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It is strongly recommended to screen the data graphically (e.g. Alternatively, if it is not obvious which model best fits the data, an option is to try several models and select among them. For example, if the data resemble an exponential function, an exponential model is to be used. It is strongly advised to view early a scatterplot of your data if the plot resembles a mathematical function you recognize, fit the data to that type of model. If the transformation does not help then a more complicated model may be needed. If the variables appear to be related linearly, a simple linear regression model can be used but in the case that the variables are not linearly related, data transformation might help. It is essential to plot the data in order to determine which model to use for each depedent variable. In most statistical packages, a curve estimation procedure produces curve estimation regression statistics and related plots for many different models (linear, logarithmic, inverse, quadratic, cubic, power, S-curve, logistic, exponential etc.). Logistic regression coefficients can be used to estimate odds ratios for each of the independent variables in the model. Logistic regression is similar to a linear regression but is suited to models where the dependent variable is dichotomous. Linear regression is the procedure that estimates the coefficients of the linear equation, involving one or more independent variables that best predict the value of the dependent variable which should be quantitative. In this article we focus in linear regression. These methods allow us to assess the impact of multiple variables (covariates and factors) in the same model 3, 4. By modeling we try to predict the outcome (Y) based on values of a set of predictor variables (Xi). The type of the regression model depends on the type of the distribution of Y if it is continuous and approximately normal we use linear regression model if dichotomous we use logistic regression if Poisson or multinomial we use log-linear analysis if time-to-event data in the presence of censored cases (survival-type) we use Cox regression as a method for modeling. There are various types of regression analysis. An option to answer this question is to employ regression analysis in order to model its relationship. One of the most important and common question concerning if there is statistical relationship between a response variable (Y) and explanatory variables (Xi). The goal in any data analysis is to extract from raw information the accurate estimation. Proper use of any approach requires careful interpretation of statistics 1, 2. Another approach, the Bayesian, uses data to improve existing (prior) estimates in light of new data. Frequentist approaches derive estimates by using probabilities of data (either p-values or likelihoods) as measures of compatibility between data and hypotheses, or as measures of the relative support that data provide hypotheses. It is argued that the question if the pair of limits produced from a study contains the true parameter could not be answered by the ordinary (frequentist) theory of confidence intervals 1. An interval estimation procedure will, in 95% of repetitions (identical studies in all respects except for random error), produce limits that contain the true parameters. The range of values, for which the p-value exceeds a specified alpha level (typically 0.05) is called confidence interval. One way to account for is to compute p-values for a range of possible parameter values (including the null). In the estimation process, the random error is not avoidable. Confounding, measurement errors, selection bias and random errors make unlikely the point estimates to equal the true ones. Usually point estimates are the measures of associations or of the magnitude of effects. Inferential statistics are used to answer questions about the data, to test hypotheses (formulating the alternative or null hypotheses), to generate a measure of effect, typically a ratio of rates or risks, to describe associations (correlations) or to model relationships (regression) within the data and, in many other functions. Statistics are used in medicine for data description and inference.