Individual Assessment: Data Analysis and Forecasting of Turkey Data

This report provides an individual assessment of data analysis and forecasting techniques. It utilizes scatter graphs and regression analysis to predict the total population and vehicle usage in urban areas of Turkey. The report explores the correlation between various factors such as income, population, population density, and the percentage of the population in urban areas. The main body includes the creation and interpretation of scatter graphs, the calculation of regression line equations, and the prediction of future values using linear regression models. The study determines the impact of different variables on vehicle ownership and usage, offering insights for decision-making. The conclusion emphasizes the benefits of data analysis in making effective plans and predicting future values, supported by the use of statistical methods like correlation and regression.

Individual assessment
(Data Analysis and
Forecasting)
Abstract
In this report useful concepts of data analysis and forecasting are shows which help in
actually predicting the value of Turkey total population and vehicle used in urban areas. The

concept of regression analysis is helpful in defining the actual correlation between variables
that ease in making effective decision.

Contents
Abstract............................................................................................................................................2
INTRODUCTION...........................................................................................................................4
MAIN BODY...................................................................................................................................4
A) Scatter graphs....................................................................................................................4
B) Equation of the regression line..........................................................................................7
C) Scatter graphs....................................................................................................................7
D) Equation of the regression line..........................................................................................9
E) Two regression equations..................................................................................................9
F) Future values by using liner regression equations...........................................................10
CONCLUSION..............................................................................................................................10
REFERENCES..............................................................................................................................11

INTRODUCTION
The process of collecting valuable information related to a specific task or an activating within
an organisation and making effective analysis to detect the main issues or any other key finding
is known as Data analysing (Lamessa, 2019). Data interpretation can be helpful in number of
ways for companies dealing with large number as it provides a brief about actual happening of
business resulting into making of valuable decision. It also supports in making future estimation
about future possibilities and business scenarios that can be gained with proper execution of
plan.
In this report, different graph and calculation of regression line is shown. In addition, report
also covers future prediction about turkey vehicles with proper explanation.
MAIN BODY
A) Scatter graphs
Scatter graphs is basically a kind of diagram or plot which is used as a mathematical
Cartesian which help to demonstrate the values of two sort of variables as a part of series of data.
The position of each dot on the horizontal and vertical axis indicates values for an individual data
point.
Correlation between per capita income and vehicles per 1000 population is 0.724.

Correlation between Population and vehicles per 1000 population is 0.162.
Correlation between Population density per km^2 and vehicles per 1000 population is
0.015

Correlation between Percentage of population in urban areas and vehicles per 1000
population is 0.392.
Form all the above scatter graph, it has been observed that there are total 20 countries for
which data regarding cars usage and other factors have been shown such as vehicles per
thousand populations against income, population, population density and percentage of
population in urban areas (Madenci, Barut and Dorduncu, 2019). The different graph is effective
in determining the correlation value that help in defining the regression line equation. In general,
it is defined that if correlation coefficient of 1 implies that there is a positive improvement in a
specified proportion in the other for any significant change in one component. A correlation
coefficient of -1 implies there was a significant decline in a specified percentage in another for
any positive rise in one component. Similarly, zero means that for every increase, there isn’t a
positive or negative increase in the values of variable. The above graph 1 shows the coefficient
value among vehicles per thousand populations against income is 0.724, graph 2 represent the
value of coefficient is 0.162, in graph 3 the value of is 0.015. Similarly, the figure 4 shows the
value of correlation as 0.392. From these graph it is determined that correlation between vehicles
per thousand populations against income level and % of population living in urban areas are
more closely correlated with each other.
Correlation (per capital income and 1000 vehile) 0.724
Correlation (Population (million) and 1000
vehile) 0.162
Correlation (Population density per km^2 and
vehicles per 1000 population) 0.015

Correlation (Percentage of population in urban
areas and vehicles per 1000 population) 0.392
B) Equation of the regression line
From the graph above, it is observed that two depended variables like income level and actual
percentage of Population living areas are more correlated with the independent variable Vehicles
per 1000 population (Wang and et. al., 2019). Thus, liner equation for these variables are as
follows:
Formula of equation: Y= a + bX, in which Y is the depended variable and X denotes the value
of independent variable. To find the value of A and b.
For Population living in the urban areas and Vehicles per 1000 population the liner
equation is:
Y= 0.0462x + 52.641
R2 = 0.01537
Similarly, the regression line equation for Vehicles per 1000 population and income level
of population is as follows:
Y= 0.0489x-1.7796
R2 = 0.5243
C) Scatter graphs
Correlation between total vehicle ownership and population level in entire countries is
0.987.

Correlation between total vehicle ownership and population density per km^2 is
0.281.
Correlation between total vehicle ownership and percentage of population living in urban
areas is 0.117.
From the above graphical presentation, it is observed that all dependent variable has a
positive correlation with independent variable. Such as the value of correlation among total
vehicle ownership and population level of various counties is closely significant to the standard
value of correlation. Thus, 0.987 is consider to be the closest value to positive correlation in the
meanwhile which is further can be used to determine the regression value of these variables.

D) Equation of the regression line
Linear regression measures an approximation that significantly reduces the gap from all the
datasets along the connected rows (Figueres-Esteban, Hughes and Van Gulijk, 2015).
Functionally, the analysis of ordinary minus squares (OLS) reduces the squared residuals total.
In addition, if the variations between the observable values and the expected values of the model
are minimal and impartial a model matches the results well. R-squared is a quantitative indicator
of how similar the installed regression path is to the results. This is also recognized as the
decision component, or multiple decision factor for several regressions. Even, if the R-squared
value is small but, still have statistically relevant predictors, essential assumptions can also be
made on whether improvements in the predictor values are correlated with improvements in the
outcome value. Independent of the R-squared, when keeping certain determinants throughout the
model unchanged, the relevant coefficients also reflect the mean difference in answer per one
unit of shift in the predictor. This kind of knowledge may certainly be incredibly useful.
From the above scatter plot, it has been determined that most significant value coefficient is
between total vehicle ownership and population level in entire countries, thus the liner regression
equation is as follows:
Y= 1.6789x + 2.1399
R2 = 0.09744
On, the other side the regression equation for variables total vehicle ownership and population
density living in per ^2 km in different countries is as follow:
Y= 2.2111x + 114.15
R2 = 0.0791
E) Two regression equations
From the above calculated different regression values, the most useful liner equation for
the car company is listed underneath:
 Vehicles per 1000 population and income level of population is as follows:
Y= 0.0489x-1.7796
R2 = 0.5243
 Total vehicle ownership and population density living in per ^2 km
Y= 2.2111x + 114.15
R2 = 0.0791

These two liner regression values equation can be really effective mathematical method that can
be used to produce insights into customer preferences, market comprehension that profitability-
influencing influences. In car company, linear regressions may be used to determine patterns and
render projections or predictions. Therefore, a negative coefficient may be viewed as having a
negative or an opposite interaction with the regression coefficient so it can be assumed to be a
favourable effect (Gatobu, Arocha and Hoffman-Goetz, 2016). For every mathematical model
the primary element is the correct comprehension of the topic and its market operation. The
benefit of linear regression being that it helps one to catch the discrete influences of each
advertisement strategy along with monitoring the variables that could affect the sales. There are
several promotional promotions in real-life situations that operate over the same time span for
car company that is effective in determining the overall sales income for company in nearby
future. Analysing regression will add a quantitative perspective to every company management.
Regression analysis guides the path to better and more informed choices by transforming the
enormous volume of raw data into actionable information. That does not mean that the
evaluating analysis is also an end to innovative innovation by administrators. This method acts as
a great resource to evaluate a theory before immersion in performance of company. It increases
market success by emphasizing areas which have the greatest effect on operational quality and
sales.
F) Future values by using liner regression equations.
As the values of liner regression equation from the above calculation, in part b and c it is
estimated that vehicle per 1000 population with turkey would be 518 as well as on the other side
the total vehicle per owner in turkey is 11.85.
CONCLUSION
In the end of this report, it has been concluded that data analysis is beneficial in determining
the useful value for future use that also support in making effective plans that directly increase
the overall productivity. In addition, the application of different statistical approaches and
techniques like correlation and regression are effective in defining the future value.

REFERENCES
Books and Journals
Lamessa, T., 2019. Computational Data analysis of Fourıer Transformatıon by Numerical
experiments (Numerical CODE). International Journal For Research In Mathematics
And Statistics (ISSN: 2208-2662). 5(5). pp.01-13.
Madenci, E., Barut, A. and Dorduncu, M., 2019. Peridynamic differential operator for numerical
analysis. Springer International Publishing.
Wang, Y. and et. al., 2019. Test of a Weather-Adaptive Dual-Resolution Hybrid Warn-on-
Forecast Analysis and Forecast System for Several Severe Weather Events. Weather and
Forecasting. 34(6). pp.1807-1827.
Figueres-Esteban, M., Hughes, P. and Van Gulijk, C., 2015, September. The role of data
visualization in railway big data risk analysis. In Proceedings of the 25th European
Safety and Reliability Conference, ESREL 2015 (pp. 2877-2882). CRC Press/Balkema.
Gatobu, S. K., Arocha, J. F. and Hoffman-Goetz, L., 2016. Numeracy, health numeracy, and
older immigrants’ primary language: an observation-oriented exploration. Basic and
Applied Social Psychology. 38(4). pp.185-199.
Marks, G. N., 2015. School sector differences in student achievement in Australian primary and
secondary schools: A longitudinal analysis. Journal of School Choice. 9(2). pp.219-238.