Relationship Between Population and Aircraft Production

This assignment examines the relationship between population and aircraft production. Students will use statistical analysis to model this connection, focusing on a regression analysis with 'population' as a predictor variable for 'aircraft'. The output includes coefficients, significance levels, and an R-squared value to assess the strength of the model. Students are expected to interpret these results and draw conclusions about the influence of population on aircraft production.

Running Head: PROFESSIONAL PRACTICE
Professional Practice
The Impact on Population Growth on Trasporation System of New South Wales
Name of the Student
Name of the University
Author note

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Executive Summary
The report intends to evaluate the imapct of population density on the trasportation system of
New South Wales. There are three specific research questions that the paper focuses on. The
main question is how traport system of New South Wales are affected from high population
density. Additioanly, the paper tries to find how different mode of trasporation and construction
of new roads are influenced from growing population. Four mode of transportation are used
namely, rail, bus, cars and aircraft.
There are several reserches that have already undertaken in this field. Some of these paper are
based on quantitative analysis while other are based on quantitative analysis. The previous
papers conduct research on how people are affected from crowding in the trasportation. With
crowing in vehicles, people suffer from several discomfort. With increasing population road
congestion in likely to increase. People have to wait long for vehicles. Keeping in mind this
background, the paper conducts research on NSW trasportation.
Relavent data on population and transporation is collected from official websites. The data is
analyzed to find answer to the specific reserch question. It is found that with increasing
popualtion in NSW, use of all the four modes of trasportation increases. More roads are
constructed to avoid congestion.
Table of Contents
1. Introduction..................................................................................................................................3
1.1 Research Statement................................................................................................................3
1.2 Research aim and Research Question....................................................................................4
2. Literature Review........................................................................................................................4
3. Methodology................................................................................................................................9

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3.1 Data Collection......................................................................................................................9
3.2 Data Analysis.......................................................................................................................10
4. Findings and Analysis................................................................................................................11
4.1. Findings on congestion level and peak hours in NSW.......................................................11
4.2 Descriptive Statistics...........................................................................................................13
4.3 Relation between population and transportation.................................................................15
4.4 Population and different mode of transportation.................................................................15
4.5 Regression Analysis.............................................................................................................16
4.6 Population and Length of road............................................................................................19
5. Discussion..................................................................................................................................20
5.1 Population growth is related with transportation.................................................................20
5.2 Relation between different mode of transportation and population growth........................20
5.3 Road lengths are related with population............................................................................21
6. Conclusion.................................................................................................................................21
6.1 Addressing aim and objective of the research.....................................................................21
6.2. Limitation...........................................................................................................................22
6.3. Recommendation................................................................................................................22
7. Reference list.............................................................................................................................23
8. Appendix....................................................................................................................................30
1. Introduction
In the world of globalization, countries have become increasingly interconnected. In
building a strong inter-country relationship transport and communication system play an
important role. Now a day, different modes of transportation is being used. People travel to their
desired destination by road, water or air (Neirotti et al., 2014). Both private and public sector
have now come forward to invest in transportation. Means of transportation being a part of
infrastructural development has a vital role in economic growth of a nation (Meyer & Shaheen,
2017). With growing population, traffic congestion has become a major problem worldwide
(Mayer & Trevien, 2017).

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1.1 Research Statement
The problem of traffic congestion in Australia is going worst day by day. People in
popular capital cities in Australia especially Sydney and Melbourne face trouble of congestion in
rush hours (Boyacı, Zografos, & Geroliminis, 2015). During peak hours, it takes more than
double time for commuters to reach office, schools and to other important places. New South
Wales is one of the most populated State of Australia. Around one third of Australia’s total
population live in New South Wales (Chaturvedi & Kim, 2015). The estimated population of
New South Wales during 2015-2016 is 7.7 million (Bugheanu, A. M., & Colesca, S. E. (2016).
With growing population, the state is likely to face severe traffic congestion. The research paper
aims at finding the impact of population growth on transportation system of New South Wales.
1.2 Research aim and Research Question
The aim of the research paper is to evaluate the relationship between transport system and
population growth overtime. The specific research questions are as follows
 How the transport system of NSW is crowded due to high density of population?
 How the mode of transportation varies with population growth?
 How construction of new road is related with growth of population?
2. Literature Review
In connection to growing concern for impact of population on transportation system
several scholarly research have already been undertaken in this field. In a paper formulated by
Kroes et al., 2013 focus is given on finding the perceived value of overcrowding in public
transport vehicles in France. The paper has made a qualitative research analysis to derive
perceived value in terms of their physical comfort (Kroes et al., 2013). Passengers are classified

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into 4 groups- Type 1 contains passengers who fear closeness to other passengers, Type 2; this
group contains people who enjoy traveling a time of their own, Type 3; those who wait for
saving time and finally Type 4; those acting as individualist. The result of the paper reveals a
clear distinction of preferences to avoid crowding out among the four groups. Other papers as
well (Lam, Leung & Chu, 2016) support the results.
Another study developed by Haywood et al., 2017 concentrates on crowding out of
public transport vehicles. The paper has twin objectives. First is to analyze the discomfort in
public transport with varying density level and across heterogeneous groups. Second is to find
the reason for this effect of discomfort. The level of discomfort is analyzed using three different
perspectives- dissatisfaction arising from not getting seat and for long standing hours, discomfort
due to having less opportunity to utilize time at times of journey and finally uneasiness from
being physically close in the crowded vehicles (Haywood, Koning & Monchambert, 2017). The
paper concerns with interaction between in vehicle density (IVD) and crowding discomfort
(CDD). These seem to be significant and negative for discomfort measured from standing,
Wasted time and over closeness (Clifton & Mulley, 2016). Passengers having high level of
dissatisfaction in any of these three discomfort have greater disutility from crowing effect (Lam,
Taghia & Katupitiya, 2016). The results show Standing and Time Wasted from it are the most
important aspect affecting satisfaction (Geertman et al., 2017). This is followed by the problem
of over closeness. It is found that people have high disutility in times of high density followed
from long standing during their journey (Pearce, 2017).). The disutility arises from the not
getting scope for utilizing time while traveling and physical closeness to other passengers.
A similar research is conducted by Tiranchi, Hensher and Rose in their paper in 2016.
The effect of passengers’ density is different at bus stops, rail stations and inside trains and

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buses. The single paper researches on multiple dimension relating to long waiting time,
reliability of travel time, well-being of passengers, importance of time saving in vehicles, choice
of travelling route, size of vehicles and its fare (Tirachini, Hensher & Rose, 2013). The
externalities from crowding in vehicles are for bus and rai service in Sydney are estimated to
capture the impact of crowding on prediction of demand and in-vehicle time saving. A regression
estimation is made to find out how crowding impacted passengers in times of alighting and
boarding. The high level of occupancy raises the probability of vehicle circulation (Philip et al.,
2014). This causes people to wait for a long time in stations and bus stops increasing the waiting
time and variability of travel time (Hendrigan & Newman, 2017). While coming to impacts of
health and well-being the paper documented an increase in anxiety, exhaustion, stress level with
perceived risk for personal safety and interruption of privacy. These factors contribute to a
negative valuation for the resulting from high occupancy of passengers at stations and stops
(Chen, Gong & Paaswell, 2012). The passengers’ choice is likely to be affected from competing
routes and unbalanced loads of vehicles (Gharehbaghi & Georgy, 2015). The externality arises
from crowding increases marginal cost for travelling. This external effect should be taken into
consideration for designing the system of public transport. The frequency for vehicles
availability, its size and fare are subjected to the impact of population growth on vehicles
(Glazier et al., 2014).
The research paper developed by Li et al. in 2016, studies traveler’s mode of choice in
the presence of a multimodal network. The paper conducts a comprehensive study on perceived
behavioral differences based on a travel time reliability (TTR) model with considering crowding
out for different transport modes with a multimodal network. A survey of passengers’ is
conducted at two stage including a combined mode of travel with park and ride in reference to

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four major factors ( Li, Gao & Tu, 2016). In the first stage, the perceived travel time reliability in
obtained with respect to delays at different transportation mode (Li, Burke & Dodson, 2017).
After obtaining perceived TTR, in the next stage the paper designs an experimental analysis
using balance theory of utility for the stated preference survey to examine mode of choice
behavior and the difference in passengers’ perception in TTR and crowding (Chen, Gong &
Paaswell, 2012). The results obtained from the first stage survey revealed that the travelers have
differential tolerance level regarding the span of accepted delays for different modes (Saberi et
al., 2017). The perceived TTR of travelers measured in relation to a certain delay differs by
transportation mode (Ricciardi, Xia & Currie, 2015). The survey results from the second stage
further confirms the result of perceived difference obtained from the first stage survey (Grinin &
Korotayev, 2018). TTR based on different modes greatly influences choice behavior and hence
should be taken for consideration in times of forecasting models used for analyzing mode in a
multimodal network (Blanco Ramírez, 2016).
There are studies that found some contrasting results on effect of population growth on
mode of transportation and satisfaction. According to Olsen, Macdonald and Ellaway, 2017 the
effect of population growth on trasportation is not uniform. That is it is not true that population
growth always have detremental effect on satisfaction and transportation (Olsen, Macdonald &
Ellaway, 2017). Studies aim at finding how overtime satisfaction changes with mode of
trasportation. It is found that satisfaction level of two third of the population increases with their
mode of transporatation (Boyacı, Zografos & Geroliminis, 2015). The unsatisfied people are
those who has poor health condition. Access to car was related with satisfaction level of
transportation (Yao & Yang, 2012). The deprivation or dissatisfaction offsets when adjusted for
car access to household. When population from 1997 to 2010 are considered, the results shows

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most of the retired individuals travelled by public transport with satisfaction (Bugheanu &
Colesca, 2016). However, those using private car are relatively less satisfied following increased
consumption level.
Based on the theoretical backgroud and research objectives following hypotheses are framed
Hypothesis 1
H01: There is no significant relation between railway trasporation and population growth
HA1: Significant relation exist between railway transpportation and population growth.
Hypothesis 2
H02: There is no significant relation between bus transportation and growth of population.
HA2: Population growth significantly influences bus transportation
Hypothesis 3
H03: There is no significant relation between aircraft travel and growth of population.
HA3: Population growth significantly influences aircraft travel and population growth
Hypothesis 4
H04: There is no significant relation between private car transportation and growth of
population.
HA4: A significant relation exists car transportation and population growth

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3. Methodology
3.1 Data Collection
Data on population and different mode of transportation are collected along with length
of road and safety measures. The timeframe considered are varied from 2009 to 2016. All the
relevant data are collected for New South Wales, one if the major capital city of Australia. The
data on population is obtained from “Australian bureau of statistics’. The data related to
transportation system are collected from ‘NSW and Sydney Transportation Facts’.
3.2 Data Analysis
The paper has conducted a quantitative research analysis. The obtained data are analyzed
with statistical software namely SPSS. The trend in population growth is considered by
constructing a line graph with respect to time. The description statistics is estimated to have an
overall idea about the mean, variability, range of the data set for the chosen timeframe (Golub &
Martens, 2014). As far as mode of transportation is concerned, transportation via Rail and Bus
routes are considered. The length of roads are considered for different time to find whether
population growth significantly influence length of roads. Finally, data on road, rail and aviation
accidents are considered to analyze the safety status.

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4. Findings and Analysis
4.1. Findings on congestion level and peak hours in NSW
Chart 1: History of congestion level in NSW
The congestion history in New South Wales is presented in the above figure. In 2008, the
congestion level is below 20%. From 2009 onwards, the congestion level started to increase and
remain at the level 20% until 2015. Between 2015 and 2016, the congestion level grows up
slightly (Parolin & Rostami, 2017).

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Char 2: Evening Peak Hours in NSW
The evening peak rate for different days of the week are considered. The peak rate for
Monday and Tuesday is almost same (Pettit et al., 2017) . Evening is high in Wednesday and
Thursday. Evening peak in Friday is less as compared to that is Thursday.
Chart 3: Morning Peak in NSW
The morning peak is greater as compared to the evening peak. Because of rush hours for
schools and offices morning peak is higher. The peak rate is maximum for Wednesday. For

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Tuesday and Thursday, peak rate is around that in Wednesday. For Monday and Friday, peak
rate is lower as compared to three other days but still higher than evening peak.
4.2 Descriptive Statistics
Table 1: Descriptive Statistics of Population
Population
N Valid 8
Missing 0
Mean 7419350.00
Median 7378750.00
Std. Deviation 205955.719
Variance 4241775839
2.857
Skewness .337
Std. Error of Skewness .752
Kurtosis -.807
Std. Error of Kurtosis 1.481
Range 613175
Minimum 7133275
Maximum 7746450
Percentiles
25 7261393.75
50 7378750.00
75 7607387.50

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The average population for the chosen time is 7419350. The population has reached to its
maximum level in 2016 with population being 7746450.The population in New South Wales has
constituted an upward trend overtime.
Table 2: Descriptive Statistics of mode of transportation
Statistics
Rail Bus Aircraft Cars
N Valid 8 8 8 8
Missing 0 0 0 0
Mean 3293.88 1184.00 924.25 46645.50
Std. Error of Mean 96.499 42.399 44.574 974.573
Median 3320.00 1187.50 896.25 46743.50
Mode 2803a 998a 770a 41806a
Std. Deviation 272.941 119.923 126.074 2756.510
Variance 74496.982 14381.429 15894.723 7598346.286
Skewness -.606 -.419 .647 -.400
Std. Error of Skewness .752 .752 .752 .752
Kurtosis .166 -.714 -.308 .163
Std. Error of Kurtosis 1.481 1.481 1.481 1.481
Range 859 335 379 8828
Minimum 2803 998 770 41806
Maximum 3662 1333 1149 50634
Sum 26351 9472 7394 373164
Percentiles
25 3102.50 1069.00 828.98 44827.25
50 3320.00 1187.50 896.25 46743.50
75 3494.50 1299.50 1008.35 48681.00
a. Multiple modes exist. The smallest value is shown
The average number of visitors in rail transportation in the last 8 years is 3294. The same
for bus, aircraft and cars transportation are 1184, 924 and 46645 respectively. The standard
deviation in rail transportation is 272.941, for bus it is 119.923 and for aircraft and bus standard
deviation is 126.074 and 2756.510 respectively. The standard deviation are lower than mean
implying less variability in mode of transportation.

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4.3 Relation between population and transportation
Table 3: Registered vehicles
Year Registered vehicle Annual
growth
2011 5.6 2.30%
2015 6.2 2.50%
The number of registered vehicles has increased in between 2011 and 2015. During 2011,
the number of registered vehicles were 5.6 million which increases to 6.2 million in 2015. The
annual growth rate for registered vehicles has increased from 2.3% to 2.5%.
4.4 Population and different mode of transportation
Table 4: Correlation between population and different mode of transportation
Correlations
Population Rail Bus Aircraft Cars
Population
Pearson Correlation 1 .654 .737* .977** .842**
Sig. (2-tailed) .079 .037 .000 .009
N 8 8 8 8 8
Rail
Pearson Correlation .654 1 -.621 .610 .762*
Sig. (2-tailed) .079 .100 .108 .028
N 8 8 8 8 8
Bus
Pearson Correlation .737* -.621 1 -.643 -.535
Sig. (2-tailed) .037 .100 .085 .172
N 8 8 8 8 8
Aircraft
Pearson Correlation .977** .610 -.643 1 .806*
Sig. (2-tailed) .000 .108 .085 .016
N 8 8 8 8 8
Cars
Pearson Correlation .842** .762* -.535 .806* 1
Sig. (2-tailed) .009 .028 .172 .016
N 8 8 8 8 8
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).

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The correlation between population and rail transportation is 0.654. This means with
growth of population, the use of rail transportation has increased. For bus, the correlation is
negative having a correlation co-efficient of -0.757. That means with growth of population use of
bus transportation reduces because of high congestion on roads. For aircraft and cars, there is
high correlation between population and these transportation means.
4.5 Regression Analysis
The relation obtained from correlation analysis further analyzed using linear regression
Relation between population and rail transport
Table 5: Regression model between rail and population
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .654a .427 .332 223.077
a. Predictors: (Constant), Population

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Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) -3134.429 3038.397 -1.032 .342
Population .001 .000 .654 2.116 .079
a. Dependent Variable: Rail
The R square value of the model is 0.427 implying a positive relation between rail
transport and population. However, the coefficient is not statistically significant. Therefore, the
null hypothesis of no significant relation between rail transport and population growth is
accepted.
Relation between population and bus transport
Table 6: Regression model between bus and population
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .737a .543 .467 87.520
a. Predictors: (Constant), Population
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 4368.793 1192.059 3.665 .011
Population -.0004 .000 -.737 -2.673 .037
a. Dependent Variable: Bus

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The regression results shows a statistically significant relation between bus transportation
and population growth. An inverse relation exists between the two variables. Therefore, the null
hypothesis of no significant relation between bus transportation and population growth is
rejected.
Relation between population and aircraft transport
Table 7: Regression model between aircraft and population
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .977a .955 .948 28.884
a. Predictors: (Constant), Population
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) -3514.111 393.409 -8.932 .000
Population .001 .000 .977 11.286 .000
a. Dependent Variable: Aircraft
The estimated co-efficient of population is 0.001. Therefore, population growth has a
negligible impact on aircraft transportation. The co-efficient is statistically significant as
obtained from the significant p value. The positive and significant co-efficient implies population
growth positively influences aircraft transportation.

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Relation between population and cars transport
Table 8: Regression model between cars and population
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .842a .710 .661 1604.402
a. Predictors: (Constant), Population
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) -37004.462 21852.569 -1.693 .141
Population .011 .003 .842 3.829 .009
a. Dependent Variable: Cars
For car transport, a positive coefficient is obtained from the regression result. The p value
for the co-efficient is 0.009, which is less than significance level. Therefore, the null hypothesis
of no significant relation between population and car transportation is rejected.
4.6 Population and Length of road
Table 9: Population and road length
Length of roads
Year Population Length
2009 7133275 184,761
2016 7746450 185,000
With an increase in population overtime, the length of roads has increased. The length of
roads in 2009 was 184,761 km, which increases to 185,000 km in 2016.

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5. Discussion
5.1 Population growth is related with transportation
The analysis has shown, population in New South Wales is growing continuously. This is
now considered as one of the most populated regions of Australia. With increase in population,
there is an increasing pressure on transportation system. The number of registered vehicles have
increased significantly with level of population. This is in line with results of previous research
papers (Stevenson et al., 2016).
5.2 Relation between different mode of transportation and population growth
With growth of population, the use of different mode of transportation increases. The
used mode of transportation are rail, bus, aircraft and cars. A positive relation exists between rail
transport and population. The rail patronages though have increased for city rail and metropolitan
areas, in regional and rural areas patronages has accounted a decline as compared to previous
years. This means more people are now shifted to the metropolitan areas (Wanke, Barros &
Figueiredo, 2016). Popualtion growth has a negative signifiant relation with bus transportation.
However, with increasing population crowd in public and private transportation is likely to
increases. To avoid heavy crowd, people prefer to use their own cars. A statistically signifcant
positive relation exists between car use and popualtion growth. To avoid traffic congestion
people increasingly uses the means of airtransport. Popualtion growth has a positve significant
influence on aircraft users.
5.3 Road lengths are related with population
For easing the congestion level, new roads are constructed. The congestion level in New South
Wales is increasing at a comparatively slow rate.

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6. Conclusion
6.1 Addressing aim and objective of the research
The first research question of this paper is how population growth impacts transportation system
of New South Wales. Overtime the population in New South Wales have increased. With this,
the number of vehicles on roads have also increased.
Secondly, the paper tries to find relation between mode of transportation and population growth.
Passengers travelling by rail records a significant increase. However, most of the increase in
transportation have recorded in metropolitan areas. For bus transportation population growth
negatively influnces bus trasportation leading to a decrase in number of people using bus
transportation. The use of cars and aircrafts have recorded a considerable incrase.
Third objective is to find whether length of road increases along with population. The length of
roads in NSW has increased to control the traffic congestion.
6.2. Limitation
The main limitation of the paper is only a limited data can be obtained and analyzed. The
timeframe taken for comparison is relatively small. Another limitation is that the study focuses
only on New South Wales. However, major capital cities like Sydney and Melbourne also faces
problem of congestion because of overpopulation. The scenario in other cities has not considered
in the research.
6.3. Recommendation
The limitation of present research opens up scope for further research in this field. The
paper makes only quantitative analysis on transportation and population growth. Research can be

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made to find how population growth affect comfort of people. For this, a qualitative research
needs to be conducted on the comfort and satisfaction level of people along with population.
The paper finds that with growing population the preference for rail transportation has increased.
Therefore, government should focus on a better development of rail transportation in the city.
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Ricciardi, A. M., Xia, J. C., & Currie, G. (2015). Exploring public transport equity between
separate disadvantaged cohorts: a case study in Perth, Australia. Journal of transport
geography, 43, 111-122.

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Saberi, M., Wu, H., Amoh-Gyimah, R., Smith, J., & Arunachalam, D. (2017). Measuring
housing and transportation affordability: a case study of Melbourne, Australia. Journal
of transport geography, 65, 134-146.
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M. (2016). Land use, transport, and population health: estimating the health benefits of
compact cities. The Lancet, 388(10062), 2925-2935.
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Data Source:
3101.0 - Australian Demographic Statistics, Jun 2017. (2018). Abs.gov.au. Retrieved 30 January
2018, from http://www.abs.gov.au/ausstats/abs@.nsf/mf/3101.0
Bitre.gov.au. Retrieved 30 January 2018, from
https://bitre.gov.au/publications/2014/files/is_059.pdf

25PROFESSIONAL PRACTICE
NSW and Sydney Transport Facts | Transport for NSW. (2018). Transport.nsw.gov.au. Retrieved
30 January 2018, from https://www.transport.nsw.gov.au/newsroom-and-events/reports-
and-publications/nsw-and-sydney-transport-facts
Destinationnsw.com.au. Retrieved 8 February 2018, from
https://www.destinationnsw.com.au/wp-content/uploads/2018/01/travel-to-nsw-time-
series-ye-sept-17.pdf

26PROFESSIONAL PRACTICE
8. Appendix
Data
Year
Populatio
n
Mode of transportation
Railway Bus/coach Aircraft Cars
2009 7133275 2803 1333 770.2 41806
2010 7246350 3197 1319 824.1 44686
2011 7334125 3256 1186 843.6 45251
2012 7306525 3500 1241 845.4 48712
2013 7423375 3071 1171 947.1 46665
2014 7532275 3662 1035 1009.3 46822
2015 7632425 3384 998 1005.5 48588
2016 7746450 3478 1189 1148.8 50634
SPSS output
Chart 1: Trend in Population
Regression
Variables Entered/Removeda

27PROFESSIONAL PRACTICE
Model Variables
Entered
Variables
Removed
Method
1 Populationb . Enter
a. Dependent Variable: Rail
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .654a .427 .332 223.077
a. Predictors: (Constant), Population
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 222898.363 1 222898.363 4.479 .079b
Residual 298580.512 6 49763.419
Total 521478.875 7
a. Dependent Variable: Rail
b. Predictors: (Constant), Population
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) -3134.429 3038.397 -1.032 .342
Population .001 .000 .654 2.116 .079
a. Dependent Variable: Rail
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 Populationb . Enter
a. Dependent Variable: Bus

28PROFESSIONAL PRACTICE
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .737a .543 .467 87.520
a. Predictors: (Constant), Population

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ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 54711.235 1 54711.235 7.143 .037b
Residual 45958.765 6 7659.794
Total 100670.000 7
a. Dependent Variable: Bus
b. Predictors: (Constant), Population
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 4368.793 1192.059 3.665 .011
Population -.0004 .000 -.737 -2.673 .037
a. Dependent Variable: Bus
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 Populationb . Enter
a. Dependent Variable: Aircraft
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .977a .955 .948 28.884
a. Predictors: (Constant), Population

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ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 106257.407 1 106257.407 127.365 .000b
Residual 5005.653 6 834.276
Total 111263.060 7
a. Dependent Variable: Aircraft
b. Predictors: (Constant), Population
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) -3514.111 393.409 -8.932 .000
Population .001 .000 .977 11.286 .000
a. Dependent Variable: Aircraft
Variables Entered/Removeda
Model Variables
Entered
Variables
Removed
Method
1 Populationb . Enter
a. Dependent Variable: Cars
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .842a .710 .661 1604.402
a. Predictors: (Constant), Population

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ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 37743792.261 1 37743792.261 14.663 .009b
Residual 15444631.739 6 2574105.290
Total 53188424.000 7
a. Dependent Variable: Cars
b. Predictors: (Constant), Population
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) -37004.462 21852.569 -1.693 .141
Population .011 .003 .842 3.829 .009
a. Dependent Variable: Cars