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As per available reports about 10 relevant Journals, 8 Conference, 158 Conferences Proceedings are presently dedicated exclusively to Regression analysis and about 22 Open Access articles are being published on Regression Analysis.
Regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modelling and analysing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'criterion variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on aquantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution. Topics like: Linear Regression, Interpolation & Extrapolation, and Software are of interested.
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Scope & Importance:
Regression analysis conference aims at bringing together researchers and practitioners to discuss recent developments in statistical methods, regression analysis computational methods, methodologies for data analysis and applications in statistics.
Market Analysis:
Regression analysis is one of the most frequently used tools in market research. In its simplest form, regression analysis allows market researchers to analyze relationships between one independent and one dependent variable. In marketing applications, the dependent variable is usually the outcome we care about (e.g., sales), while the independent variables are the instruments we have to achieve those outcomes with (e.g., pricing or advertising). Regression analysis can provide insights that few other techniques can. The key benefits of using regression analysis are that it can:
Make predictions. 
Knowing about the effects of independent variables on dependent variables can help market researchers in many different ways. For example, it can help direct spending if we know promotional activities significantly increases sales.
Knowing about the relative strength of effects is useful for marketers because it may help answer questions such as whether sales depend more on price or on promotions. Regression analysis also allows us to compare the effects of variables measured on different scales such as the effect of price changes (e.g., measured in $) and the number of promotional activities.
Regression analysis can also help to make predictions. For example, if we have estimated a regression model using data on sales, prices, and promotional activities, the results from this regression analysis could provide a precise answer to what would happen to sales if prices were to increase by 5% and promotional activities were to increase by 10%. Such precise answers can help (marketing) managers make sound decisions. Furthermore, by providing various scenarios, such as calculating the sales effects of price increases of 5%, 10%, and 15%, managers can evaluate marketing plans and create marketing strategies.
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This page was last updated on December 23, 2024
