scholarly journals Risiko Usahatani Mangga di Kecamatan Rembang Jawa Tengah

2020 ◽  
Vol 20 (2) ◽  
pp. 161-169
Author(s):  
Hendrik Johannes Nadapdap ◽  
Bobby Rachmat Saefudin
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Organic Fertilizers ◽  
Inorganic Fertilizers ◽  
Production Risk ◽  
Coefficient Variation ◽  
High Chance ◽  
Level Of Risk

Production fluctuation are an indication of production risk. Kragan District is one of the largest producers of mango in Rembang Regency. The production of mangoes produced by Kragan Subdistrict is still relatively low so this research aims to measure the level of risk, find out the factors that influence production and find out the factors that influence the risk of mango production in the Kragan Subdistrict. The object of this study was 31 mango farmers. The measure to calculate the level of risk is the standard deviation and the coefficient variation. The magnitude of the effect of the use of inputs on production risk is analyzed using multiple linear regression with the heteroscedastic method. The results of the calculation of production risk were analyzed using standard deviation and coefficient variation (KV) and having a standard deviation of 66.87 and the coefficient variation is 0.84, this shows that the high chance of production risk in conducting mango farming by 84 percent. Factors that significantly affect the production of mangoes in Kragan Subdistrict are the number of mango trees, organic fertilizers and inorganic fertilizers while the factors that significantly influence the risk of mango production are the number of trees and labor.  

Influence of farm, parity, season and litter size on the lactation curve parameters of white British dairy goats

1993 ◽  
Vol 57 (1) ◽  
pp. 99-104 ◽  
Author(s):  
J. C. Williams
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Litter Size ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Dairy Goats ◽  
Lactation Curve ◽  
Non Linear ◽  
Research Institute

AbstractThe following goat lactation model was fitted (using non-linear regression) to 407 lactations from five commercial goat dairies and one Research Institute goat herd: y = A exp (B(l + n'/2)n' + Cn' 2 - 1·01/n) where y = daily yield in kg; n = day of lactation (post parturition); and n' = (n -150)1100.Influence of farm, parity and season on the parameter estimates for 376 individual lactations was studied, using multiple linear regression. The models adopted were of the form: A = 1·366 + 1·122 × parity - 0·137 × parity2; ln(-B) = - 1·711 + 0·107 × parity + 0·512 season one; C = 0·037, with a standard deviation for A of 0·658, for ln(-B) of 0·636 and for C of 0·127.Influence of litter size on parameters was investigated for the Research Institute herd. There was no evidence of an effect on any of the model parameters.

Breastfeeding Self-Efficacy and Its Relationship with the Perceived Stress and the Breastfeeding Performance in Iranian Mothers with Late Preterm Infants, 2019

2020 ◽  
Author(s):  
Akram Kahforoushan ◽  
Shirin Hasanpour ◽  
Mojgan Mirghafourvand
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Preterm Infants ◽  
Perceived Stress ◽  
Self Efficacy ◽  
Short Form ◽  
Late Preterm ◽  
Late Preterm Infants ◽  
The Mean

Abstract BackgroundLate preterm infants suffer from many short-term and long-term problems after birth. The key factor in fighting these problems is effective breastfeeding. The present study aimedto determine the breastfeeding self-efficacy and its relationship with the perceived stress and breastfeeding performance in mothers with late preterm infants. MethodsIn this prospective study, 171 nursing mothers with late preterm infants born in Alzahra Medical Center of Tabriz, Iran, who met the conditions of this study were selected through convenience sampling. The Breastfeeding Self-Efficacy Scale-Short Form (BSES- SF) was employed to measure breastfeeding self-efficacy and 14-item Perceived Stress Scale (PSS14) was used to measure the perceived stress during 24 hours after giving birth and when the child was 4 months old the breastfeeding performance was measured by the standard breastfeeding performance questionnaire. The data were analyzed by Pearson and Spearman’s correlation tests, independent t-test, one-way ANOVA, and Multiple Linear Regression.ResultsThe mean (standard deviation) of breastfeeding self-efficacy equaled 50.0 (7.8) from the scores ranging between13-65 and the mean (standard deviation) of the perceived stress equaled to 26.5 (8.8) from the scores ranging between 0-56. The median (25-75 percentiles) of breastfeeding performance score in the mothers equaled 2.0 (1.0 to 3.0) from the scores ranging between 0-6. On the basis of multiple linear regression and through adjusting the personal-social characteristic, by increasing the score of the breastfeeding self-efficacy, the perceived stress was decreased to a statistically significant amount (B=-0.1, 95%CI=-0.3 to 0.0), however, there was no statistically significant relationship between breastfeeding self-efficacy and breastfeeding performance (p=0.418). ConclusionDue to the modifiable variability of breastfeeding self-efficacy and its role in perceived maternal stress, the development of appropriate strategies to further increase breastfeeding self-efficacy and provide more support to these mothers and infants is of particular importance.

Descriptive and Predictive Analytical Methods for Big Data

Web Services ◽  
2019 ◽  
pp. 314-331 ◽  
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim ◽  
Nabeel R. Kasim
Keyword(s):  
Big Data ◽  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Knowledge Discovery ◽  
Data Visualization ◽  
Analytical Methods ◽  
Data Analytics ◽  
Enterprise Performance ◽  
Analytical Tools

Data analytics and modeling are powerful analytical tools for knowledge discovery through examining and capturing the complex and hidden relationships and patterns among the quantitative variables in the existing massive structured Big Data in efforts to predict future enterprise performance. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools for analyzing structured Big Data. The chapter covers descriptive and predictive analytical methods. Descriptive analytical tools such as mean, median, mode, variance, standard deviation, and data visualization methods (e.g., histograms, line charts) are covered. Predictive analytical tools for analyzing Big Data such as correlation, simple- and multiple- linear regression are also covered in the chapter.

scholarly journals Risiko Produksi Karet Petani di Kecamatan Kejuruan Muda Kabupaten Aceh Tamiang

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Fifi Ariska Siregar ◽  
Fiddini Alham ◽  
Thursina Mahyuddin ◽  
Muslimah
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Rubber Tree ◽  
Price Fluctuation ◽  
Production Risk ◽  
Rubber Production ◽  
Production Factors ◽  
Independent Variable

The aim of this research to know the source of risk of rubber production, to analyze the affected of rubber production factors and price fluctuation affected of rubber production in Kejuruan Muda district, Aceh Tamiang region. The method used a survey. Analyze of rubber production of a source of risk used multiple linear regression with independent variable is a number of rubber tree died, a number of disease tree, climate and production month before and the dependent variable is production. The sources of rubber production risk are climate and pest. The number of died tree and tree with disease affected rubber production.   

Descriptive and Predictive Analytical Methods for Big Data

2016 ◽  
pp. 66-83
Author(s):  
Sema A. Kalaian ◽  
Rafa M. Kasim ◽  
Nabeel R. Kasim
Keyword(s):  
Big Data ◽  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Knowledge Discovery ◽  
Data Visualization ◽  
Analytical Methods ◽  
Data Analytics ◽  
Enterprise Performance ◽  
Analytical Tools

Data analytics and modeling are powerful analytical tools for knowledge discovery through examining and capturing the complex and hidden relationships and patterns among the quantitative variables in the existing massive structured Big Data in efforts to predict future enterprise performance. The main purpose of this chapter is to present a conceptual and practical overview of some of the basic and advanced analytical tools for analyzing structured Big Data. The chapter covers descriptive and predictive analytical methods. Descriptive analytical tools such as mean, median, mode, variance, standard deviation, and data visualization methods (e.g., histograms, line charts) are covered. Predictive analytical tools for analyzing Big Data such as correlation, simple- and multiple- linear regression are also covered in the chapter.

Pengaruh Motivasi Kerja Terhadap Kinerja Pegawai Kantor Sekretariat Daerah Kabupaten Gunungkidul

2017 ◽  
Vol 23 (2) ◽  
pp. 121-137
Author(s):  
Ary Sutrischastini ◽  
Agus Riyanto
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Sampling Method ◽  
Work Motivation ◽  
Probability Sampling ◽  
Random Probability

This paper will discuss the effect of work motivation (incentives, motives and expectations) on the performance of the staff of the Regional Secretariat Gunungkidul. The purpose of this paper is: 1) Determine the effect of incentives on the performance of the staff of the Regional Secretariat Gunungkidul, 2) Determine the effect of motive on the performance of the staff of the Regional Secretariat Gunungkidul, 3) To know the effect of expectations on the performance of the staff of the Regional Secretariat Gunungkidul, 4)To know the effect of incentives, motives and expectations on the performance of the staff of the Regional Secretariat Gunungkidul.Research sites in the Regional Secretariat Gunungkidul and the population is 162entire employee in the Regional Secretariat Gunungkidul. Samples amounted to 116 respondents taken with simple random probability sampling method. Data were analyzed using multiple linear regression. Results obtained: (1) incentives positive and significant effect on the performance of, (2) motif positive and significant effect on the performance of, (3) expectations positive and significant impact on the performance of , and (4) incentives, motives and expectations of positive and significant impact on the performance of the staff of the Regional Secretariat Gunungkidul.

PENGARUH FAKTOR-FAKTOR INTERNAL PERBANKAN TERHADAP NON PERFORMING FINANCING PADA BANK UMUM SYARIAH

2019 ◽  
Vol 6 (2) ◽  
pp. 115-130
Author(s):  
Eka Ambara Harci Putranta ◽  
Lilik Ambarwati
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Capital Adequacy ◽  
Annual Reports ◽  
Regression Methods ◽  
Capital Adequacy Ratio ◽  
Negative Effect ◽  
Independent Variable

The study aims to analyze the influence of internal banking factors in the form of: Capital Adequency Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing at Sharia Banks. This research method used multiple linear regression analysis with the help of SPSS 16.00 software which is used to see the influence between the independent variables in the form of Capital Adequacy Ratio (CAR), Financing to Deposit Ratio (FDR) and Total Assets (TA) to Non Performing Financing. The sample of this study was 3 Islamic Commercial Banks, so there were 36 annual reports obtained through purposive sampling, then analyzed using multiple linear regression methods. The results showed that based on the F Test, the independent variable had an effect on the NPF, indicated by the F value of 17,016 and significance of 0,000, overall the independent variable was able to explain the effect of 69.60%. While based on the partial t test, showed that CAR has a significant negative effect, Total assets have a significant positive effect with a significance value below 0.05 (5%). Meanwhile FDR does not affect NPF.

Faktor-Faktor Yang Mempengaruhi Keputusan Mahasiswa Memilih Program Studi Kebidanan STIKES Muhammadiyah Ciamis Tahun 2016

2018 ◽  
Vol 5 (1) ◽  
pp. 69-82
Author(s):  
Evi Mariana
Keyword(s):  
Data Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Random Sampling ◽  
Sampling Method ◽  
Marketing Mix ◽  
Stratified Random Sampling ◽  
Price Promotion ◽  
F Test ◽  
Physical Evidence

The purpose of this study was to analyze the factors that influence the decisionof the students chose to study in Obstetrics Prodi STIKES Muhammadiyah Ciamis and analyze the factors that most influence the decision of the students chose to study in Obstetrics Prodi STIKES Muhammadiyah Ciamis. Collecting data in this study was conducted using a survey by questionnaire to 114 students by stratified random sampling method. Methods of data analysis using multiple linear regression, F test and test T. The result is a marketing mix that significantly is the product, place, and physical evidence. And that does not affect the marketing mix is price, promotion, place, and processes

ANALISIS PENGARUH INFLASI, KURS DAN IHSG TERHADAP RETURN REKSA DANA SAHAM PT NIKKO SECURITIES INDONESIA PERIODE 2014-2017

2019 ◽  
Vol 2 (2) ◽  
pp. 103
Author(s):  
Muhammad Rois Rois ◽  
Manarotul Fatati Fatati ◽  
Winda Ihda Magfiroh
Keyword(s):  
Regression Analysis ◽  
Exchange Rate ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Stock Price ◽  
Price Index ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Equity Fund ◽  
Stock Price Index

This study aims to determine the effect of Inflation, Exchange Rate and Composite Stock Price Index (IHSG) to Return of PT Nikko Securities Indonesia Stock Fund period 2014-2017. The study used secondary data obtained through documentation in the form of PT Nikko Securities Indonesia Monthly Net Asset (NAB) report. Data analysis is used with quantitative analysis, multiple linear regression analysis using eviews 9. Population and sample in this research are PT Nikko Securities Indonesia. The result of multiple linear regression analysis was the coefficient of determination (R2) showed the result of 0.123819 or 12%. This means that the Inflation, Exchange Rate and Composite Stock Price Index (IHSG) variables can influence the return of PT Nikko Securities Indonesia's equity fund of 12% and 88% is influenced by other variables. Based on the result of the research, the variables of inflation and exchange rate have a negative and significant effect toward the return of PT Nikko Securities Indonesia's equity fund. While the variable of Composite Stock Price Index (IHSG) has a negative but not significant effect toward Return of Equity Fund of PT Nikko Securities Indonesia

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