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A line chart, also known as a line plot, line graph, or curve chart, shows data as a sequence of markers linked by straight line segments. It’s a simple chart that’s used in a variety of areas. The measurement points are sorted (usually by their x-axis value) and connected by straight line segments, similar to a scatter plot.

Because a line chart is frequently used to illustrate a pattern in data across time intervals – a time series – the line is usually drawn chronologically. They’re known as run charts in these situations.

Francis Hauksbee, Nicolaus Samuel Cruquius, Johann Heinrich Lambert, and William Playfair are usually attributed with some of the first known line charts.

Data obtained from studies is frequently represented using graphs in the experimental sciences. For instance, if data on a body’s speed at different periods in time were collected, the data might be shown using a data table like this:

Tables are a fantastic method to display accurate figures, but they’re not always the best way to comprehend the patterns those values reflect. Because of these characteristics, the table display is sometimes mistakenly confused with the data, while it is actually just another representation of the data.

Producing a graph or line chart of Speed vs. Time can help you understand the process represented by the data in the table. The graphic on the right illustrates such a visualization.

A mathematical function representing the best-fit trend of the dispersed data is frequently superimposed on charts. This layer is called a best-fit layer, and the graph that contains it is called a line graph.

It is straightforward to create a “best-fit” layer by connecting nearby data points with a set of line segments; nevertheless, such a “best-fit” is seldom an accurate depiction of the underlying scatter data’s trend.

The discontinuities in the slope of the best fit are extremely unlikely to coincide perfectly with the locations of the measurement results.

Despite the fact that the experimental error in the data is extremely unlikely to be insignificant, the curve falls perfectly across each of the data points.

The best-fit layer can highlight trends in the data in either scenario. Visual measurements of the gradient or area under the curve can also be done, leading to further inferences or findings from the data table.

A real best-fit layer should represent a continuous mathematical function whose parameters are generated using an appropriate error-minimization technique that correctly weights the error in the data values. Curve fitting features are commonly found in graphing applications and spreadsheets. Simple linear equations to more complicated quadratic, polynomial, exponential, and periodic curves are all examples of best-fit curves.

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