Estimating Linear Trends: Simple Linear Regression versus Epoch Differences

2015 ◽  
Vol 28 (24) ◽  
pp. 9969-9976 ◽  
Author(s):  
Elizabeth A. Barnes ◽  
Randal J. Barnes
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Linear Trend ◽  
Simple Linear Regression ◽  
Difference Estimator ◽  
First Order ◽  
Epoch Length ◽  
Average Value ◽  
The Difference ◽  
Linear Regression Estimator

Abstract Two common approaches for estimating a linear trend are 1) simple linear regression and 2) the epoch difference with possibly unequal epoch lengths. The epoch difference estimator for epochs of length M is defined as the difference between the average value over the last M time steps and the average value over the first M time steps divided by N − M, where N is the length of the time series. Both simple linear regression and the epoch difference are unbiased estimators for the trend; however, it is demonstrated that the variance of the linear regression estimator is always smaller than the variance of the epoch difference estimator for first-order autoregressive [AR(1)] time series with lag-1 autocorrelations less than about 0.85. It is further shown that under most circumstances if the epoch difference estimator is applied, the optimal epoch lengths are equal and approximately one-third the length of the time series. Additional results are given for the optimal epoch length at one end when the epoch length at the other end is constrained.

scholarly journals PM2.5 Concentration Estimation Based on Image Processing Schemes and Simple Linear Regression

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2423 ◽  
Author(s):  
Jiun-Jian Liaw ◽  
Yung-Fa Huang ◽  
Cheng-Hsiung Hsieh ◽  
Dung-Ching Lin ◽  
Chin-Hsiang Luo
Keyword(s):  
Image Processing ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Simple Linear Regression ◽  
Simple Linear Regression Model ◽  
Concentration Estimation ◽  
Single Feature ◽  
The Difference ◽  
Pm2.5 Concentration

Fine aerosols with a diameter of less than 2.5 microns (PM2.5) have a significant negative impact on human health. However, their measurement devices or instruments are usually expensive and complicated operations are required, so a simple and effective way for measuring the PM2.5 concentration is needed. To relieve this problem, this paper attempts to provide an easy alternative approach to PM2.5 concentration estimation. The proposed approach is based on image processing schemes and a simple linear regression model. It uses images with a high and low PM2.5 concentration to obtain the difference between these images. The difference is applied to find the region with the greatest impact. The approach is described in two stages. First, a series of image processing schemes are employed to automatically select the region of interest (RoI) for PM2.5 concentration estimation. Through the selected RoI, a single feature is obtained. Second, by employing the single feature, a simple linear regression model is used and applied to PM2.5 concentration estimation. The proposed approach is verified by the real-world open data released by Taiwan’s government. The proposed scheme is not expected to replace component analysis using physical or chemical techniques. We have tried to provide a cheaper and easier way to conduct PM2.5 estimation with an acceptable performance more efficiently. To achieve this, further work will be conducted and is summarized at the end of this paper.

scholarly journals Pengaruh Penerapan Sistem Control Valve Cooler 1 Berbasis Microcontroller ATmega 2560 Terhadap Moisture Pakan After Mixing dengan Bagging Off di PT. Japfa Comfeed Indonesia, Tbk. Unit Padang

2020 ◽  
Vol 6 (2) ◽  
pp. 80
Author(s):  
Eko Solihin ◽  
Sukardi Sukardi
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Control Valve ◽  
T Test ◽  
Test Results ◽  
Simple Linear Regression ◽  
Simple Linear Regression Analysis ◽  
Regression Test ◽  
The Difference

This research aims to know and analyze the effect of application of control valve Cooler 1 Based On atmega 2560 microcontroller for Moisture feed after mixing with bagging off at PT. JAPFA comfeed Indonesia, Tbk. Unit Padang. The type of research used is the study of surveys with the analysis of data used i.e. simple linear regression analysis. Based on the results of the study, testing of normality obtained the significance value of 0.200 with normal categories and R square test results worth 29.4%. The results of simple linear regression test partially with T test, Mempertlihatkan that the application of the control valve Cooler 1-Base atmega 2560 microcontroller system significantly affects the after-mixing Moisture feed with bagging off. The interpretation is that each temperature reduction of the resulting control valve cooler 1 Microcontroller-Based atmega 2560 in one unit affects the difference of moisture feed after mixing with a bagging off of 2.425.

scholarly journals Effect of Fear of Success on Employee Career Development with Achievement Motivation as a Moderating Variable at PT. PLN (Persero) Bulukumba Area

2019 ◽  
Vol 1 (1) ◽  
pp. 1-9
Author(s):  
Eka Suhartini ◽  
S Milawati ◽  
Hardin Hardin
Keyword(s):  
Regression Analysis ◽  
Career Development ◽  
Linear Regression ◽  
Achievement Motivation ◽  
Linear Regression Analysis ◽  
Simple Linear Regression ◽  
Fear Of Success ◽  
Moderating Variables ◽  
Saturated Sample ◽  
The Difference

Career development that makes others give different views such as appreciation, recognition, appreciation and higher social status in the eyes of society. This is what makes a person want to develop. But they are confronted with the reality that makes it negative. Require success or goals to be achieved and make someone doubt the ability required to fear success. This study aims to determine the effect of fear of success on career development and interaction on achievement motivation and fear of success on career development. This type of research is quantitative with associative research. The population in this study are companies that work at PT. PLN (Persero) Bulukumba Area. The technique of taking samples uses the saturated sample method. The sample in this study was 70 respondents. Data analysis uses simple linear regression analysis and moderation regression analysis by obtaining approved difference values. The results of research with simple linear regression show a fear of positive and significant success in career development. Analysis of moderating variables with the difference in assessment proves achievement motivation is not able to moderate the fear of success in career development.  

scholarly journals Long term dynamics of OH<sup>∗</sup> temperatures over Middle Europe: Trends and solar correlations

2016 ◽  
Author(s):  
Christoph Kalicinsky ◽  
Peter Knieling ◽  
Ralf Koppmann ◽  
Dirk Offermann ◽  
Wolfgang Steinbrecht ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Trend ◽  
Temperature Time Series ◽  
Time Interval ◽  
Radio Flux ◽  
Solar Radio Flux ◽  
Temperature Time

Abstract. We present the analysis of annual average OH∗ temperatures in the mesopause region derived from measurements of the GRound based Infrared P-branch Spectrometer (GRIPS) at Wuppertal (51° N, 7° E) in the time interval 1988 to 2015. The current study uses a 7 year longer temperature time series compared to the latest analysis regarding the long term dynamics of OH* temperatures measured at Wuppertal. This additional time of observation leads to a change in characterisation of the observed long term dynamics. We perform a multiple linear regression using the solar radio flux F10.7cm (11-year cycle of solar activity) and time to describe the temperature evolution. The analysis leads to a linear trend of (−0.089&amp;pm;0.055) K year−1 and a sensitivity to the solar activity of (4.2&amp;pm;0.9) K (100 SFU)−1 (r2 of fit 0.6). However, one linear trend in combination with the 11-year solar cycle is not sufficient to explain all observed long term dynamics. Actually we find a clear trend break in the temperature time series in middle of 2006. Before this break point there is an explicit negative linear trend of (−0.22&amp;pm;0.08) K year−1 and after 2006 the linear trend turns positive with a value of (0.38&amp;pm;0.23) K year−1. This apparent trend break can also be described using a long periodic oscillation. One possibility is to use the 22-year solar cycle that describes the reversal of the solar magnetic field (Hale cycle). A multiple linear regression using the solar radio flux and the solar polar magnetic field as parameters leads to the regression coefficients Csolar = (5.0&amp;pm;0.7) K (100 SFU)−1 and Chale = (1.8&amp;pm;0.5) K (100 µT)−1 (r2 = 0.71). But the best way to describe the OH* temperature time series is to use the solar radio flux and a 24-year oscillation. A multiple linear regression using these parameters leads to a sensitivity to the solar activity of (4.3&amp;pm;0.7) K (100 SFU)−1 and an amplitude of the 24-year oscillation A = (1.95&amp;pm;0.43) K (r2 = 0.77). The most important finding here is that using these parameters for the multiple linear regression an additional linear trend is no longer needed. Moreover, with the knowledge of this 24-year oscillation the linear trends derived in this and in a former study of the Wuppertal data series can be reproduced by just fitting a line to the corresponding part (time interval) of the oscillation. This actually means that depending on the analysed time interval completely different linear trends with respect to magnitude and sign can be observed. This fact is of essential importance for any comparison between different observations and model simulations. After detrending the temperature time series regarding the 11-year solar cycle and the 24-year oscillation multi-annual oscillations (MAOs) remain. A harmonic analysis finds three pronounced oscillations with periods of (2.69&amp;pm;0.06) years, (3.15&amp;pm;0.07) years, and (4.54&amp;pm;0.17) years. The corresponding amplitudes are (1.03&amp;pm;0.33) K, (1.03&amp;pm;0.33) K, and (0.91&amp;pm;0.36) K, respectively.

scholarly journals Analisis Pengaruh Faktor Kebutuhan Energi Listrik Tahun 2015 Terhadap Daya Yang Tersambung Dan Energi Yang Terjual Menggunakan Regresi Linear Sederhana (Studi Kasus Pada PT. PLN (Persero) Unit Area Pelayanan Dan Jaringan (APJ) Palembang)

2018 ◽  
Vol 9 (1) ◽  
Author(s):  
Nazori Suhandi ◽  
Irma Yuliawati ◽  
Indah Charista
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Electric Energy ◽  
Electrical Energy ◽  
Regression Method ◽  
Linear Regression Method ◽  
Simple Linear Regression ◽  
Level Of Significance

<p class="SammaryHeader" align="center"><strong><em>Abstract</em></strong></p><p><em>The availability of electrical energy is a very important aspect and even become a parameter to support the successful development of a region. Proper management of electrical energy resources and directed clearly will make the potential possessed of an area developed and utilized optimally. Population growth and economic development of a region can be influenced by the use of electrical energy. The supply of electricity must be taken into account so that the electrical energy can be available in an amount that suits your needs. Demand for the use of electricity in Indonesia will always increase with economic growth in addition to the development of electrical energy is also influenced by the development of the population in terms of quantity of customers to be electricity. Predicting methods such as using time series method (Gustriansyah, 2017) or data mining methods. The purpose of this research is to know how to overcome the influence of electricity usage (VA) connected with electric energy sold (KWh). Research done by simple linear regression method to facilitate writer in processing data. Based on the calculation result using simple linear regression method can be concluded 99.2% of the variation of electric power connected can be explained by the variable amount of electrical energy sold. While the rest (100% - 99.2% = 0.8%) is explained by other causes. And the level of significance &lt;0.05 so that the regression model can be used to predict the electrical energy sold.</em></p><p><strong><em>Keywords : </em></strong><em>Linear regression, analysis, electrical energy</em></p><p class="SammaryHeader" align="center"> </p><p class="SammaryHeader" align="center"><strong><em>Abstrak</em></strong></p><p><em>Ketersediaan energi listrik merupakan aspek yang sangat penting dan bahkan menjadi suatu parameter untuk mendukung keberhasilan pembangunan suatu daerah. Pengelolaan sumber daya energi listrik yang tepat dan terarah dengan jelas akan menjadikan potensi yang dimiliki suatu wilayah berkembang dan termanfaatkan secara optimal. Pertumbuhan populasi dan perkembangan ekonomi suatu wilayah dapat dipengaruhi penggunaan energi listrik. Penyediaan listrik harus diperhitungkan sehingga energi listrik dapat tersedia dalam jumlah yang sesuai dengan kebutuhan Anda. Permintaan untuk penggunaan energi listrik di Indonesia akan selalu meningkat dengan pertumbuhan ekonomi disamping pengembangan energi listrik juga dipengaruhi oleh perkembangan populasi dalam hal kuantitas pelanggan yang akan dialiri listrik. </em><em>Metode untuk memprediksi seperti menggunakan metode time series (Gustriansyah, 2017) atau metode data mining.</em><em> Adapun tujuan dari penelitian ini adalah untuk mengetahui bagaimana cara mengatasi pengaruh penggunaan tenaga listrik (VA) yang terhubung dengan energi listrik yang terjual (KWh). Penelitian dilakukan dengan metode regresi linier sederhana agar memudahkan penulis dalam mengolah data. Berdasarkan hasil perhitungan menggunakan metode regresi linier sederhana dapat disimpulkan sebesar 99,2% dari variasi daya listrik yang terhubung dapat dijelaskan oleh variabel jumlah energi listrik yang terjual. Sedangkan sisanya (100% - 99,2% = 0,8%) dijelaskan oleh penyebab lain. Dan tingkat signifikansi &lt;0,05 sehingga model regresi dapat digunakan untuk memprediksi energi listrik yang terjual.</em></p><p align="left"><strong><em>Kata kunc</em></strong><em>i: Regresi linier, analisis, energi listrik</em></p>

scholarly journals STUDY HABITS INFLUENCE MOTIVATION TOWARDS LEARNING STUDENTS

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Siti Hasanah ◽  
Omon Abdurakhman ◽  
Muhammad Ichsan
Keyword(s):  
Linear Regression ◽  
Random Sampling ◽  
Regression Equation ◽  
Study Habits ◽  
Learning Motivation ◽  
Sampling Techniques ◽  
Motivation To Learn ◽  
Simple Linear Regression ◽  
Final Exam ◽  
Average Value

This study started from the premise that students 'learning habits affect the students' motivation. It is strengthened by seeing, reading and watching the existing reality in these days that students always given extra lessons beyond the limit when it will face a final exam. The purpose of this study was to test the students' learning habits on motivation to learn. Good study habits influence on motivation to learn at SDN Cisarua 03. The method used in this study is a simple linear regression. The data obtained in this study originated from the observation, documentation and questionnaire. The population in this research is class 5 A and 5 B amounting to 73 peoples, then of the 73 sampled by the number of 62 peoples. Sampling techniques in this study using random sampling with using Yamane formula taro. Instrument in this study consisted of 40 items of 20 questions for the variable study habits of student and 20 questions for the variable of learning motivation. Results from this study indicate that students' learning habits have influence with an average value of 38.20% through the regression equation Ŷ = 20.46 + 0,67X the remaining 61.80% is determined by other factors not included in the discussion on research this.

scholarly journals GAYA HIDUP KONSUMEN DALAM PENGAMBILAN KEPUTUSAN PEMBELIAN

2017 ◽  
Vol 2 (1) ◽  
pp. 57-64
Author(s):  
Ajeng Sri Hartati ◽  
Ratih Hurriyati ◽  
Bambang Widjajanta
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Random Sampling ◽  
Research Study ◽  
Linear Regression Analysis ◽  
Purchase Decision ◽  
Simple Linear Regression ◽  
Cross Sectional ◽  
Analysis Technique ◽  
The Difference

Objective – To describe and determine the influence of lifestyle on purchasing decisions.Design/methodology/approach – This type of research is descriptive and verfikatif with random sampling of 120 respondents. Data analysis technique used is a simple linear regression The design of this study is cross sectional method with a certain period of time.Findings – Based on research results by using simple linear regression analysis showed that there is positive lifestyles of consumers on purchasing decisions.Originality/value – The difference in this study lies in the object of research, study time, measuring tools, literature used, the theory used and the results of research. Keywords: Marketing, Consumer Behavior, Lifestyle Consumer Purchase Decision, Action Cameras, GoPro.Type Article: Research paper

scholarly journals PENGARUH TINGKAT LITERASI BACA SISWA TERHADAP EFEKTIVITAS PEMBELAJARAN

2022 ◽  
Vol 6 (1) ◽  
Author(s):  
Yudho Ramafrizal S ◽  
Teni Julia Somadi
Keyword(s):  
Linear Regression ◽  
Descriptive Analysis ◽  
Linear Regression Analysis ◽  
Reading Literacy ◽  
Learning Effectiveness ◽  
Simple Linear Regression ◽  
Average Value ◽  
Student Reading ◽  
Literacy Level ◽  
Survey Research Methods

The title of this study is the Effect of Student Reading Literacy Level on Learning Effectiveness (Survey on Introduction of Accounting Class X Accounting at SMK Negeri 3 bandung academic year 2021-2022). This research aims to (i) find out the level of student read literacy in Introduction of Accounting of class X Accounting at SMK Negeri 3 bandung (ii) to know the level of learning effectiveness Introduction of Accounting of class X Accounting at SMK Negeri 3 bandung (iii) knowing the magnitude of the influence of students' reading literacy levels on the effectiveness of learning in Introduction of Accounting of class X Accounting at SMK Negeri 3 bandung. In this study the authors used quantitative approach methods using survey research methods, with a population of 30. To seek influence, The data analysis used is a simple linear regression analysis using IBM SPSS Statistics Version 24.0,descriptive analysis ofreading literacy level with a weight of 37.97 and an average value of 3.80, and theeffectiveness of learning with a sum weight of 40.03 and an average value of 4.00, on a simple linear regression test there is a regression coefficient value obtained is 0.580, and the determination coefficient valueis obtained the number R (correlation coefficient) or the relationship number between variable X to variable Y of 0.557 or 55.7%. The amount of contribution made by the variable level of reading literacy can be seen from the number in R Square which is 0.310 which means that the level of reading literacy affects the effectiveness of learning by 31.0%. While the other 69.0% was influenced by other factors outside of the research conducted.

scholarly journals On the observed changes in upper stratospheric and mesospheric temperatures from UARS HALOE

2008 ◽  
Vol 26 (5) ◽  
pp. 1287-1297 ◽  
Author(s):  
E. Remsberg
Keyword(s):  
Time Series ◽  
Solar Cycle ◽  
Linear Regression ◽  
Linear Trend ◽  
Upper Atmosphere ◽  
Middle Latitudes ◽  
Solar Occultation ◽  
Decadal Scale ◽  
Low Latitudes ◽  
Pressure Altitude

Abstract. Temperature versus pressure or T(p) time series from the Halogen Occultation Experiment (HALOE) of the Upper Atmosphere Research Satellite (UARS) have been extended and re-analyzed for the period of 1991–2005 and for the upper stratosphere and mesosphere in 10-degree wide latitude zones from 60 S to 60 N. Even though sampling from a solar occultation experiment is somewhat limited, it is shown to be quite adequate for developing both the seasonal and longer-term variations in T(p). Multiple linear regression (MLR) techniques were used in the re-analyses for the seasonal and the significant interannual, solar cycle (SC-like or decadal-scale), and linear trend terms. Plots of the amplitudes and phases for the interannual (QBO and subbiennial) terms are provided. A simple SC-like term of 11-yr period was fitted to the time series residuals after accounting for the seasonal and interannual terms. Highly significant SC-like responses were found for both the upper mesosphere and the upper stratosphere. The phases of these SC-like terms were checked for their continuity with latitude and pressure-altitude; the larger amplitude responses are directly in-phase with that of standard proxies for the solar flux variations. The analyzed, max minus min, responses at low latitudes are of order 0.5 to 1 K, while at middle latitudes they are as large as 3 K in the upper mesosphere. Highly significant, linear cooling trends were found at middle latitudes of the middle to upper mesosphere (−1.5 to −2.0 K/decade), at tropical latitudes of the lower mesosphere (about −0.5 K/decade), and at 2 hPa (of order −1 K/decade). Both the diagnosed solar cycle responses and trends from HALOE for the mid to upper mesosphere at middle latitudes are larger than simulated with most models, perhaps an indication of decadal-scale dynamical forcings that are not being simulated so well.

scholarly journals PENGARUH TINGKAT PENDIDIKAN TERHADAP KUALITAS KERJA KARYAWAN PADA BANK PERKREDITAN RAKYAT "POLATAMA KUSUMA" KECAMATAN JIWAN KABUPATEN MADIUN

2013 ◽  
Vol 1 (2) ◽  
Author(s):  
Ekik Mayundarwati
Keyword(s):  
Regression Analysis ◽  
Standard Deviation ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Simple Linear Regression ◽  
Level Of Education ◽  
Average Value ◽  
Maximum Value ◽  
Linearity Test

<div class="rtejustify">This study aims to determine  the level of employee education BPR POLATAMA KUSUMA  Madiun,  to determine  the quality  of employees  BPR POLATAMA KUSUMA Madiun,  and to determine  how much  influence  the level of education  of the employees' quality  of rural  banks "POLATAMA  KUSUM&lt;\"  Madiun.  Determination   of the sample in this study using a sample  that is saturated  all employees  Rural Bank Polatama  Kusuma Madiun  totaling  50 employees.  Data collection  using questionnaires   and documentation. In  analyzing   the  data  in this  study  using  a linearity  test  and  simple  linear  regression analysis  the t test. The level of education  has an average  value of 18.60 with a maximum value  of24,   the minimum  value  of 14, a standard  deviation  of 2.523,   median  and mode 18.50 by 18. From  the description  of the variables  is known  that the level of education  of respondents  tended  to agree on the level of education  is used managers  (average  18.60), Quality  employees   have  an average  value  of 53.64  with  a maximum   value  of  75, the minimum  value of37,  a standard  deviation  of9.752,   55.00 median  and mode of 60. From the description   of the variable  quality  of employee   respondents   tended  to have  known that the quality  of work  (average  53.64). Linearity  test results note that the value of 0.937</div>&gt; FhitunFgtabel  at 4.03 and Sig linearity  of 0.000  &lt; 0.05  mean education  level has an influ- ence on the quality of employees'  BPR POLATAMA KUSUMA  Madiun. Results of simple linear  regression  analysis  obtained   by the equation  Y = 43.696  + 0.535  X mean  when Level up 1% Quality Education employees will also rise by 0.535%, while other factors being equal. While the results of the calculation of the t test using SPSS for Windows 16.0 tcount obtained for 0.968 &gt; 1.677 for mean Ha ttabel accepted and HO is rejected it means there is the influence of the educational level of the employees'  quality of BPR POLATAMA KUSUMA Madiun.

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