Comprehensive Analysis of Imported Goods into Canada in 2023 - Data Acquisition, Analysis, and Visualization In the project focused on Data Acquisition, Analysis, and Visualization, I undertook an in-depth examination of the goods imported into Canada in the year 2023. The primary objective was to derive valuable insights from the dataset through various statistical and analytical methods.

1. Report: Imports of Goods and Services,
quarterly (2022)
created by Mignesh Rajesh Birdi
2023

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Introduction and motive:
The purpose of this report is to analyze and provide insights into the quarterly trends of
imports ofgoods and services in Canada. The data was sourced to gain a comprehensive
understanding of theeconomic landscape, trade dynamics, and potential areas for strategic
decision-making.
Objectives:
1. Trend Analysis: To identify patterns and trends in the import of goods and services
over thequarters.
2. Sectoral Insights: To examine the performance of specific sectors contributing to
the overallimports.
3. Quarterly Comparisons: To compare and contrast the import figures across
differentquarters.
4. International Trade Impact: To assess the impact of international trade
conditions onCanada's import activities.
The rationale for Analysis:
1. Economic Decision-Making: The analysis aims to provide policymakers,
businesses, andstakeholders with valuable insights for informed economic
decision-making.
2. Risk Management: Understanding quarterly fluctuations can aid in
proactive riskmanagement for businesses involved in international trade.
3. Policy Formulation: The findings can contribute to the formulation of trade policies
that alignwith economic goals and global market dynamics.
4. Market Opportunities: Identifying growth sectors in imports can help businesses
explorenew market opportunities and optimize supply chain strategies.
In conclusion, this report serves as a crucial tool for understanding the nuances of Canada's
imports of goods and services, offering a foundation for strategic planning and fostering
economic resiliencein a dynamic global market.

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Q1 Q2 Q3 Q4
Mean 30866.71429 Mean 34081.57143 Mean 34598.71429 Mean 34202.57
Standard Error 4681.067968 Standard Error 5077.239535 Standard Error 5232.698564 Standard Error 5213.019
Median 19184 Median 21326 Median 20838 Median 19874
Mode 75596 Mode 84016 Mode 1244 Mode 90272
Standard Deviation 35029.90508 Standard Deviation 37994.58162 Standard Deviation 39157.93047 Standard Deviation 39010.66
Sample Variance 1227094250 Sample Variance 1443588233 Sample Variance 1533343519 Sample Variance 1.52E+09
Kurtosis 4.69494457 Kurtosis 4.591078692 Kurtosis 4.792725062 Kurtosis 4.760923
Skewness 2.074490956 Skewness 2.029854641 Skewness 2.075607142 Skewness 2.080848
Range 162788 Range 178388 Range 186636 Range 187248
Minimum 148 Minimum 184 Minimum 152 Minimum 116
Maximum 162936 Maximum 178572 Maximum 186788 Maximum 187364
Sum 1728536 Sum 1908568 Sum 1937528 Sum 1915344
Count 56 Count 56 Count 56 Count 56
Descriptive Statistics:
 lower standard error suggests that the sample mean is likely a good estimate of the
population mean.
 standard deviation measures the amount of variation or dispersion of a set of
values,higher standard deviation indicates greater variability.
 sample variance is the average of the squared differences from the mean.
 Kurtosis is a statistical measure that describes the shape of a probability
distribution,positive kurtosis indicates a more peaked distribution. Our kurtosis 4.69,
suggesting a distribution with heavy tails.
 Skewness measures the asymmetry of a distribution, a positive skewness indicates a
distribution that is skewed to the right.

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Total goods and services
968,764
954,284 957,672
864,268
Q1 2022 Q2 2022 Q3 2022 Q4 2022
Data Visualization:
Estimates
Q1
2022
Q2
2022
Q3
2022
Q4
2022
Industrial machinery, equipment and
parts 75,596 84,016 88,388 90,272
Quarterly Trends in Industrial Machinery, Equipment, and Parts (Q1 2022 -
Industrial machinery, equipment and parts
92,000
90,000
88,000
86,000
84,000
82,000
80,000
78,000
76,000
74,000
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
Estimates Q1 2022 Q2 2022 Q3 2022 Q4 2022
Total goods and
services 8,64,268 9,54,284 9,68,764 9,57,672
Quarterly Trends in Total Goods and Services (Q1 2022 - Q4 2022)
90, 72
88, 88
84, 16
75, 96

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CONSUMER GOODS
164000
162000
160000
158000
156000
154000
152000
150000
148000
0 0. 5 1 1.5 2 2.5 3 3.5 4 4. 5
Estimates
Q1
2022
Q2
2022
Q3
2022
Q4
2022
Consumer
goods 148900 160316 160948 155000
Quarterly Trends in Consumer Goods (Q1 2022 - Q4 2022)
• Q1 2022: The quarter begins with a substantial value of 148,900 units of
consumer goods.
• Q2 2022: There is a noticeable increase, with the value rising to 160,316 units,
indicating a positive growth or demand for consumer goods in the second
quarter.
• Q3 2022: The upward trend continues, reaching 160,948 units, suggesting a
sustained demand or potentially a seasonal peak for consumer goods.
 Q4 2022: The trend shifts slightly, with the value decreasing to 155,000 units.
This might indicate a seasonal dip or a temporary reduction in demand for
consumer goods in the last quarter

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Chart Title
4 123,716
3 121,572
2 114,008
1 99,764
0 20,000 40,000 60,000 80,000 100,000 120,000 140,000
Estimates
Q1
2022 Q2 2022 Q3 2022 Q4 2022
Motor vehicles and
parts 99,764 1,14,008 1,21,572 1,23,716
• Q1 2022: The first quarter starts with a significant value of 99,764 units of
motor vehicles and parts, indicating a substantial presence in the market during
the beginning of the year.
• Q2 2022: There is a substantial increase in the second quarter, with the value
soaring to 114,008 units. This surge suggests a notable spike in demand or
production during this period.
• Q3 2022: The upward trend continues, reaching 121,572 units. This sustained
growth might be indicative of a strong market demand for motor vehicles and
related parts during this quarter.
• Q4 2022: The trend persists with a further increase to 123,716 units in the
fourth quarter. This could be a continuation of the positive market conditions
observed in the previous quarters.
Regression Statistic:
Multiple R: The multiple correlation coefficient, a measure of the strength and direction of the
linear relationship between the independent variable(s) and the dependent variable. In this case, it's
approximately 0.997, indicating a very strong positive correlation.
R Square (R²): The coefficient of determination expresses the percentage of the dependent
variable's variation that can be predicted based on the independent variable (or variables). In this
case, it is around 0.994, indicating that the independent variable(s) accounts for 99.4% of the
variability in the dependent variable.
Adjusted R Square: This adjusts the R² for the number of predictors in the model. It's close to R²,
indicating a good fit.

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Standard Error: An approximation of the errors' standard deviation, or the average difference
between the observed and anticipated values. In this instance, it comes to around 2.66.
ANOVA (Analysis of Variance):
Regression and Residual Sum of Squares (SS): The F-statistic, which is a ratio of the
variance explained by the model to the variance not explained, is calculated using these data. The F-
statistic (325.97) indicates that the regression is statistically significant.
Significance F: The p-value for the F-statistic. A low p-value (0.003) suggests that the overall
model is significant.
Coefficients:
Intercept: The regression equation's y-intercept is roughly -483.67.
Plastic and rubber products: This variable's coefficient is roughly 0.0265. This is the change in
the dependent variable because of a one-unit change in the independent variable.
Statistics on Coefficients:
T-statistic: The t-statistic is a measurement of how many standard deviations the coefficient is from
zero. Greater absolute values suggest more evidence against the null hypothesis of no impact.
P-value: The likelihood that the coefficient is zero. A p-value of less than 0.003 indicates that the
variable is a significant predictor.
95% CI: The range within which we are 95% positive that the real coefficient exists.
Output Residue:
Observation: A collection of data points or observations.
Predicted Plastic and rubber waste and scrap: The values predicted by the regression
model.
Residuals: Differences between actual and expected values are referred to as residuals.
Standard Residuals: Residuals are standardized such that they have a mean of 0 and a standard
deviation of 1.
Probability Output:
Percentile: The location of each observation in the projected variable's cumulative distribution.
Plastic and rubber waste: The actual values of the dependent variable.

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Normal Probability Plot
184
200
150
100
50
0
0 20 40 60 80 100
Sample Percentile
116 148 152
SUMMARY OUTPUT Plastic
and
rubber
products
Waste and
scrapof
plastic and
rubber
Regression Statistics
Multiple R 0.9969463
23,672 148
R Square 0.9939019
25,176 184
Adjusted R
Square 0.9908528
23,988 152
22,640 116
Standard Error 2.659671
Observations 4
ANOVA
df SS MS F
Significance
F
Regression 1 2305.8523 2305.8523 325.96851 0.0030537
Residual 2 14.1477 7.07385
Total 3 2320
Coeffici
ents
Standa
rd
Error
t Stat P-value Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept - 35.122 - 0.0052 - - - -
483.674 837 13.770 318 634.79 332.55 634.79 332.55
926 536 263 536 263
Plastic and rubber 0.02654 0.0014 18.054 0.0030 0.0202 0.0328 0.0202 0.0328
products 8 704 598 537 212 747 212 747
RESIDUAL OUTPUT PROBABILITY OUTPUT
Observationand scrap of Residuals ndard Residuals Percentile crap of plastic and rubber
1 144.77005 3.2299542 1.4873531 12.5 116
2 184.69822 -0.6982241 -0.3215234 37.5 148
3 153.15921 -1.1592109 -0.533802 62.5 152
4 117.37252 -1.3725191 -0.6320277 87.5 184
ste
and
scrap
of
plastic
and
rubber

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Summary:
The regression model has a high level of significance.
Plastic and rubber products" are an independent variable that has a
considerable positive influence on the dependent variable.
The model explains a significant portion of the variation in the dependent
variable.
Plastic and rubber products Line Fit Plot
200
180
160
140
120
100
80
60
40
20
0
22500
Waste and scrap of plastic and
rubber
Predicted Waste and scrap of
plastic and rubber
23000 23500 24000 24500 25000 25500
Plastic and rubber products
Plastic and rubber products Residual Plot
4
3
2
1
0
22500
-1
23000 23500 24000 24500 25000 25500
-2
Plastic and rubber products
Residuals
Waste
and
scrap
of
plastic
and
rubber

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Commercial services
111,000 110,564
110,000
109,108
109,000
108,280
108,000
107,356
107,000
106,000
105,000
1 2 3 4
Motor vehicles and parts
140000
120000
100000
80000
60000
40000
20000
0
1 2 3 4
The residuals (the disparities between the observed and predicted values) are
minimal.
This result indicates that the independent and dependent variables have a
strong linear connection. However, it is critical to evaluate the data context as
well as the linear regression model's assumptions.
o Statistical Inference:
Quarterly trends in commercial services (Q1 2022- Q4 2022)
Quarterly trends in Motor Vehicles and parts (Q1 2022 - Q4 2022)

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Chart Title
100%
80%
60%
40%
20%
0%
90272 90272 Forestry products and building and packaging materials
90272 90272 Industrial machinery, equipment and parts
Hypothesis t-Test: Paired Two Sample for Means
Industrial machinery,
equipment and parts
Forestry products and building and
packaging materials
Mean 84568 35657
Variance 42643168 2522831
Observations 4 4
Pearson Correlation 0.874693
Hypothesized Mean
Difference 0
df 3
t Stat 18.8185
P(T<=t) one-tail 0.000164
t Critical one-tail 2.353363
P(T<=t) two-tail 0.000328
t Critical two-tail 3.182446
There is a significant positive association between the two groups, as indicated by the Pearson
correlation of roughly 0.87.
There appears to be a statistically significant difference between the means of the two groups based
on the incredibly low p-values (one-tail and two-tail).
Given the high positive association, it appears that the two categories tend to rise in tandem with
each other's growth.

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Chart Title
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Crude oil and crude bitumen Natural gas, natural gas liquids and related
products
Natural gas, natural gas liquids and related products
Crude oil and crude bitumen
Correlation analysis : Crude oil vs Natural Gas
The correlation coefficient of 0.599 indicates a moderate positive relationship.
A value that is closer to one indicates that, while not exactly, the other variable tends to increase as
the first one does.
Conclusion:
The positive correlation suggests a moderate relationship between changes in natural gas and
related products and changes in the production or consumption of crude oil.
Exponential Smoothing:
Conclusion:
To forecast the values of all the goods and services.
Interpretation:
The weight assigned to recent observations is influenced by the smoothing value (α).
The process of exponential smoothing begins with the original forecast.
Conclusion:
All goods and services are forecasted by the exponential smoothing model, which aids in
predicting future trends.

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Graphical representation of Exponential Smoothing:
Exponential Smoothing
900000
800000
700000
600000
500000
400000 Actual
Forecast
300000
200000
100000
0
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55
Data Point
Value

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SPREADSHEET MODELLING:
The purpose for creating this spreadsheet model is to predict imports for each quarter in 2023. we
derived an estimated growth rate from previous years to inform this forecast. The intention is to gain
insights into the expected trends in imports across various categories, enabling strategic planning
and decision-making. Leveraging historical data, we have established a growth pattern and applied it
to the last quarter of 2022 to project imports throughout the upcoming year.
Estimated growth rate FORECAST MODEL( 2022* growth rate)
Q1 Q2 Q3 Q4 Q1 2023 Q2 2023 Q3 2023 Q4 2023
0.227126 0.280561 0.152604 0.05802 33574.17 36916.03 31931.75 29171.73
0.190395 0.248512 0.136273 0.026593 26555.33 29330.04 25770.66 22827.32
0.420697 0.443723 0.232581 0.212727 7177.363 7703.706 6192.487 6471.113
0.595434 0.926323 0.577712 0.384972 68227.12 107982 83984.76 74439.48
0.646455 0.632448 0.390858 0.218235 33798.43 38107.86 30131.54 24481.65
0.589989 1.616842 0.811615 0.375 9495.416 26021.88 18536.44 12100
0 0.925 0.930851 1.229947 916 2371.6 2803.596 3719.551
0.014563 0.414986 0.27459 0.296296 1696.35 2779.032 1585.59 2903.704
0.831776 0.693431 0.316456 0.398268 1436.112 1571.504 1095.291 1806.563
0.698998 1.2132 0.753446 0.570652 21903.49 41262.91 31225.37 30866.46
0.163922 0.379143 0.410886 0.100922 21718.79 26628.49 28234.64 21040.81
0.167733 0.385657 0.428745 0.089312 20454.02 24952.91 26814.69 19450.75

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