Cameroonian Consumers' Attitude towards Tv Advertising Form and Content

2021 ◽  
Vol 6 (3) ◽  
pp. 167-180
Author(s):  
ADAMOU HADJI Mohamadou ◽  
KUATE KAMGA Sangeniss Leblanc
Keyword(s):  
Social Networks ◽  
Principal Component Analysis ◽  
Linear Regression ◽  
Principal Component ◽  
Consumer Attitudes ◽  
Television Advertising ◽  
Jel Classification ◽  
Convenience Sample ◽  
Tv Advertising ◽  
Advertising Content

Objective - The research focuses on analysing the TV advertising in an environment dominated by social networks and its ever-increasing cost in order to evaluate the influence of its content and form on consumer attitudes in Cameroon. Methodology/Technique - A survey was conducted with a convenience sample of 337 individuals in the cities of Douala, Yaoundé, and Maroua. Principal component analysis and multiple linear regression were used to assess the results. Findings - The results reveal the significant influence of the content (character and language) and even the form (traditional advertising and placement) of television advertising on consumer attitudes. Novelty - It contributes to the development of the study of television advertising in relation to the attitude of the consumers in Cameroon by integrating the content and form aspect. Type of Paper - Empirical. Keywords: Attitude, Consumer, Advertising content, Advertisement form, Cameroon JEL Classification: D1, M31, M37.

Principal component analysis for functional data

2018 ◽  
Author(s):  
Peter Hall
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Functional Data ◽  
Weighted Least Squares ◽  
Principal Component ◽  
Adaptive Methods ◽  
Component Analysis ◽  
Infinite Dimensional ◽  
Role Of Principal ◽  
Functional Linear Regression

This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how principal component functions are linked to the amount of probability mass contained in a small ball around a given, fixed function, and how this property can be used to define a simple, easily estimable density surrogate. The article concludes by explaining the use of PCA for estimating log-density.

Dimensioni e direttrici del mutamento socio-economico in prospettiva territoriale dal 1951 a oggi

2013 ◽  
pp. 78-92
Author(s):  
Domenico Maddaloni ◽  
Fiorenzo Parziale
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Southern Italy ◽  
Principal Component ◽  
Research Strategy ◽  
Two Phase ◽  
Economic Changes ◽  
Local Contexts ◽  
Italian Provinces ◽  
History Of

In this study we go back to examine the economic and sociological changes throughout the local contexts and divisions of our country. The instrument used is a research strategy that combines a two-phase principal component analysis developed by Di Franco and Marradi with multiple linear regression. From data inherent to four key moments in the recent history of Southern Italy and the whole country - 1951, 1971, 1991 and 2007 - we obtain four «photographs» of dimensions that clarify the structure of the selected variables. We then propose two models of path analysis that underline the causal links between the factors emerged from the PCA, in order to reconstruct the socio-economic changes in the Italian provinces from 1951 to 2007.

The Quantitative Evaluation on the Response of Climate Change upon the Sustainability of the Agricultural Production

2010 ◽  
Vol 129-131 ◽  
pp. 1161-1165
Author(s):  
Lin Chun Hou ◽  
Hui Qin Li
Keyword(s):  
Climate Change ◽  
Principal Component Analysis ◽  
Linear Regression ◽  
Agricultural Production ◽  
Agricultural Productivity ◽  
Principal Component ◽  
Environmental Condition ◽  
Land Utilization ◽  
Agricultural Society ◽  
Per Capita

The aim: quantitatively evaluate the response of climate change upon the sustainability of the agricultural production. The method: the paper selected two regions (Hubei and shan’xi province) which represented different climate environment, utilized modern statistic data, Principal Component Analysis and multivariate linear regression to quantitatively evaluate the influence of climate change upon agricultural production through isolating climate environment from arable area, land utilization and management and landform and so on. The conclusion: The study indicated that when environmental condition turned good to agriculture, the function of environmental condition to agriculture relatively decreased; the capability of agricultural society and production decreased too, and people could select the land to cultivate, where agricultural productivity is higher. And that when environmental condition turned bad to agriculture, the function of environmental condition to agriculture relatively increased; the capability of agricultural society and production increased, too; people could not put emphasis on the land where agricultural productivity is higher, whereas focused on productivity per capita.

The Research of Wine Quality Evaluation Based on Multiple Linear Regression

2013 ◽  
Vol 756-759 ◽  
pp. 2489-2493
Author(s):  
Huai Hui Liu ◽  
Wen Long Ji ◽  
Peng Zhang ◽  
Chuan Wen Yao
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Quality Evaluation ◽  
Evaluation Model ◽  
Multiple Linear Regression Model ◽  
Principal Component ◽  
Wine Grape ◽  
Wine Quality

Through the establishment of evaluation model based on principal component analysis, select 8 principal components from nearly 30 indexes of wine grape. Then we establish the multiple linear regression model and analyse the association between physicochemical indexes of wine grape and wine, and the influence of physicochemical indexes of wine grape and wine on wine quality. Finally study whether we could use the physicochemical indexes to evaluate the wine quality.

scholarly journals Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis

2018 ◽  
Vol 26 (0) ◽  
pp. 170-176 ◽  
Author(s):  
Stephen J.H. Yang ◽  
Owen H.T. Lu ◽  
Anna Y.Q. Huang ◽  
Jeff C.H. Huang ◽  
Hiroaki Ogata ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Academic Performance ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Principal Component ◽  
Component Analysis

scholarly journals Relationship between the Physical Properties and Hand of Jean Fabric

2016 ◽  
Vol 16 (3) ◽  
pp. 138-145 ◽  
Author(s):  
Atsushi Kawamura ◽  
Chunhong Zhu ◽  
Julie Peiffer ◽  
KyoungOk Kim ◽  
Yi Li ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Physical Properties ◽  
Multiple Linear Regression ◽  
Principal Components ◽  
Evaluation System ◽  
Principal Component ◽  
Semantic Differential ◽  
Differential Method ◽  
Fabric Hand

Abstract We investigated the distinctive characteristics of jean fabrics (denim fabrics obtained from jeans) and compared the physical properties and the hand. We used 13 kinds of jean fabric from commercial jeans and 26 other fabric types. The physical properties were measured using the Kawabata evaluation system, and the fabric hand was evaluated by 20 subjects using a semantic differential method. To characterise the hand of jean fabrics compared with other fabrics, we used principal component analysis and obtained three principal components. We found that jean fabrics were characterised by the second principal component, which was affected by feelings of thickness and weight. We further characterised the jean fabrics according to ‘softness & smoothness’ and ‘non-fullness’, depending on country of origin and type of manufacturer. The three principal components were analysed using multiple linear regression to characterise the components according to the physical properties. We explained the hand of fabrics including jean fabrics using its association with physical properties.

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