scholarly journals The Analysis of the Influence Factors of China’s Outbound Tourism Market

2018 ◽  
Vol 228 ◽  
pp. 05004
Author(s):  
He Liu
Keyword(s):  
Exchange Rate ◽  
Quantitative Analysis ◽  
Linear Regression ◽  
Qualitative Analysis ◽  
Regression Model ◽  
Linear Regression Model ◽  
Influence Factors ◽  
Multiple Linear Regression Model ◽  
Literature Study ◽  
Econometric Methods

Outbound tourism gets special attention from tourism academic circles and tourism companies recently. And now China has become the world’s largest outbound tourism consumer. So this paper is written to research the influence factors of China’s outbound tourism market, providing reference for the study of China’s outbound tourism market. The literature study have found that the number of China’s outbound tourism related to the duration of their leisure trips, exchange rate, residents’ deposits and the number of tourism agency. By using of the econometric methods and R-studio, we build a multiple linear regression model. In the end, we get a conclusion by quantitative analysis of data and qualitative analysis of tourism’s law. We found that there are linear relations exists in logarithm of the number of China’s outbound tourism, the residents’ deposits ,the logarithm of duration of their leisure trips and the logarithm of number of tourism agency.

scholarly journals PENGARUH INFLASI, BI RATE, KURS DAN INDEKS DOW JONES INDUSTRIAL AVERAGE TERHADAP JAKARTA ISLAMIC INDEX (JII)

Media Ekonomi ◽  
2019 ◽  
Vol 25 (2) ◽  
pp. 87
Author(s):  
Muhammad Rasyid Ridha ◽  
Harmaini Harmaini
Keyword(s):  
Exchange Rate ◽  
Linear Regression ◽  
Regression Model ◽  
Foreign Exchange ◽  
Significant Influence ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Analysis Method ◽  
Dow Jones Industrial Average ◽  
Research Show

<em>This research discusses the influence of inflation, BI Rate, Exchange rate (IDR/USD) and Dow Jones Industrial Average</em>. <em>The analysis method used is multiple linear regression model with α = 5%. With EViews 9.0 applications.</em> <em>The results of this research show that inflation, BI Rate, Foreign Exchange and Dow Jones Industrial Average simultaneously had significant influence towards on the Jakarta Islamic Index (JII). Meanwhile, partially Inflation had positive and significant influence towards on the JII. BI Rate partially had negative and significant influence towards on the JII. But Exchange rate (IDR/USD) partially do not influence on the JII and Dow Jones partially had positive and significant influence towards on the JII</em>.

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.

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.

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