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sb2_regression_analysis

# sb2_regression_analysis - Evaluating Socio Economic...

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Regression analysis [PLEASE NOTE: The following SOURCEBOOK text is designed for presentation as part of the Internet-based collection of materials for Evaluating Socio Economic Development, and should be viewed in this context. Introductory remarks are provided on the site www.evalsed.info] REGRESSION ANALYSIS Description of the technique Regression analysis is the statistical technique that identifies the relationship between two or more quantitative variables: a dependent variable whose value is to be predicted, and an independent or explanatory variable (or variables), about which knowledge is available. The technique is used to find the equation that represents the relationship between the variables. A simple regression analysis can show that the relation between an independent variable X and a dependent variable Y is linear, using the simple linear regression equation Y= a + bX (where a and b are constants). Multiple regression will provide an equation that predicts one variable from two or more independent variables, Y= a + bX 1 + cX 2+ dX 3. Purposes of the technique Regression analysis is used to understand the statistical dependence of one variable on other variables. The technique can show what proportion of variance between variables is due to the dependent variable, and what proportion is due to the independent variables. The relation between the variables can be illustrated graphically, or more usually using an equation. Box 1 provides an example of the approach in practice. Box 1: Evaluation of ESF training courses in Spain In 1995, the Departments of Economic Analysis and Applied Economics at the Independent University in Madrid, were involved in evaluating the impacts of training measures funded through the European Social Fund. The evaluation aimed at identifying the numbers and profile of trainees who entered employment after attending the course. A survey was conducted on a sample of 13,127 actual or potential trainees throughout Spain (although excluding three regions). An explanatory model was constructed with the following variables: Variables to be explained (dependent variables): the employment rate and the length of time looking for a job; Explanatory variables: gender, age, qualifications, place of residence, type of unemployment (long-term, search for a first job), and professional experience; A special explanatory variable was added: participation in the programme (duration of ESF training). The model consisted of continuous variables such as age or length of time seeking employment, as well as discontinuous variables such as gender or the outcome of finding a job. The regression analysis was used to analyse the results of the questionnaire survey with respect to the impact of the programme on the employment of trainees. Results from the regression analysis indicated that: Men were 5% more likely to find a job than women; Regional locality is important: trainees living in the Balearic Islands have about 20% more chance of finding a job

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sb2_regression_analysis - Evaluating Socio Economic...

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