Dampak Brand Image dan Harga terhadap Keputusan Pembelian Produk Miniso

2021 ◽  
Vol 2 (2) ◽  
pp. 75-87
Author(s):  
Kardinah Indrianna Meutia ◽  
Hadita Hadita ◽  
Wirawan Widjarnarko
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Brand Image ◽  
Multiple Linear Regression Model ◽  
Purchasing Decisions ◽  
Business World ◽  
Independent Variables ◽  
Spss Software ◽  
Fierce Competition

The economy in the current era of globalization has fierce competition, especially in the business world, where each company moves to continue to make products primarily to meet what is needed by consumers and companies are always innovating to make products that are different from before and from  competitors and strive to be superior to other products.  This study was conducted with the aim of analyzing the independent variables which include brand image and price variables on their influence on the dependent variable, namely purchasing decisions.  This study uses multiple linear regression model and with classical assumption test using SPSS software version 24. Data were obtained primarily by distributing questionnaires to 162 students at Bhayangkara University, Jakarta Raya.  This study states that brand image and price variables can partially and significantly influence consumer purchasing decisions positively. The F test explains that the brand image and price variables together can influence purchasing decisions with results showing f-count>f-table.

A Mathematical Comparison of the Lachance - Traill Matrix Correction Procedure with Statistical Multiple Linear Regression Analysis in XRF Applications

1992 ◽  
Vol 36 ◽  
pp. 1-10
Author(s):  
Anthony J. Klimasara
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Model ◽  
Mathematical Meaning ◽  
Matrix Correction ◽  
Independent Variables ◽  
Xrf Analysis

AbstractIt will be shown that the Lachance-Traill XRF matrix correction equations can be derived from the statistical multiple linear regression model. By selecting and properly transforming the independent variables and then applying the statistical multiple linear regression model, the following form of the matrix correction equation is obtained:Furthermore, it will be shown that the Lachance-Traill influence coefficients have a deeper mathematical meaning. They can be related to the multiple regression coefficients of the transformed system:Finally, it will be proposed that the Lachance-Traill model is equivalent to the statistical multiple linear regression model with the transformed independent variables. Knowing these facts will simplify correction subroutines in Quantitative/Empirical XRF Analysis programs. These mathematical facts have already been implemented and presented in a paper: “Automated Quantitative XRF Analysis Software in Quality Control Applications” (Pacific-International Congress on X-ray Analytical Methods, Hawaii, 1991).This demonstrates that the Lachance-Traill model has a strong mathematical foundation and is naturally justified mathematically.

scholarly journals Revenue Autonomy and Entrepreneurship in The Municipalities of the Warmian-Masurian Voivodeship

2019 ◽  
Vol 14 (1) ◽  
pp. 75-86
Author(s):  
Anna Wichowska ◽  
Tomasz Wierzejski
Keyword(s):  
Linear Regression ◽  
Private Sector ◽  
Regression Model ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Agricultural Producers ◽  
Factors Affecting ◽  
Independent Variables ◽  
Construction Companies ◽  
Working Age

One of the most important problems in the proper functioning and fulfillment of entrusted tasks by municipalities in Poland is their high income independence. It depends, inter alia, on the broadly understood entrepreneurship undertaken in their area. Therefore the aim of this study was to identify the factors affecting entrepreneurship in the Warmian-Masurian Voivodeship in 2014–2016, which determined the revenue autonomy of municipalities in the region. The analysis was conducted with the use of a multiple linear regression model. Revenue autonomy, which is measured by the proportion of a municipality’s own-source revenues in total revenues, was the explained (dependent) variable. The initial group of explanatory (independent) variables consisted of 22 indicators linked with the operations of local businesses in the evaluated region. The key determinants of the revenue autonomy of municipalities were: the percentage of commercial partnerships in the total number of companies in the private sector, the percentage of private-sector companies in the total number of companies, the percentage of industrial and construction companies in the total number of companies, the percentage of self-employed in the total number of companies in the private sector, the number of agricultural producers, livestock breeders and hunting companies per 1000 residents, and the number of companies employing up to 9 people per 10,000 working-age residents.

The Impact of Climate Change on Agriculture in Morocco: An Econometric Study

2020 ◽  
Vol 1 (2) ◽  
pp. 19-28
Author(s):  
Faycel Tazigh
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Independent Variables ◽  
Temperature And Precipitation ◽  
The Impact ◽  
The Relationship

This paper aims to analyze the relationship that may exist between climate change and cereal yield in Morocco. In order to study this correlation between variables, we used the most common form of regression model which is the multiple linear regression model. There are two main uses of multiple linear regression model. The first one is to quantify the weight of impact that the independent variables had on the dependent variable. The second use is to predict not only the relationship that may found between variables but also their impacts. In our case, we have chosen temperature and precipitation as an independent variables and cereal yield as dependent variable.

FACTORS ASSOCIATED WITH CHILDREN’S HEIGHT AND BODY MASS INDEX IN LITHUANIA, 1990–2008

2015 ◽  
Vol 48 (4) ◽  
pp. 502-529 ◽  
Author(s):  
Andrej Suchomlinov ◽  
Janina Tutkuviene
Keyword(s):  
Body Mass Index ◽  
Linear Regression ◽  
Regression Model ◽  
Socioeconomic Factors ◽  
Multiple Linear Regression ◽  
Birth Order ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Factors Associated ◽  
Independent Variables

SummaryThe aim of the study was to reveal the ethnic and socioeconomic factors associated with height and body mass index (BMI) of children during the period of political and social transition in Lithuania in 1990–2008. Data were derived from the personal health records of 1491 children (762 boys and 729 girls) born in 1990 in Vilnius city and region. Height and BMI from birth up to the age of 18 years were investigated. Children were divided into groups according to their ethnicity, place of residence, father’s and mother’s occupation and birth order. Height and BMI were compared between the groups; a Bonferroni correction was applied. A multiple linear regression model was used to measure the effects of the independent variables on height and BMI. Girls living in Vilnius city were significantly taller in later life at the ages of 8 and 11 years. Sons of mothers employed as office workers appeared to be significantly taller at the ages of 7, 12, 14 and 15 years compared with the sons of labourers. First-born girls were taller at the age of 7 years than later-born girls of the same age (124.48±5.11 cm and 122.92±5.14 cm, respectively,p<0.001). Later-born children of both sexes had higher BMIs at birth compared with first-borns; however, first-born girls had higher BMIs at the age of 11 years compared with their later-born peers (17.78±2.87 kg/m² and 16.79±2.14 kg/m² respectively,p<0.001). In the multiple linear regression model, the five tested independent variables explained only up to 18% of total variability. Boys were more sensitive to ethnic and socioeconomic factors: ethnicity appeared to be a significant predictor of boys’ height at the age of 5 years (p<0.001), while birth order (p<0.001) predicted boys’ BMI at birth. In general, ethnicity, place of residence, father’s and mother’s occupation and birth order were not associated with children’s height and BMI in most age groups.

scholarly journals Enterprise Power Consumption Data and GDP Forecasting Based on Ensemble Algorithms

2021 ◽  
Vol 233 ◽  
pp. 01030
Author(s):  
Yibin Xu ◽  
Lu He ◽  
Ying Liang ◽  
Jianhong Si ◽  
Yonglong Bao
Keyword(s):  
Linear Regression ◽  
Power Consumption ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Independent Variables ◽  
Consumption Data ◽  
Dependent Variables ◽  
Ensemble Algorithms

This paper focuses on the development of regional GDP and proposes a method proposed for forecast of enterprise power consumption data and GDP based on ensemble algorithms. The enterprise power consumption data are used as independent variables and GDP data as dependent variables. A multiple linear regression model is selected as the primary learner for training and its outputs will be sorted into a new dataset of input features to train a secondary learner. The forecast of GDP is thus realized through ensemble learning.

scholarly journals The Use of Multiple Linear Regression Model for Predicting The Influence of Weather to The Prevalence of Newcastle Disease in Chicken in North Aceh

2008 ◽  
Vol 2 (1) ◽  
Author(s):  
Mahdi Abrar
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Newcastle Disease ◽  
Linear Regression Model ◽  
Minimum Temperature ◽  
Multiple Linear Regression Model ◽  
Maximum Temperature ◽  
Independent Variables ◽  
Dependent Variables

The objective of this research is to see the influence of weather on the prevalence of Newcastle Disease (ND) in chicken in Kabupaten Aceh Utara (North Aceh). Data used in this research were obtained from Dinas Peternakan North Aceh for the number of chicken suffered ND and from Badan Meteorologi dan Geofisika Lhokseumawe, North Aceh for the form of weather. Multiple Linear Regression Model with five independent variables (the average of rainfall per month, the average of maximum temperature, the average of minimum temperature, the velocity of the wind, and the average of humidity per month) was used to see the influence of wheather to the prevalence of Newcastle Disease. Proportion the number of chicken suffered from ND which is the ratio of the number of chicken suffered from ND to the total number of chicken was used as dependent variables. The result shows that the best model is Ŷ= 120.529278 – 1.33 x wind humidity + 1.907 x wind velocity.

An Enhanced Multiple Linear Regression Model for Seasonal Rainfall Prediction

2020 ◽  
Vol 10 (4) ◽  
pp. 473-483
Author(s):  
Pundra Chandra Shaker Reddy ◽  
Alladi Sureshbabu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Weather Prediction ◽  
Big Data Analytics ◽  
Multiple Linear Regression Model ◽  
Seasonal Rainfall ◽  
Climate Data ◽  
Rainfall Forecasting

Aims & Background: India is a country which has exemplary climate circumstances comprising of different seasons and topographical conditions like high temperatures, cold atmosphere, and drought, heavy rainfall seasonal wise. These utmost varieties in climate make us exact weather prediction is a challenging task. Majority people of the country depend on agriculture. Farmers require climate information to decide the planting. Weather prediction turns into an orientation in farming sector to deciding the start of the planting season and furthermore quality and amount of their harvesting. One of the variables are influencing agriculture is rainfall. Objectives & Methods: The main goal of this project is early and proper rainfall forecasting, that helpful to people who live in regions which are inclined natural calamities such as floods and it helps agriculturists for decision making in their crop and water management using big data analytics which produces high in terms of profit and production for farmers. In this project, we proposed an advanced automated framework called Enhanced Multiple Linear Regression Model (EMLRM) with MapReduce algorithm and Hadoop file system. We used climate data from IMD (Indian Metrological Department, Hyderabad) in 1901 to 2002 period. Results: Our experimental outcomes demonstrate that the proposed model forecasting the rainfall with better accuracy compared with other existing models. Conclusion: The results of the analysis will help the farmers to adopt effective modeling approach by anticipating long-term seasonal rainfall.

scholarly journals Magnetization Transfer Ratio of Peripheral Nerve and Skeletal Muscle

2021 ◽  
Author(s):  
Olivia Fösleitner ◽  
Véronique Schwehr ◽  
Tim Godel ◽  
Fabian Preisner ◽  
Philipp Bäumer ◽  
...  
Keyword(s):  
Skeletal Muscle ◽  
Body Weight ◽  
Sciatic Nerve ◽  
Linear Regression ◽  
Peripheral Nerve ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Magnetization Transfer ◽  
Multiple Linear Regression Model

Abstract Purpose To assess the correlation of peripheral nerve and skeletal muscle magnetization transfer ratio (MTR) with demographic variables. Methods In this study 59 healthy adults evenly distributed across 6 decades (mean age 50.5 years ±17.1, 29 women) underwent magnetization transfer imaging and high-resolution T2-weighted imaging of the sciatic nerve at 3 T. Mean sciatic nerve MTR as well as MTR of biceps femoris and vastus lateralis muscles were calculated based on manual segmentation on six representative slices. Correlations of MTR with age, body height, body weight, and body mass index (BMI) were expressed by Pearson coefficients. Best predictors for nerve and muscle MTR were determined using a multiple linear regression model with forward variable selection and fivefold cross-validation. Results Sciatic nerve MTR showed significant negative correlations with age (r = −0.47, p < 0.001), BMI (r = −0.44, p < 0.001), and body weight (r = −0.36, p = 0.006) but not with body height (p = 0.55). The multiple linear regression model determined age and BMI as best predictors for nerve MTR (R2 = 0.40). The MTR values were different between nerve and muscle tissue (p < 0.0001), but similar between muscles. Muscle MTR was associated with BMI (r = −0.46, p < 0.001 and r = −0.40, p = 0.002) and body weight (r = −0.36, p = 0.005 and r = −0.28, p = 0.035). The BMI was selected as best predictor for mean muscle MTR in the multiple linear regression model (R2 = 0.26). Conclusion Peripheral nerve MTR decreases with higher age and BMI. Studies that assess peripheral nerve MTR should consider age and BMI effects. Skeletal muscle MTR is primarily associated with BMI but overall less dependent on demographic variables.

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