2 results listed
Inflation refers to an ongoing and overall comprehensive increase in the overall level
of goods and services price in the economy. Today; inflation, which is tried to be kept under control
by the central banks, is trying to ensure price stability, the continuous price changes that arise in
all the goods or services that consumers use includes. Undoubtedly in terms of economy, inflation
expectations are also ganing importance, except for rhe realized inflation. This situation makes it
necessary to predict the future vaules of inflation. In that case, a reliable estimate of the future
values of inflation in any country will create an entry in determining the policies that decisionmaker
units will implement on the economy.
The aim of this article is to predict inflation in the next period by using the Consumer Price Index
(CPI) data with two alternative techniques. It is also aimed to examine the prediction performances
of these two techniques in comparisons. Thus, the first of the two main objectives of the study is
to predict the future values of inflation with two alternative techniques. The second goal is to
determine which of these two techniques well compared to statistical and econometric criteria.
In this context, the estimated performance of both techniques was predicted by the 9-month
inflation, Box-Jenkins (ARIMA) and Artificial Neural Networks (ANN) in the April – December
2019 period, using CPI data consisting of 207 in the period of January 2002 – March 2019. In the
study, Eviews and Matlab programs were utilized.
International Data Science & Engineering Symposium
IDSES
Erkan IŞIĞIÇOK
Ramazan Öz
Savaş Tarkun
processes in the business world have two fundamental disease, including deviation and
variability from the average (target). One of the statistical process control graphs used for
quantitative variables is to keep the average and the other to control variability. Apart from the
normal distribution or nonnormal of quantitative data, the average and variability are controlled or
not, and then the capability of process or machine is checked. The desired outcome is in addition
to the normal distribution of data, the process is under control and capable. On the other hand,
capability analysis is defined as the machine capability analysis when it is performed for the
machine, while the process capability analysis takes its name when it is done for the process. In
this study, machine capability analysis has been applied.
The aim of this article is to investigate the effects of the normal distribution or nonnormal of data
on machine capability analysis. For this purpose, data on the lengths measured by surface of the
shock absorber body pipe cut by a CNC machine in a company in the automative industry were
used. In the study, 50 observations values were used, and the lower specification limit was 124.5
and upper specification limit was 125.5, and the CNC (pipe cutting) machine was sufficient or not.
The analysis first started with the implementation of the normality test and the data was not
distributed normally. It is concluded that assuming this data, which does not have normal
distribution, is normally distributed, and the machine is under control and is also sufficient with
the I-MR control charts in the Minitab program. The same analysis was applied with the nonnormal
command under the assumption that the data was nor normally distributed, and even in this case
the machine was sufficient. In addition, the data that does not have normal distribution has been
transformed into normality, and I-MR control charts and machine are under control and also
sufficient. According to the findings, the average and specification limits of the values of I-MR
control charts are the same and the machine capability results are different. In this study, these similarities and differences were examined comparatively. Let us add it right away; these findings
are specific to the machine and cut pipes we take into consideration and should not be generalized.
International Data Science & Engineering Symposium
IDSES
Erkan IŞIĞIÇOK
Gözde TÜRK