Any straight line can be defined by the slope (1) and the point at which the line crosses the vertical axis of the graph (intercept) (2). The general formula for the linear model is the following:Regression analysis refers to fitting a linear model to data and using it to predict values of an outcome variable (dependent variable) from one or more predictor variables (independent variables). The residuals are the differences between what the model predicts and the actual outcome. The residual sum of squares is used to assess the ‘goodness-of-fit’ of the model on the data. The smaller the residual sum of squares, the better the fit. Ordinary least squares regression refers to defining the regression models for which the sum of squared errors is the minimum it can be given the data. The sum of squared differences is the total sum of squares and represents how good the mean is as a model of the observed outcome scores. The model sum of squares represents how well the model can predict the data. The larger the model sum of squares, the better the model can predict the data. The residual sum of squares uses the differences between the observed data and the model and shows how much of the data the model cannot predict.The proportion of improvement due to the model compared to using the mean as a predictor can be calculated using the following formula:This value represents the amount of variance in the outcome explained by the model relative to how much variation there was to explain. The F-statistic can be calculated using the following formulas:‘k’ represents the degrees of freedom and denotes the number of predictors. The F-statistic can also be used t test the significance of with the null hypothesis being that is zero. It uses the following formula:Individual predictors can be tested using the t-statistic.BIAS IN LINEAR MODELSAn outlier is a case that differs substantially from the main trend in the data. Standardized residuals...