Garuda - Garba Rujukan Digital

Article Per Year (5 Year)

All

Media Statistika

media_statistika
Website

Published by Universitas Diponegoro

ISSN : - EISSN : 24770647 DOI : -

Core Subject : Science,

Decision Sciences, Operations Research & Management

Arjuna Subject : -

Articles 6 Documents

Search results for , issue "Vol 1, No 1 (2008): Media Statistika" : 6 Documents clear

UJI STASIONERITAS DATA INFLASI DENGAN PHILLIPS-PERON TEST Maruddani, Di Asih I; Tarno, Tarno; Anisah, Rokhma Al
MEDIA STATISTIKA Vol 1, No 1 (2008): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The classical regression model was devised to handle relationships between stationary variables. It should not be applied to nonstationary series. A time series is therefore said to be stationary is its mean, variance, and covariances remain constant over time. A problem associated with nonstationary variables, and frequently faced by econometricians when dealing with time series data, is the spurious regression. An apparent indicator of such spurious regression was a particularly low level for the Durbin-Watson statistics, combined with an acceptable R2. Statistical test for stationarity have proposed by Dickey and Fuller (1979). The distribution theory supporting the Dickey-Fuller test assumes that the errors are statistically independent and have a constant variance. Phillips and Peron (1988) developed a generalization of the Dickey-Fuller procedure that the error terms are correlated and not have constant variance. In this paper, we use Phillips-Peron test for inflation data in Indonesia for the time period 1996-2003. The data showed upward trend and the error terms are correlated. The empirical results showed that the inflation data in Indonesia is a nonstationary series. Â Keywords : stationarity, non autocorrelation, Phillips-Peron Test, inflation

RANCANGAN STRIP PLOT MODEL TETAP Wuryandari, Triastuti; Wilandari, Yuciana; Afifah, Noor
MEDIA STATISTIKA Vol 1, No 1 (2008): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

The experiment involve the study of the effects of two or more factors can be used the factorial designs. The factorial designs have several advantages. They are more efficient than one factor at a time experints. Furthermore, a factorial designs is necessary when interaction may be present to avoid misleading conclutions. In the Strip Plot design is factorial two factors which random factors arenâ€™tÂ based on main plot or the whole plot but the important is itâ€™s interaction. There are three error in the Strip plot. They are error caused by factor A, error caused by factor B and error by A and B interaction. Â Keywords: Factorial, Strip Plot, Interaction

PEMILIHAN PARAMETER THRESHOLD OPTIMAL DALAM ESTIMATOR REGRESI WAVELET THRESHOLDING DENGAN PROSEDUR FALSE DISCOVERY RATE (FDR) Suparti, Suparti; Tarno, Tarno; Haryono, Yon
MEDIA STATISTIKA Vol 1, No 1 (2008): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

If X is predictor variable and Y is responseÂ variable of following model Y = f (X) +e with function f is regression which not yet been known and e is independent random variable with mean 0 and variant , hence function of f can estimate with parametric and nonparametric approach. At this paper estimate f with nonparametric approach. Nonparametric approach that used is wavelet shrinkage or wavelet thresholding method. At function estimation with method of wavelet thresholding, what most dominant determine level of smoothing estimator is value of threshold. The small threshold give function estimation very no smoothly, whileÂ the big value of threshold give function estimation very smoothly. Therefore require to be selected value of optimal threshold to determine optimal function estimation. Â Â Â Â Â Â Â Â Â Â Â Â One of the method to determine the value of optimal threshold is with procedure of False Discovery Rate ( FDR). In procedure of FDR, the optimal threshold determined by selection of level of significance. Smaller mount used significance progressively smoothly its . Â Keywords: Nonparametric regression, wavelet thresholding estimator, procedure of False Discovery Rate

PEMODELAN GENERAL REGRESSION NEURAL NETWORK UNTUK PREDIKSI TINGKAT PENCEMARAN UDARA KOTA SEMARANG Warsito, Budi; Rusgiyono, Agus; Amirillah, M. Afif
MEDIA STATISTIKA Vol 1, No 1 (2008): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

This paper is discuss about General Regression Neural Network (GRNN) modelling to predict time series data, i.e. the air pollution rate in Semarang City comprises the floating dust, carbon monoxide (CO) and nitrogen monoxide (NO). The GRNN model have four processing layer that are input layer, pattern layer, summation layer and output layer. The input variable is determined by the ARIMA model. The result of GRNN modelling shows that the network have a good performance both at predict in sample and predict out of sample, that can be seen from the mean square error. Â Keywords: GRNN, predict, air pollution Â

PENELUSURAN KARAKTERISTIK PERILAKU KONSUMEN DENGAN METODE AUTOMATIC INTERACTION DETECTION (AID) Rusgiyono, Agus
MEDIA STATISTIKA Vol 1, No 1 (2008): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

AID methods used to see relation between respons variable with a number of variable of predictor and also see related pattern between predictors. In AID procedures data disjointed in two group determined by variable of predictor most explaining of differenceÂ values of respons variables, and this recuring process so that yielded to with refer to splits in data. Every split yield new data sub which its variable values is mutually and exclusiveÂ exhaustive. Its end resultsÂ with refer toing crotch is so-called tree of AID Â Keywords : split, tree of AID

MODEL PERTUMBUHAN DUA SAMPEL Sudarno, Sudarno
MEDIA STATISTIKA Vol 1, No 1 (2008): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Growth curve model is generalization of the multivariate regression models. This paper determine growth curve model at two samples of ras. They are observed body weight after given by substances. The problems will be done are testing for an outlier, testing for multivariate normality, testing for hogeneous, test of the adequacy of the model, and estimate of the parameters model. In doing computation and visualization of statistics at needed formulas, use up date some softwares, such as SAS, Minitab, MATLAB, etc. If the estimated model is known, it can be used to some objectives. The estimate model could be applied to predicted tool for next time, give characteristic and properties of the model. The growth of rat which be given thyroxin substance, at beginning time cause significant increasing of the body weight, but after at sixth weeks, given substance imply significant decreasing of their body weight, too. Meanwhile for the rat be given thyouracil, at beginning time to tenth weeks, their body weight affect slowly increasing by continue. Therefore thyroxin substance should be given before seventh weeks, but for thyouracil substance could be given continuosly if cause increasing the body weight. This result could be used to optimalization of rat growth. Â Key words: Multivariate normal test, Adequacy of model test, Growth curve model.

Page 1 of 1 | Total Record : 6

Filter by Year

2008 2008

Filter By Issues

All Issue Vol 18, No 1 (2025): Media Statistika Vol 17, No 2 (2024): Media Statistika Vol 17, No 1 (2024): Media Statistika Vol 16, No 2 (2023): Media Statistika Vol 16, No 1 (2023): Media Statistika Vol 15, No 2 (2022): Media Statistika Vol 15, No 1 (2022): Media Statistika Vol 14, No 2 (2021): Media Statistika Vol 14, No 1 (2021): Media Statistika Vol 13, No 2 (2020): Media Statistika Vol 13, No 1 (2020): Media Statistika Vol 12, No 2 (2019): Media Statistika Vol 12, No 1 (2019): Media Statistika Vol 11, No 2 (2018): Media Statistika Vol 11, No 1 (2018): Media Statistika Vol 10, No 2 (2017): Media Statistika Vol 10, No 1 (2017): Media Statistika Vol 9, No 2 (2016): Media Statistika Vol 9, No 1 (2016): Media Statistika Vol 8, No 2 (2015): Media Statistika Vol 8, No 1 (2015): Media Statistika Vol 7, No 2 (2014): Media Statistika Vol 7, No 1 (2014): Media Statistika Vol 6, No 2 (2013): Media Statistika Vol 6, No 1 (2013): Media Statistika Vol 5, No 2 (2012): Media Statistika Vol 5, No 1 (2012): Media Statistika Vol 4, No 2 (2011): Media Statistika Vol 4, No 1 (2011): Media Statistika Vol 3, No 2 (2010): Media Statistika Vol 3, No 1 (2010): Media Statistika Vol 2, No 2 (2009): Media Statistika Vol 2, No 1 (2009): Media Statistika Vol 1, No 2 (2008): Media Statistika Vol 1, No 1 (2008): Media Statistika More Issue

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
-

Editorial Address
-

Location
Kota semarang,

Jawa tengah

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name -

Contact Email -

Phone -

Journal Mail Official -

Editorial Address -

Location Kota semarang, Jawa tengah INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
-

Editorial Address
-

Location
Kota semarang,

Jawa tengah

INDONESIA