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Which of the following is not required when fitting a least squares regression line. Linearity. The data should show a linear trend. If there is a nonlinear trend, an advanced regression method from another book or later course should be applied. Nearly normal residuals. Generally the residuals must be nearly normal. When this condition is found to be unreasonable, it is usually because of outliers or concerns about influential points. Independent observations. Be cautious about applying regression to time series data, which are sequential observations in time such as a stock price each day. Such data may have an underlying structure that should be considered in a model and analysis. Small Residuals. In many applications, a residual twice as large as another residual is more than twice as bad. Constant variability. The variability of points around the least squares line remains roughly constant.