Month: November 2023

Here is the analysis:

ADF Test: Total Jobs
The plot shows the time series data for “total_jobs.”
The ADF statistic is positive, and the p-value is very high (0.9475), indicating strong evidence that the series is non-stationary.

ADF Test: Unemp Rate
This is the time series data for “unemp_rate.”
The ADF statistic is negative, but the p-value is not below the common threshold of 0.05 (0.4789), suggesting the series is likely non-stationary.

ADF Test: Logan Passengers
This plot represents “logan_passengers” over time.
The ADF statistic is positive, and the p-value is extremely high (0.9853), indicating that the series is non- stationary.

ADF Test: Logan Intl Flights
The time series data for “logan_intl_flights” is shown.
The ADF statistic is negative, and the p-value is 0.2306, which is above the 0.05 threshold, suggesting non- stationarity.

ADF Test: Hotel Occup Rate
The plot displays the “hotel_occup_rate” time series.
The ADF statistic is negative, with a p-value of 0.4359, again indicating non-stationarity as the p-value is above 0.05.

ADF Test: Hotel Avg Daily Rate
This plot shows the “hotel_avg_daily_rate” time series.
The ADF statistic is negative, and the p-value is very low (0.0058), suggesting that the series is stationary.

ADF Test: Labor Force Participation Rate
This plot shows the “Labor_Force_Part_Rate” time series.
The ADF statistic value is positive, with a p-value (0.9691). With a p-value significantly greater than the common threshold of 0.05, the test suggests that the series is non-stationary.

For the ADF test, a p-value below a threshold (commonly 0.05) indicates stationarity, meaning there is no unit root present in the time series. A non-stationary time series is characterized by a changing mean or variance over time, which can be problematic for many types of time series analysis, including forecasting.

Total Jobs vs Hotel Average Daily Rate:
The plot suggests a concentration of points where the average daily hotel rate is around $250, with the highest job numbers.
This may indicate that when hotel rates are at a moderate level, it is correlated with higher employment, possibly due to balanced tourism or business travel activities.

Total Jobs vs Hotel Occupancy Rate:
The highest density is observed at occupancy rates between 0.7 and 0.9, which could suggest a positive association with total jobs.
This pattern implies that higher hotel occupancy rates, potentially indicating higher tourist or business activity, might correspond with higher employment levels.

Total Jobs vs Unemployment Rate:
The density is elongated and negatively sloped, indicating an inverse relationship between the unemployment rate and total jobs, which is expected.
As the unemployment rate decreases, the total number of jobs tends to increase.

Total Jobs vs Labor Force Participation Rate:
The plot shows a slight positive trend, with higher job numbers corresponding to a labor force participation rate mainly between 0.63 and 0.67.
This could imply that as more people participate in the labor force, it is indicative of a stronger job market.

Total Jobs vs Logan International Flights:
The density suggests a positive relationship, with a greater number of jobs associated with an increased number of international flights.
This may reflect the impact of international travel on local employment, particularly in sectors linked to travel, tourism, and possibly international business.

Total Jobs vs Logan Passengers:
Like international flights, there is a positive correlation with the number of passengers.
The highest density of job numbers coincides with passenger numbers around 3 million, indicating that air travel volume may positively influence employment figures.

Total Jobs vs Logan Passengers:
There is a positive relationship between the number of passengers at Logan Airport and the total number of jobs. The R-Squared value is approximately 0.729, suggesting that about 72.9% of the variability in total jobs can be explained by the number of Logan passengers. The p-value is extremely low (approximately 3.57×10-15), indicating a statistically significant relationship.

Total Jobs vs Logan International Flights:
Similarly, the number of international flights has a positive correlation with the total number of jobs. The R-Squared value is 0.764, meaning that approximately 76.4% of the variability in total jobs is accounted for by the number of international flights. The p-value is very small (around 3.04×10-17), which implies a statistically significant relationship.

Total Jobs vs Hotel Occupancy Rate:
The relationship between hotel occupancy rates and total jobs is weaker compared to the previous two variables. The R-Squared value is about 0.142, indicating that only 14.2% of the variability in total jobs is explained by the hotel occupancy rate. The p-value is approximately 0.197, which is above the typical significance level of 0.05, suggesting that the relationship might not be statistically significant.

Total Jobs vs Hotel Average Daily Rate:
There is a moderate positive relationship between the average daily rate of hotels and total jobs. The R-Squared value is 0.313, which means that about 31.3% of the variability in total jobs can be explained by the hotel average daily rate. The p-value is approximately 0.0038, indicating a statistically significant relationship at common significance levels.

Total Jobs vs Unemployment Rate:
There is a strong negative relationship between the unemployment rate and the total number of jobs, which is intuitive as higher unemployment would typically be associated with fewer jobs. The R-Squared value is about 0.872, suggesting that 87.2% of the variability in total jobs can be explained by the unemployment rate. The p-value is extremely low (around 4.10 x 10-27), indicating a very strong statistically significant relationship.

R-squared (0.9564): This indicates a very high proportion of variance in the dependent variable (total jobs) is predictable from the independent variables in the model.
Adjusted R-squared (0.9213): This is a modified version of R-squared adjusted for the number of predictors in the model, still indicating a good fit.
MAE (Mean Absolute Error): The average absolute error of the predictions is 3889 jobs.
MSE (Mean Squared Error): The average squared difference between the estimated values and the actual value is 2,229,292.5, a measure that gives higher weight to larger errors.
RMSE (Root Mean Squared Error): The square root of MSE, which is 4708 jobs, gives an idea of the magnitude of the errors in the same units as the dependent variable (total jobs).

Hotel Average Daily Rate: Shows a distribution with a clear peak, which may indicate a common average daily rate around which hotel prices are centered. The spread of the plot might suggest variations in pricing, which could reflect different hotel categories or seasonal pricing strategies.

Hotel Occupancy Rate: Has a unimodal distribution, perhaps indicating that most hotels maintain a consistent occupancy rate, with fewer occurrences of very low or very high occupancy. This could reflect a stable demand for accommodation in Boston.

Labor Force Participation Rate: Shows a tight distribution, indicating that the participation rate does not fluctuate widely and remains relatively stable over time.

Logan International Flights: Displays the distribution of the number of international flights at Logan Airport. A unimodal, possibly slightly skewed distribution would suggest that there’s a common range of flight numbers, with occasional periods of increased or decreased international traffic.

Logan Passengers: Shows a distribution potentially skewed to one side, indicating variability in passenger numbers. Peaks could correspond to high-travel seasons or specific events that attract more travelers.

Total Jobs: Appears to illustrate the distribution of total job numbers in Boston. The distribution might be relatively broad, indicating variability in employment numbers, which could be influenced by economic cycles, job market health, and seasonal employment trends.

Unemployment Rate: Seems to have a pronounced peak, suggesting the most common unemployment rate that the city experiences. A narrower peak could indicate a relatively stable unemployment rate over the period analyzed.

Logan Passengers: Shows the frequency distribution of the number of passengers traveling through Logan Airport. The distribution seems to be skewed to the right, indicating that there are days with exceptionally high passenger numbers, possibly during peak travel seasons or special events.

Logan Intl Flights: The histogram for international flights also appears to be right-skewed, suggesting that while there is a consistent average number of flights, there are periods with significantly higher international traffic.

Hotel Occupancy Rate: Shows a potential left-skewed distribution, indicating that there are fewer instances of low occupancy rates and a tendency for higher occupancy on most days.

Hotel Avg Daily Rate: Exhibits a somewhat uniform distribution with several peaks, suggesting that there are common price points at which hotels set their daily rates, with fluctuations around these points.

Total Jobs: Looks to be normally distributed with a slight right skew, implying that most days have a consistent number of jobs, with occasional peaks possibly due to seasonal employment or economic growth.

Unemployment Rate: Shows a right-skewed distribution, indicating that lower unemployment rates are more common, with fewer occurrences of higher rates.

Labor Force Participation Rate: Appears somewhat normally distributed, suggesting that the labor force participation rate in Boston remains relatively stable over time.