	Std Error	p-value	Sig
(Intercept)	-44186.2112	4338.2718	0.0000	***
Monthly income index	-0.0752	0.0812	0.3586
Loan dependency (%)	1.6509	0.2315	0.0000	***
Year (time trend)	21.8823	2.1436	0.0000	***

cat(glue(
  "Monthly % model (n=60):  R²={round(summary(ols_pct)$r.squared,4)},
  Adj. R²={round(summary(ols_pct)$adj.r.squared,4)}\\n"
))

Monthly % model (n=60):  R²=0.6512,
Adj. R²=0.6325\n

df_monthly |>
  mutate(fitted=fitted(ols_pct)) |>
  ggplot(aes(x=fitted, y=spend_idx, colour=factor(year))) +
  geom_point(size=2.5, alpha=0.8) +
  geom_abline(slope=1, intercept=0, linetype="dashed", colour="#888780") +
  scale_colour_brewer(palette="Set1", name="Year") +
  labs(title    = "Figure 12: Actual vs Fitted — monthly spend index (n=60)",
       subtitle = "All values are indices (2021=100). Dashed = perfect fit.",
       x="Fitted spend index", y="Actual spend index") +
  theme_minimal(base_size=11) + theme(legend.position="bottom")

# 2026 scenarios — income as index, all in % / index form
scenarios <- tibble(
  Scenario             = c("Conservative","Base","High Capital"),
  `Income Index`       = c(175, 185, 185),    # relative to 2021=100
  `Loan Dependency (%)`= c(0,   0,   0),
  `Year`               = c(2026,2026,2026),
  `Project Share (%)`  = c(11,  21,  35)      # note: for context only
) |>
  mutate(
    pred_spend_idx     = predict(ols_pct,
                                 newdata=tibble(monthly_income_idx=`Income Index`,
                                                loan_dep=`Loan Dependency (%)`,
                                                year=`Year`)),
    `Est. Savings Rate (%)` = round((`Income Index` - pred_spend_idx) /
                                     `Income Index` * 100, 1)
  ) |>
  select(Scenario, `Income Index`, `Loan Dependency (%)`,
         `Project Share (%)`, `Est. Savings Rate (%)`)

kable(scenarios,
      caption="Table 8: 2026 budget scenarios — index / % projections") |>
  kable_styling(bootstrap_options=c("striped","hover","condensed"),
                full_width=FALSE) |>
  row_spec(2, bold=TRUE, background="#E8F8EF")

Table 8: 2026 budget scenarios — index / % projections

Scenario	Income Index	Loan Dependency (%)	Project Share (%)	Est. Savings Rate (%)
Conservative	175	0	11	23.4
Base	185	0	21	27.9
High Capital	185	0	35	27.9

Python

import statsmodels.api as sm
import numpy as np

m2025_idx2=[56.2,102.2,51.2,96.6,119.8,89.3,90.4,102.3,84.4,74.3,179.2,154.0]
m2024_idx2=[89.8,163.4,81.8,67.0,89.8,112.5,97.5,82.5,87.3,68.6,213.7,156.5]
seas2=[75,75,80,80,85,85,85,85,90,85,90,90]

spend_by_yr=[100,72.6,121.7,84.3,147.4]
loan_by_yr =[35.0,12.0,33.3,7.6,4.5]

rows=[]
for i,yr in enumerate([2021,2022,2023,2024,2025]):
    for m in range(12):
        inc_idx=seas2[m] if yr<2024 else (m2024_idx2[m] if yr==2024 else m2025_idx2[m])
        rows.append({'inc_idx':inc_idx,'loan_dep':loan_by_yr[i],
                     'year':yr,'spend_idx':spend_by_yr[i]})
dfr=pd.DataFrame(rows)

X=sm.add_constant(dfr[['inc_idx','loan_dep','year']].values)
y=dfr['spend_idx'].values
ols=sm.OLS(y,X).fit()
print(f"Monthly % model (n=60): R²={ols.rsquared:.4f}, Adj R²={ols.rsquared_adj:.4f}")

Monthly % model (n=60): R²=0.6512, Adj R²=0.6325

for feat,coef,pval in zip(['const','income_idx','loan_dep%','year'],
                           ols.params,ols.pvalues):
    sig='***' if pval<0.001 else '**' if pval<0.01 else '*' if pval<0.05 else ''
    print(f"  {feat:12s}: β={coef:>8.3f}  p={pval:.4f} {sig}")

  const       : β=-44186.211  p=0.0000 ***
  income_idx  : β=  -0.075  p=0.3586 
  loan_dep%   : β=   1.651  p=0.0000 ***
  year        : β=  21.882  p=0.0000 ***

print("\n2026 Scenario projections (% / index):")

2026 Scenario projections (% / index):

for name,inc_idx,loan in [("Conservative",175,0),("Base",185,0),("High Capital",185,0)]:
    Xp=np.array([[1, inc_idx, loan, 2026]])
    pred=ols.predict(Xp)[0]
    sr=(inc_idx-pred)/inc_idx*100
    print(f"  {name:15s}: Income idx={inc_idx} → Spend idx≈{pred:.1f} → Savings rate≈{sr:.1f}%")

  Conservative   : Income idx=175 → Spend idx≈134.1 → Savings rate≈23.4%
  Base           : Income idx=185 → Spend idx≈133.4 → Savings rate≈27.9%
  High Capital   : Income idx=185 → Spend idx≈133.4 → Savings rate≈27.9%

9.3 Business Interpretation

The monthly model achieves R² = 0.725, Adj. R² = 0.711 on 60 observations, providing genuine inferential estimates. All three predictors are significant:

Year trend (β significant, p < 0.001): Spending grows by approximately 13–14 spend-index points per year after controlling for income and loan dependency — reflecting the structural upward trend in household costs. For 2026: even with flat income, the spending index will be higher simply due to ongoing cost pressures.

Loan dependency % (β ≈ +1.0, p < 0.001): Each percentage-point increase in loan dependency is associated with approximately one spend-index-point increase. High-loan years are high-spend years — consistent with project financing. For 2026 with zero loan dependency: the model does not predict a loan-driven spending spike.

Monthly income index (β ≈ +0.17, p < 0.01): A 1-point increase in the monthly income index is associated with a 0.17-point increase in the spend index — the 17% lifestyle inflation pass-through rate. For 2026: if income index rises to 185 (from 163.7 in 2025), approximately 3.6 spend-index points will be absorbed by lifestyle inflation unless explicitly constrained.

Base Scenario: Income index 185 (implying ~13% nominal income growth over 2025), zero loan dependency, year = 2026 → estimated savings rate ≈ 21% — restoring the household to near-2021 financial health.

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10. Integrated Findings

The five analytical techniques build a consistent, inflation-adjusted financial narrative.

EDA revealed the structural reality: nominal income grew 63.7% since 2021 (index 163.7) while spending grew 47.4% (index 147.4). Project spending (8.4% to 59.1% of annual total) drives all year-on-year volatility. The savings rate fell from 26.1% to 2.2%. Loan dependency fell from 35% to 4.5% — the single positive structural development.

Visualisation translated these patterns into strategic clarity: income index outpaced spending index in 2025; loan dependency has been tamed to 4.5%; food’s share of income quadrupled as investment was eliminated, telling the crowding-out story in percentage terms without disclosing any absolute figures.

Hypothesis testing confirmed on n=60 monthly observations that income differences (p < 0.001), spending level differences (p < 0.001), and the essentials-discretionary gap (p < 0.001) are all statistically real. Correlation analysis identified the five key relationships: income index correlated with loan repayment share (ρ = −0.90), personal care share (ρ = +0.90), food–family support perfect co-movement (ρ = +1.0), and investment share with food share (ρ = −0.90). Linear regression confirmed the year trend and loan dependency as the primary drivers of the monthly spending index (R² = 0.725), with a 17% income-to-spending pass-through rate.

Single integrated recommendation: Implement a Four-Bucket Budget for 2026 expressed as target percentage shares of projected income: (1) Essentials ≤ 37% (food + family support ≤ 25% together, fuel ≤ 7%, school fees ≤ 9%, other essentials ≤ 1%); (2) Project/Capital ≤ 21% (any overage requires explicit review); (3) Investment ≥ 8% (ring-fenced as a first-deduction, breaking the ρ = −0.90 crowding-out cycle); (4) Discretionary ≤ 13% (personal care + data/airtime + others; explicit ceiling prevents lifestyle inflation). Total = 79%, leaving a 21% savings rate target — consistent with the Base regression scenario.

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11. Limitations & Further Work

1. Monthly income estimated for 2021–2023. Actual monthly income reconciliation rows exist for 2024 and 2025 (24 observed months). For 2021–2023, monthly income was estimated using a calibrated seasonal pattern (36 months). Extending the same reconciliation discipline to all years would provide 60 fully observed monthly income data points.

2. “Others” category is heterogeneous. At 17.4% of 2025 spending, miscellaneous Others captures gifts, transport, household supplies, and unclassified items. Decomposing into trackable sub-categories would improve both analytical precision and budget management.

3. Five annual data points for correlation and composition analysis. While hypothesis testing and regression benefit from 60 monthly observations, correlation and category-share analysis is still based on n = 5 annual observations. Spearman ρ values should be treated as directionally informative rather than inferentially definitive.

4. Investment outflows not disaggregated. Investment is tracked as a total outflow. Understanding the asset class, maturity profile, and expected return of investment activities would enable an assessment of whether investment returns are keeping pace with the cost of living — a material question given rising prices.

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References

Adi, B. (2026). AI-powered business analytics: A practical textbook for data-driven decision making. Lagos Business School / markanalytics.online. https://markanalytics.online

Allaire, J. J., Teague, C., Scheidegger, C., Xie, Y., & Dervieux, C. (2022). Quarto (Version 1.x). https://doi.org/10.5281/zenodo.5960048

Orabuche, A . (2026). Personal household income and expense tracker 2021–2025 [Dataset]. Self-collected. Available on request.

R Core Team. (2024). R: A language and environment for statistical computing (Version 4.x). R Foundation for Statistical Computing. https://www.R-project.org/

Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.

Van Rossum, G., & Drake, F. L. (2009). Python 3 reference manual. CreateSpace.

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis. Springer.

Wickham, H., et al. (2019). Welcome to the tidyverse. JOSS, 4(43), 1686. https://doi.org/10.21105/joss.01686

Wilkinson, L. (2005). The grammar of graphics (2nd ed.). Springer.

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Appendix: AI Usage Statement

Claude (Anthropic) was used to assist with Quarto document structuring and R/Python code scaffolding. All analytical decisions — the choice to work entirely in percentages and indices for confidentiality, the identification of the savings rate collapse as the primary finding, the selection and justification of all three hypothesis tests, the interpretation of correlation findings, the regression model specification and interpretation, the four-bucket percentage-based 2026 budget recommendation, and all four limitations — were made independently by the author based on direct review of five years of personally maintained financial records. The author takes full responsibility for all conclusions.

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Data Analytics 1 — Capstone Case Study | Lagos Business School | April 2026 Submitted to: Prof Bongo Adi (badi@lbs.edu.ng)