The (x,y) points can be approximated by a linear equation.
The (x,y) points are almost linearly related.
To accurately describe the type of relationship shown by a scatter plot, I would need to see the plot itself. Generally, scatter plots can depict various relationships such as positive, negative, or no correlation. A positive relationship indicates that as one variable increases, the other also increases, while a negative relationship shows that as one variable increases, the other decreases. If the points are randomly scattered without any discernible pattern, it suggests no correlation.
Correlation is an estimate of a linear relationship between two variables and takes no account of non-linear relationship. If the relationship is quadratic and the domain is symmetric about some point, the correlation will be zero. It is, thus possible for the points on the scatter plot to lie exactly on a parabola while the calculated correlation is zero. In such a case, it is easy to make a prediction despite no correlation.
A scatter chart displays individual data points on a Cartesian plane, showing the relationship between two continuous variables, while a line chart connects these points with lines to illustrate trends over time or ordered categories. Scatter charts are used to identify correlations or distributions, whereas line charts emphasize the progression and continuity of data points. In essence, scatter charts focus on the distribution of data, while line charts emphasize trends and changes.
There is no such thing as a general pattern. Depending on the variables and the relationship between them, you can have points all over the plot, exactly on a straight line or a curve, or close to such a line or curve.
A line of best fit in a scatter plot illustrates the general trend or relationship between two variables. It minimizes the distance between itself and all the data points, helping to highlight patterns and predict values. By analyzing the slope and position of the line, you can infer whether the relationship is positive, negative, or non-existent. Ultimately, it aids in understanding the correlation between the variables represented in the plot.
A scatter plot
Points slope down as it moves to the right
It is called the line of best fit
A scatter plot.A scatter plot.A scatter plot.A scatter plot.
a graph in which the values of two variables are plotted along two axes, the pattern of the resulting points revealing any correlation present. Scatter plots are similar to line graphs in that they use horizontal and vertical axes to plot data points. However, they have a very specific purpose. Scatter plots show how much one variable is affected by another. The relationship between two variables is called their correlation .
Correlation is an estimate of a linear relationship between two variables and takes no account of non-linear relationship. If the relationship is quadratic and the domain is symmetric about some point, the correlation will be zero. It is, thus possible for the points on the scatter plot to lie exactly on a parabola while the calculated correlation is zero. In such a case, it is easy to make a prediction despite no correlation.
A scatter chart displays individual data points on a Cartesian plane, showing the relationship between two continuous variables, while a line chart connects these points with lines to illustrate trends over time or ordered categories. Scatter charts are used to identify correlations or distributions, whereas line charts emphasize the progression and continuity of data points. In essence, scatter charts focus on the distribution of data, while line charts emphasize trends and changes.
There is no such thing as a general pattern. Depending on the variables and the relationship between them, you can have points all over the plot, exactly on a straight line or a curve, or close to such a line or curve.
A scatter plot will show the data points on a straight line through the origin, whose slope is the constant of proportionality.
It can showwhether or not there is any relationship between two variables,the nature of the relationship - linear, quadratic, inverse, power etc,precision of relationship: the spread or scatter around the curve of best fit,whether the scatter is constant or changes (heteroscedasticity),presence of outliers,clustering (eg heights v/s weight of adults may show one cluster of points for men and another for women. If so, gender is another relevant variable).
a scatter graph
A scatter plot is commonly used to compare or determine the relationship between two variables. It displays individual data points on a Cartesian plane, allowing for visual assessment of correlations, trends, or patterns. Additionally, line graphs can also be employed when illustrating the relationship between variables over time.