Watch
Watching this resources will notify you when proposed changes or new versions are created so you can keep track of improvements that have been made.
Favorite
Favoriting this resource allows you to save it in the “My Resources” tab of your account. There, you can easily access this resource later when you’re ready to customize it or assign it to your students.
Plotting Lines
A line graph is a type of chart which displays information as a series of data points connected by straight line segments.
Learning Objective

Explain the principles of plotting a line graph
Key Points
 A line chart is often used to visualize a trend in data over intervals of time – a time series – thus the line is often drawn chronologically.
 A line chart is typically drawn bordered by two perpendicular lines, called axes. The horizontal axis is called the xaxis and the vertical axis is called the yaxis.
 Typically the yaxis represents the dependent variable and the xaxis (sometimes called the abscissa) represents the independent variable.
 In statistics, charts often include an overlaid mathematical function depicting the bestfit trend of the scattered data.
Terms

line
a path through two or more points (compare ‘segment'); a continuous mark, including as made by a pen; any path, curved or straight

bell curve
In mathematics, the bellshaped curve that is typical of the normal distribution.

gradient
of a function y = f(x) or the graph of such a function, the rate of change of y with respect to x, that is, the amount by which y changes for a certain (often unit) change in x
Full Text
A line graph is a type of chart which displays information as a series of data points connected by straight line segments. It is a basic type of chart common in many fields. It is similar to a scatter plot except that the measurement points are ordered (typically by their xaxis value) and joined with straight line segments. A line chart is often used to visualize a trend in data over intervals of time – a time series – thus the line is often drawn chronologically.
Plotting
A line chart is typically drawn bordered by two perpendicular lines, called axes. The horizontal axis is called the xaxis and the vertical axis is called the yaxis. To aid visual measurement, there may be additional lines drawn parallel either axis. If lines are drawn parallel to both axes, the resulting lattice is called a grid.
Each axis represents one of the data quantities to be plotted. Typically the yaxis represents the dependent variable and the xaxis (sometimes called the abscissa) represents the independent variable. The chart can then be referred to as a graph of quantity one versus quantity two, plotting quantity one up the yaxis and quantity two along the xaxis.
Example
In the experimental sciences, such as statistics, data collected from experiments are often visualized by a graph. For example, if one were to collect data on the speed of a body at certain points in time, one could visualize the data to look like the graph in :
Data Table
A data table showing elapsed time and measured speed.
The table "visualization" is a great way of displaying exact values, but can be a poor way to understand the underlying patterns that those values represent. Understanding the process described by the data in the table is aided by producing a graph or line chart of Speed versus Time:
Line chart
A graph of speed versus time
BestFit
In statistics, charts often include an overlaid mathematical function depicting the bestfit trend of the scattered data. This layer is referred to as a bestfit layer and the graph containing this layer is often referred to as a line graph.
It is simple to construct a "bestfit" layer consisting of a set of line segments connecting adjacent data points; however, such a "bestfit" is usually not an ideal representation of the trend of the underlying scatter data for the following reasons:
1. It is highly improbable that the discontinuities in the slope of the bestfit would correspond exactly with the positions of the measurement values.
2. It is highly unlikely that the experimental error in the data is negligible, yet the curve falls exactly through each of the data points.
In either case, the bestfit layer can reveal trends in the data. Further, measurements such as the gradient or the area under the curve can be made visually, leading to more conclusions or results from the data.
A true bestfit layer should depict a continuous mathematical function whose parameters are determined by using a suitable errorminimization scheme, which appropriately weights the error in the data values. Such curve fitting functionality is often found in graphing software or spreadsheets. Bestfit curves may vary from simple linear equations to more complex quadratic, polynomial, exponential, and periodic curves. The socalled "bell curve", or normal distribution often used in statistics, is a Gaussian function.
Assign just this concept or entire chapters to your class for free.
Key Term Reference
 datum
 Appears in these related concepts: Applications of Statistics, Outliers, and Change of Scale
 dependent variable
 Appears in these related concepts: Evaluating Model Utility, Formulating the Hypothesis, and Converting between Exponential and Logarithmic Equations
 distribution
 Appears in these related concepts: Application of Knowledge, Monte Carlo Simulation, and Selling to Consumers
 error
 Appears in these related concepts: Inferential Statistics, Estimation, and Precise Definition of a Limit
 experiment
 Appears in these related concepts: Experiments, Primary Market Research, and Descriptive and Correlational Statistics
 graph
 Appears in these related concepts: Graphing on Computers and Calculators, Reading Points on a Graph, and Graphing Functions
 independent
 Appears in these related concepts: Conditional Probability, Unions and Intersections, and Party Identification
 independent variable
 Appears in these related concepts: Distorting the Truth with Descriptive Statistics, Experimental Design, and Experimental Research
 normal distribution
 Appears in these related concepts: Shapes of Sampling Distributions, The Average and the Histogram, and Standard Deviation: Definition and Calculation
 plot
 Appears in these related concepts: Misleading Graphs, Making a Box Model, and Plotting Points on a Graph
 polynomial
 Appears in these related concepts: Basics of Graphing Polynomial Functions, Polynomials: Introduction, Addition, and Subtraction, and Greatest Common Factor and Factoring by Grouping
 scatter plot
 Appears in these related concepts: Graphs for Quantitative Data, Plotting the Residuals, and Statistical Graphics
 slope
 Appears in these related concepts: Making Inferences About the Slope, Slope and Intercept, and Applications of Linear Functions and Slope
 statistics
 Appears in these related concepts: Understanding Statistics, Population Demography, and Basic Inferential Statistics
 trend
 Appears in these related concepts: Line of Best Fit, Selected Financial Ratios and Analyses, and Scanning and Analysis
 variable
 Appears in these related concepts: Related Rates, Math Review, and Psychology and the Scientific Method: From Theory to Conclusion
Sources
Boundless vets and curates highquality, openly licensed content from around the Internet. This particular resource used the following sources:
Cite This Source
Source: Boundless. “Plotting Lines.” Boundless Statistics. Boundless, 21 Jul. 2015. Retrieved 29 Aug. 2015 from https://www.boundless.com/statistics/textbooks/boundlessstatisticstextbook/visualizingdata3/graphingdata19/plottinglines954414/