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Tham khảo tài liệu 'giáo trình marketing nghiên cứu - phần 19', kinh doanh - tiếp thị, tiếp thị - bán hàng phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Regression Analysis in Marketing Research Understanding Prediction Prediction: statement of what is believed will happen in the future made on the basis of past experience or prior observation Ch 19 Understanding Prediction Two Approaches Two approaches to prediction: Extrapolation: detects a pattern in the past and projects it into the future Predictive model: uses relationships among variables to make a prediction Ch 19 Understanding Prediction Goodness of Prediction All predictions should be judged as to their “goodness” (accuracy). The goodness of a prediction is based on examination of the residuals (errors: comparisons of predictions to actual values). Ch 19 Analysis of Residuals Ch 19 Linear Relationships and Regression Analysis Regression analysis is a predictive analysis technique in which one or more variables are used to predict the level of another by use of the straight-line formula, y=a+bx. Ch 19 Bivariate Linear Regression Analysis Bivariate . | Regression Analysis in Marketing Research Understanding Prediction Prediction: statement of what is believed will happen in the future made on the basis of past experience or prior observation Ch 19 Understanding Prediction Two Approaches Two approaches to prediction: Extrapolation: detects a pattern in the past and projects it into the future Predictive model: uses relationships among variables to make a prediction Ch 19 Understanding Prediction Goodness of Prediction All predictions should be judged as to their “goodness” (accuracy). The goodness of a prediction is based on examination of the residuals (errors: comparisons of predictions to actual values). Ch 19 Analysis of Residuals Ch 19 Linear Relationships and Regression Analysis Regression analysis is a predictive analysis technique in which one or more variables are used to predict the level of another by use of the straight-line formula, y=a+bx. Ch 19 Bivariate Linear Regression Analysis Bivariate regression analysis is a type of regression in which only two variables are used in the regression, predictive model. One variable is termed the dependent variable (y), the other is termed the independent variable (x). The independent variable is used to predict the dependent variable, and it is the x in the regression formula. Ch 19 Bivariate Linear Regression Analysis With bivariate analysis, one variable is used to predict another variable. The straight-line equation is the basis of regression analysis. Ch 19 Bivariate Linear Regression Analysis Ch 19 Bivariate Linear Regression Analysis: Basic Procedure Independent variable: used to predict the independent variable (x in the regression straight-line equation) Dependent variable: that which is predicted (y in the regression straight-line equation) Least squares criterion: used in regression analysis; guarantees that the “best” straight-line slope and intercept will be calculated Ch 19 Bivariate Linear Regression .
