Canada Freight Analysis - Analysis of Mode of Transports between different provinces in Canada between 2011 - 2017
Findings -
- Total Number of Shipments each year
- Most common modes of transportation
- Commodities shipped most frequently
- Avg weight of shipments per year
- Total Revenue generated each year
- Avg distance travelled by shipments each year
- Trend between origin city and destination city - year wise
- Statistical relation between (Distance and Shipment Value), (Shipment and Revenue), Revenue and (Shipment and WeightKg),
Shipment value and (Shipment,WeightKg, Distance, Revenue, TonneKm )
Conclusion -
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Ontario is the most popular Origin and Destination province in Canada.
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Trucks are the most common mode of transportation
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Food, Forest Products, Base metals and articles of base metal are the most common commodities w.r.t total shipments.
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Shipment vs Revenue ------ The regression analysis suggests that there is a statistically significant positive relationship between shipments and revenue. On average, an increase in shipments is associated with an increase in revenue. Correlation coefficient --- 0.64, R Square --- 0.41
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Distance vs Shipment Value --- Correlation coefficient --- 0.368, R Square Value - 0.135 The regression analysis suggests that there is a statistically significant positive relationship between distance and shipment value. On average, an increase in distance is associated with an increase in shipment value.
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Relation b/w Shipment value and (Shipment,WeightKg, Distance, Revenue, TonneKm )
Correlation coefficient - 0.779, R Square value - 0.60
Coefficients
Intercept 11306730.1 X Variable 1 24169.45141 X Variable 2 -0.559483249 X Variable 3 -11.17552351 X Variable 4 40.89071035 X Variable 5 -0.650916327
X Variable 1 (Shipment): - Indicates that, on average, each additional unit of shipment is associated with an increase in shipment value of approximately $24,169.45.
X Variable 2 (WeightKg): - suggests that, on average, each additional unit of weight (in kilograms) is associated with a decrease in shipment value by approximately $0.559.
X Variable 3 (Distance): - suggests that, on average, each additional unit of distance is associated with a decrease in shipment value by approximately $11.18.
X Variable 4 (Revenue): - indicates that, on average, each additional unit of revenue is associated with an increase in shipment value of approximately $40.89.
X Variable 5 (TonneKm): suggests that, on average, each additional unit of TonneKm is associated with a decrease in shipment value by approximately $0.651.
The multiple linear regression model suggests that the combination of Shipment, WeightKg, Distance, Revenue, and TonneKm
significantly predicts shipment value. The model explains a substantial portion (60.7%) of the variability in shipment value. Each coefficient represents the estimated change in shipment value for a one-unit change in the corresponding independent variable, holding other variables constant.