What are the key socioeconomic factors predicting Electronic Gaming Machine expenditure per capita across Local Government Areas in Victoria, and how can these insights inform targeted policy interventions to mitigate the adverse effects of gambling? 

Group W09 G7 

 

Caleb Beschi Cunningham 

COMP20008 

<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS> 
	

William Brooksby 

Comp20008 

<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS> 
	

Jiaxin Wang 

Comp20008 

<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS> 
	

Rahul Kulkarni 

Comp20008 

<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS> 
	

 

 
	

 
	

 
	

 
	

 

 

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Executive Summary 

This report aims to explore the relation between socioeconomic factors, specifically in Local Government Areas across Victoria, and their effect on the total expenditure per capita on electronic gambling machines in these areas. These relationships may better inform us to make informed educated recommendations of policy which may help aid gambling addiction. Scatter plots and Pearson correlation coefficient, k-means clustering, linear regression, k-NN and supervised random forest regressor with recursive feature elimination and cross-validation were used to investigate correlations and generate predictive models. Factors such as low income, lone-parent household and relative socioeconomic disadvantage scores was significant impact in electronic gambling expenditure. This report sheds light on the importance of instituting policy for job security with fair pay, social services such as couples therapy and other recommendations to combat social factors instrumental in increasing the damage of gambling addiction.  

 

Introduction    

The proliferation of Electronic Gaming Machines (EGMs) has emerged as a significant public health concern in Victoria, with substantial implications for community well-being and economic stability. EGMs, commonly found in clubs, pubs, and casinos, have been associated with adverse social outcomes, including financial hardship, mental health issues, and family disruptions. Understanding the socioeconomic factors that contribute to EGM expenditure is crucial for developing effective policies and interventions. 

Data exploration and preprocessing 

After investigating the available data sets, we selected several data points we found interesting, and thought could plausibly have a causal or predictive relationship with gambling spending. The chosen points are as follows: 

 

    LGA 

    2012 ERP age lower bound- upper bound, persons 

    2012 is the only data that aligns with the gambling dataset. 

    Number of Households 

    Personal income <$400/week, persons 

    Number of families 

    Female-headed lone parent families 

    Male-headed lone parent families 

    IRSD (avg) 

    A composite metric that uses data on factors such as income, education, employment status, and access to resources to measure how disadvantaged an area is compared to others. 

    Holds degree or higher, persons 

    Did not complete year 12, persons 

    Unemployed, persons 

    Aboriginal or Torres Strait Islander, persons 

    Born in non-English speaking country, persons 

 

 

Data prep/ Imputation 

Across the communities’ data set values less than 5 were replaced with <5 rather than their true value. This requires data imputation to avoid losing valuable data points. Here we simply replace <5 with 2.5 across the dataset to minimise the maximum deviation from the true value that could possibly occur. Given the minimum range across the chosen columns is 435 (male headed lone parent households) the maximum error is 0.0057 relative to range (and significantly less again across all other columns). This level of discrepancy is negligible compared to other sources of error and provides significantly more data points.  

 

The gambling dataset was grouped by LGA rather than town/ suburb as in the communities’ dataset. To allow an inner join and comparisons between the two the communities dataset was grouped by LGA. Most chosen columns were totals so could be simply added whereas IRSD (avg) had to be handled more carefully to get the weighted average across the suburbs in the LGA. Finally, the LGA names across the two datasets had to be standardised to facilitate the inner join of the two. 

 

Data manipulation/ descriptive statistics  

The cleaned and combined dataset can now be used to derive new metrics of interest, such as gambling losses per capita (the feature being predicted or explored). The total population is calculated using the separate age group data, allowing most data points to be converted into percentages. This conversion is crucial because comparing LGAs (Local Government Areas) of different sizes based on raw counts can be misleading, as larger populations naturally have higher counts. 

 

Table 1: Pearson correlation b/w each variable of interest and target variable (Losses per Capita) 

Variable 
	

Correlation with losses per capita 

IRSD (avg) 
	

-0.4187 

% Personal income <$400/week 
	

0.1023 

% Holds degree or higher 
	

0.0463 

% Did not complete year 12 
	

0.0231 

% Unemployed 
	

-0.1975 

% Aboriginal or Torres Strait Islander 
	

-0.1035 

% Born in non-English speaking country 
	

-0.0326 

% Female-headed lone parent families 
	

0.2022 

% Male-headed lone parent families 
	

0.1638 

 

Table 2: Descriptive statistics 

 
	

IRSD (avg) 
	

% Personal income <$400/week 
	

% Holds degree or higher 
	

% Did not complete year 12 
	

% Unemployed 
	

% Aboriginal or Torres Strait Islander 
	

% Born in non-English speaking country 
	

% Female-headed lone parent families 
	

% Male-headed lone parent families 
	

losses per capita 

count 
	

57.0 
	

57.0 
	

57.0 
	

57.0 
	

57.0 
	

57.0 
	

57.0 
	

57.0 
	

57.0 
	

57.0 

mean 
	

1001.552 
	

30.423 
	

12.896 
	

38.754 
	

2.332 
	

0.938 
	

11.94 
	

12.609 
	

2.694 
	

470.86 

std 
	

46.007 
	

3.876 
	

7.031 
	

10.055 
	

0.574 
	

0.762 
	

11.367 
	

2.035 
	

0.384 
	

221.49 

min 
	

899.11 
	

23.162 
	

5.514 
	

11.131 
	

1.145 
	

0.098 
	

1.492 
	

8.635 
	

1.894 
	

110.126 

max 
	

1097.682 
	

41.94 
	

35.399 
	

54.984 
	

4.034 
	

3.584 
	

47.277 
	

16.628 
	

3.759 
	

1446.956 

(max - mean) / std 
	

2.089 
	

2.972 
	

3.201 
	

1.614 
	

2.962 
	

3.471 
	

3.109 
	

1.975 
	

2.77 
	

4.407 

 

Figure 1: Scatter plot of highest (IRSD) and lowest (Holds degree) with gambling losses per capita. 

Initial insight 

Indicated both by the max value for losses per capita being 4.407 standard deviations from the mean (table 2) combined with visual inspection of the scatter plots there is clearly an outlying data point. This point was the removed manually to improve the rest of the analysis. It would be insightful to explore the cause of this extreme value including consulting experts but for the purposes of this report simple removing it is responsible and necessary. 

 

Table 3: Outlier removed person correlation 

Variable 
	

NO OUTLIER Correlation with losses per capita 

IRSD (avg) 
	

-0.4821 

% Personal income <$400/week 
	

-0.0956 

% Holds degree or higher 
	

0.1439 

% Did not complete year 12 
	

-0.1036 

% Unemployed 
	

-0.1222 

% Aboriginal or Torres Strait Islander 
	

-0.124 

% Born in non-English speaking country 
	

0.0416 

% Female-headed lone parent families 
	

0.4436 

% Male-headed lone parent families 
	

0.3108 

A graph with blue dots

Description automatically generatedA graph with blue dots

Description automatically generatedFigure 2: Scatter plot of highest (IRSD) and lowest (Personal income < $400 per week) with gambling losses per capita. 

 

Further Insights 

Included above is a small sample of the scatter plots, provided for the sake of brevity. The exclusion of the outlier has notably enhanced the readability of these scatter plots and influenced the correlation coefficients. Across the scatter plots, some linear relationships are apparent—for instance, in Figure 2 (IRSD). However, no obvious clustering was visually obvious in any of the plots. The correlation coefficients suggest that IRSD, along with both male and female-headed lone parent households, are more predictive than the other variables. This will guide further, more advanced analysis. Although the correlation remains moderate to low, the combined effect of these variables may provide deeper insights. While it is beyond the scope of this subject, confidence intervals could offer additional perspective, particularly since the low sample size might result in large intervals. 

 

Data Modelling  

Unsupervised K-means Clustering Model 

 

Figure 2.1. 1: K-means clustering of income < $400/week vs female headed lone parent family vs gambling expenditure.  
	

 

Figure 2.1. 2: K-means clustering of IRSD on average vs number of female headed lone parent family’s vs gambling expenditure. 

 

After preprocessing, and data exploration was finished on the merged communities and EGM csv files, outliers were apparent regarding the magnitude of gambling expenditure in certain LGA’s. Once these values were removed, the 3 columns of interest which were compared in Figure 2.1.1 and 2.1.2, had to be normalised using MinMaxScalar() to ensure k-means gave a reasonable, valid clustering to interpret.  

 

K-means clustering was used as it works well on clusters of a hyper spherical structure, which was the initial assumption of the data would appear once plotted. An additional benefit for k-means clustering was the desire to find the centroids of the clusters, in hopes of making meaningful comparison between clusters and interpret the implication to the research question. The KMeans() function was fitted to the normalised data and plotted on a 3-dimensional scatter plot.  

 

Table 4: Centroids both k-means plots 

 

Centroids of IRSD, Education vs Gambling k = 2. 

 
	

Centroids of Income, Lone-headed family vs Gambling k = 2 

[0.729 0.446 0.387] 
	

[0.102 0.104 0.365] 

[0.358 0.112 0.510] 
	

[0.528 0.504 0.543] 

 

Clusters with high levels of IRSD and higher numbers of individuals with degrees were categorised by lower gambling expenditure per Capita. Whereas lower IRSD scores, indicating higher relative socioeconomic disadvantage, and lower attainments of degrees were synonymous with higher values of gambling expenditure per capita. Lower number of female-headed lone-parent households and lower number of individuals with an income of less than 400 per week was also seen to be indicative of lower gambling expenditure per capita. This increased in a linear fashion with increases in both x and y axis values resulted in an increase in z. The implication of the coordinates of both centroids is that increased levels of socioeconomic disadvantage, accompanied with a lower income and lower education, and the stress and impact of a single parent household is correlated with an increase in gambling.  

 

Elbow Method Evaluation  

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Figure 2.1. 3: Average distance from every k centroid to all data points for income < $400/week vs female headed lone parent family vs gambling expenditure. 
	

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Figure 2.1. 4: Average distance from every k centroid to all data points for IRSD on average vs number of female headed lone parent family’s vs gambling expenditure. 

 

Visual Assessment of clustering Tendency (VAT) Evaluation  

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Description automatically generated with medium confidence 
	

 

 

 

 

 

 

 

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Both VAT and the ‘Elbow’ diagrams indicate that the optimal number of k clusters to be 2 or 3. Through trial-and-error k=2 provided the most insightful data to draw comparison. 

 

Supervised Linear Regression Model 

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Figure 2.2.1: Linear Regression Model of Number of Female-headed lone parent families vs Gambling Expenditure per Capita 
	

 

Figure 2.2.2: Linear Regression Model of Average IRSD of LGA vs Gambling Expenditure per Capita 

 

 

A linear regression was used as a supervised machine learning model due to the continuous nature of the data we were interested in. The LinearRegression() function was used and fitted to the training data which was then plotted with .predict() against the test data. After normalising the data to reduce MSE we implemented a test-train split of our data to train the model in conjunction with MSE and [Equation] to test and evaluate the validity the predictions of the regression lines. All these values were calculated utilising the sklearn python library.  

 

Table 5: Mean squared error, r squared, and the coefficient calculated on Figure 2.21 and 2.2.2 

MSE 
	

[Equation] 
	

Coefficients 

17332.35 
	

0.279 
	

-0.491 

26555 
	

0.104 
	

0.239 

 

The regression lines supported the k-means clustering, with a negative and positive coefficient for IRSD and number of Female-headed lone parent household respectively. The [Equation] values for both regression lines were very low indicating a poor ability to accurately predict gambling expenditure. However, this was due to a small sample size. For further improvement more data is needed to train the model and more correlated training samples could be added to ultimately reduce mean squared error.  
 

Multiple Linear Regression Model 

 

A multiple linear regression was used in this project too. The top 3 variables most correlated with ‘losses per capita’, as determined by Pearson correlation coefficients were selected from the dataset to perform multiple linear regression model. The 3 variables are Irsd(avg), Female-headed lone parent families and Male-headed lone parent families. This model evaluates their combined impact on gambling losses per capita.  

 

We used the selected variables as predictors and ‘losses per capita’ as the target variable. The dataset was split into training and testing sets. After training, predictions were made using the test data, and the model was using Mean Squared Error (MSE) and R-squared(R^2) to access its accuracy and goodness of fit. 

 

Table 6:  

MSE 
	

RMSE 
	

[Equation] 

25193.07 
	

158.72 
	

0.35 

 

Table 7: 

 
	

Coefficients 

IRSD(avg) 
	

-2.454 

Female-headed lone parent families 
	

3.21 

Male-headed lone parent families 
	

-126.47 

 

IRSD (avg): The coefficient is -2.454, indicating that for every one-unit increase in the average IRSD value, losses per capita decrease by 2.454 units. % Female-headed lone parent families: The coefficient is 3.21, meaning that for every one-unit increase in the percentage of female-headed lone parent families, losses per capita increase by 3.21 units. Male-headed lone parent families: The coefficient is -126.47, meaning that for every one-unit increase in the percentage of male-headed lone parent families, losses per capita decrease by 126.47 units. 

 

Supervised k Nearest Neighbours (k-NN) Model 

 

Figure 2.3.1: Confusion Matrix of k-NN Models’ ability to accurately predict Gambling Expenditure 

 

To generate a k-NN model we need to categorise the total gambling per capita data. This was done by creating a new column in the data set and segmenting the previous data in low, medium and high. Once the y-data was categorised the KNeighborsClassifier() function was used with the same test train split of the data used in the regression model, with knn.predict(x-data) function used on x data. This model allows you to predict the level of gambling expenditure of a region on unseen future data based on number of persons with a personal income <$400 p.w, IRSD and number of female lone-parent households. However, this model reported an accuracy of 0.5, which was calculated using .score() on the x and y test data. The precision of the sections low, medium and high being 0, 0.71, and 0.25 respectively.  

[Equation] 

[Equation] 

[Equation] 

 

This indicates the model is performing well for estimating ‘medium’ expenditure however poorly for both low and high. This is due to limited training data the model was tested on. To further improve accuracy more data is needed to both train and test.  

 

Supervised Random Forest Regressor with Recursive Feature Elimination and Cross-Validation (RFECV) 

 

An initial set of 23 socioeconomic features was considered for analysis, and Recursive Feature Elimination with Cross-Validation (RFECV) was used to improve model performance and interpretability. RFECV iteratively removes features, assessing model performance at each step by recording the mean R² score. Through multiple iterations, each reducing the feature count by one, a subset of six features was identified as optimal, maximizing the model's mean cross-validated R² score. This streamlined feature set enhanced interpretability while reducing computational complexity. 

Handling Multicollinearity through Feature Reduction 

 

Multicollinearity, characterized by high correlations among predictor variables, can obscure the true relationship between independent variables and the target variable. Although Random Forest models are relatively robust to multicollinearity due to their ensemble nature, addressing it improves model interpretability and the stability of feature importance metrics. 

Approach: 

Feature Elimination: Redundant variables were removed based on domain knowledge and their correlation with the target variable. 
Variance Inflation Factor (VIF) Assessment: VIF values were computed for the remaining features to quantify multicollinearity. Features with VIF < 5 were retained to ensure minimal multicollinearity. 

Outcome: 

The reduction to six key predictors effectively mitigated multicollinearity issues, enhancing the stability and reliability of the model's feature importance metrics. 

Model Training and Evaluation 

A Random Forest Regressor was selected for its robustness, ability to capture complex non-linear relationships, and resistance to overfitting. Its ensemble nature makes it suitable for datasets with limited observations relative to the number of features, and it effectively handles both numerical and categorical variables. The following steps outline the model training and evaluation process: 

Hyperparameter Tuning 

 

Table 8:  

Parameter 
	

Values 

Number of Estimators 
	

100, 200, 500 

Maximum Depth 
	

5, 10, 20 

Minimum Samples Split 
	

2, 5, 10 

Evaluation Metrics 

 

Table 9:  

Metric 
	

Description 

Coefficient of Determination (R²) 
	

Measures the proportion of variance in the dependent variable predictable from the independent variables. 

Mean Absolute Error (MAE) 
	

Represents the average absolute difference between predicted and actual values. 

Mean Squared Error (MSE) 
	

Measures the average squared difference between predicted and actual values. 

Root Mean Squared Error (RMSE) 
	

The square root of MSE, providing error measurement in the same units as the target variable. 

 

Results 

Explained Variance (R²): The model explained approximately 34.95% of the variance in EGM expenditure per capita. 
Performance Consistency: Variability in R² scores across cross-validation folds indicated potential influences from unmeasured factors such as accessibility, cultural attitudes, and policy differences. 
Error Analysis: Higher errors in some folds highlighted the necessity for incorporating additional data or exploring alternative modeling techniques to better capture the complexity of gambling behaviors. 

Model Performance Insights with Visuals 

 

 

Figure 3.1.1: Residuals vs. Predicted Values: Scatter plot of residuals against predicted values across all folds, with a red line indicating zero residuals. 

 

Residual analysis shows that the model performs reasonably well, with most residuals centered around zero, indicating no major bias in predictions. However, the slight right skew and a few large outliers, particularly one near 1000, suggest the model overestimates in certain cases. 
	

 

Figure 3.1.2: Relationship between the Number of Features and Mean R² Score in a Predictive Model. 

 

The feature selection graph shows that the model’s performance, measured by an R² score of around 0.35, improves significantly with the first five features. Adding more features beyond this point offers diminishing returns, slightly lowering performance, which suggests the model benefits most from a small, optimal set of features. 

 

 

Figure 3.1.3: Feature Importance Across Folds: Box plot of feature importance scores across 5 cross-validation folds. 

 

The mean feature importance values show the relative influence of different predictors on EGM expenditure. 'Personal income <$400/week, persons' is the most influential predictor, highlighting its strong role in the model. '2012 ERP age 5-9, persons' has a moderate impact, while other features like 'Unemployed, persons,' 'Born in non-English speaking country, persons,' 'IRSD (avg),' and 'Female-headed lone parent families' also contribute meaningfully, albeit to a lesser extent. These results indicate that financial hardship and demographic factors are key in explaining variations in EGM expenditure. 

Model Performance with K-Fold Cross-Validation on Optimal Feature Set 

 

The Random Forest Regressor model was evaluated using K-Fold cross-validation on the optimal feature set. The results for each fold are as follows: 

Table 10:  

Fold 
	

R² 
	

MAE 
	

MSE 
	

RMSE 

Fold 1 
	

0.3624 
	

136.49 
	

24883.06 
	

157.74 

Fold 2 
	

0.5689 
	

104.92 
	

13938.18 
	

118.06 

Fold 3 
	

0.3797 
	

89.24 
	

10797.18 
	

103.91 

Fold 4 
	

0.3075 
	

143.42 
	

26142.9 
	

161.69 

Fold 5 
	

0.1288 
	

170.55 
	

96287.73 
	

310.3 

Mean 
	

0.34946 
	

128.924 
	

34409.81 
	

170.34 

 

Overall, the Random Forest Regressor achieved a mean R² of 0.3495 (±0.1412), indicating moderate predictive power with some variability across data splits. The mean absolute error (MAE) was 128.92, and the root mean squared error (RMSE) was 170.34, suggesting that the model's predictions, while generally accurate, show larger deviations in certain instances. These results imply potential for improvement through further feature selection or model tuning. 

 

Discussion and Interpretation 

 

Throughout the report the exploratory analysis revealed correlation between major socioeconomic factor and the 2012 electronic gambling expenditure. Using the 2012 population and Local Government Areas’ data certain factors such as: IRSD, Female-headed lone parent families, Personal income <$400/week accounted for a significant proportion of the gambling expenditure per capita in these areas. The results in Table 3, Figure 2.2.1, 2.2.2, 2.3.1, 2.3.2 illustrate the significance of limiting socioeconomic disadvantage due to its high nature in predicting gambling expenditure.  

Although simple correlation analysis identified IRSD as the most predictive variable and income under $400 per week as the least, the random forest methodology revealed different patterns, with income becoming the most meaningful data point. While correlation captures only linear relationships, random forests, as a supervised learning method, detect complex, non-linear interactions. It is crucial not to discount variables based on simple analysis alone for this reason, as underlying causal mechanisms may not be directly apparent. Therefore, policy actions aimed at combatting harmful gambling behaviour may require deeper analysis to determine whether the variables found to be predictive are genuinely causal and should be addressed from a policy perspective. 

Based on the multiple linear regression results(table6,7), we found some interesting patterns. The negative coefficient for IRSD (avg) shows that as areas become more financially stable, gambling losses per capita decrease. This mean that wealthier areas tend to lose less money on gambling. For female-headed lone parent families, the positive coefficients suggest that as the number of these families increases, gambling losses also go up, likely because these families face more financial and emotional stress. The most surprising result is the large negative coefficient for male-headed lone parent families (-126.47), which shows a big drop in gambling losses per capita. This could mean that male-headed lone parent families handle stress or financial pressure differently, or it might be because the small sample size or cultural differences in gambling behaviour between male and female headed households. 

 

Analysis of Predictors of EGM Expenditure with Respect to Feature Importance Distribution 

The figure 3.1.3 presents the feature importance distribution across five folds, highlighting key predictors of EGM expenditure. The strongest predictor, as seen in the boxplot, is personal income below $400 per week, suggesting that lower income levels are closely tied to higher EGM expenditures. This reflects financial stress as a driver of gambling behavior, potentially as a coping mechanism. The variation in feature importance for this variable across different model iterations is small, emphasizing its consistent impact. 

Unemployment rates and IRSD (Index of Relative Socioeconomic Disadvantage) also show substantial importance. These factors further reinforce the notion that socioeconomic disadvantage and limited economic opportunities are significant predictors of increased gambling expenditure. The consistent importance of these features across folds highlights the role of economic vulnerability in driving gambling behaviors. 

Interestingly, the graph also shows that the number of children aged 5-9 has moderate importance as a predictor, though it may not be immediately intuitive. This could be tied to household dynamics or stressors that affect adult behavior, necessitating further exploration of family structures and their indirect influence on gambling habits. 

Although features such as being born in a non-English speaking country and female-headed lone parent families are included in the model, their importance is notably lower and varies more significantly across different folds. This suggests that while these factors contribute to gambling expenditure, their impact is less consistent. 

The model, as demonstrated in the graph, explains about 35% of the variance in EGM expenditure, indicating that these predictors are meaningful but not exhaustive. Other unmeasured factors—such as mental health or proximity to gambling venues—likely play a substantial role in influencing gambling behaviors. 

Policy Implications 

The feature importance analysis reveals that addressing financial hardship should be a primary focus for reducing EGM expenditure, as "Personal income <$400/week" is the most significant predictor. Policies that enhance financial stability, such as expanding access to social support programs and job training, could significantly reduce gambling-related harm. Additionally, the high importance of "Unemployed persons" suggests that initiatives promoting job creation and employment security would be effective in curbing gambling behaviors. The moderate impact of having children aged 5-9 highlights the potential benefit of supporting families through childcare subsidies and family support programs, which could help reduce economic pressures. Although factors like "Born in a non-English speaking country" and "Female-headed lone parent families" show lower predictive power, they remain important considerations for targeted support to further reduce gambling expenditures among these vulnerable groups. By prioritizing interventions based on the influence of these socioeconomic factors, policies can be more strategic and impactful. 

 

Limitations and improvement opportunities 

 

    Limiting the analysis to LGAs and aligning only on 2012 data significantly reduced the number of data points. 

    Using Pearson correlation to select variables of interest may overlook non-linear relationships or relationships that become apparent when variables are combined. However, this limitation did not affect the results of the random forest approach. 

    Additional composite metrics, such as losses proportional to earnings, could have been explored. However, the available datasets did not support such analyses 

    Utilizing shapefiles could enhance the geographical breakdown of data, allowing for more precise segmentation of LGAs based on community boundaries. 

    Further test train split methods could be utilised to prove more meaningful model evaluations. 

 

Conclusion 

 

Throughout the report different data modelling and exploration methods are employed to predict gambling losses per capita using demographic data. Through a range of modelling techniques including k-means clustering, linear regression and random forest regressor models, we highlighted the complex relationships between different variables and gambling behaviour. The results show that factors such as income below $400 per week, lone parents’ families, and socioeconomic disadvantage were significant predictors of gambling expenditure.  

 

Although our models explained a moderate proportion of the variance in gambling losses, they highlighted the need for further exploration of the other unmeasured factors, such as mental health and proximity to gambling venues. The results suggest that helping people with financial problems through targeted policies could reduce gambling harm. However, adding more data and improving the models could make predictions more accurate and help us better understand what drives gambling behaviour.  

Task. For an oral presentation for a group, create a list of questions about the above to test the students knowledge on the spot. Try and relate the questions you ask to the research question at hand. Make sure to include each section equally within your questions. Do not write the answers