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This is the classic 'X vs Y' chart - indeed, some packages call it just that. Ideally suited for continuous variables in X and Y, it usually requires that a line of best fit is drawn. Generally this will involve linear regression, but there might be cases when a spline or quadratic function will be required. Will the constants of the fitted curve or line be available, and can they be added to the chart?
The line chart is a favourite of scientific workers. With date variables, this sort of chart is particularly useful for showing seasonal variations. The line and scatter charts are related and have many similar features - the line being a form of scatter chart in which the data points are connected, but a data marker is rarely shown. In the scatter chart, it is assumed that X and Y are continuous variables, while the X-axis for a line chart may well be a discrete variable. It may be necessary to fit a curve or straight line through the data points, either to show a trend or to estimate a line of best-fit via regression to act as a standard curve. What level of complexity of curve-fitting will be required? Will statistics on the data (such as standard deviation, mean etc.) be required? Will correlation coefficients be required on lines?
How many data series will be plotted? It is sensible to limit the amount of information contained in a chart, so that none of the information can be overlooked, and to avoid a cluttered presentation. Two simple charts are better than one over-complicated one. Six data series should be regarded as a maximum.
Stockbrokers and laboratory scientists both use charts which show an intermediate value between two extremes. This sort of presentation is extremely important in medical and scientific work. Many packages which are designed for business use have high-low-close facilities which can be adapted to showing error bars, however some use terminators to the bar which do not fit in with scientific practice