To determine the type of correlation shown in a scatter graph, you would typically look at the pattern of the plotted points. If the points trend upwards from left to right, it indicates a positive correlation. Conversely, if the points trend downwards, it suggests a negative correlation. If the points are scattered without any discernible pattern, it indicates little to no correlation.
A scatterplot shows a correlation when there is a discernible pattern or trend in the points plotted on the graph. This can be a positive correlation, where points trend upwards, indicating that as one variable increases, the other does too; or a negative correlation, where points trend downwards, indicating that as one variable increases, the other decreases. If the points are randomly scattered without any clear pattern, it suggests little to no correlation. The strength of the correlation can be assessed visually or quantified using correlation coefficients.
A positive correlation is where the data has an increasing pattern. As X increases, Y also increases.
A scatter plot shows a correlation when there is a discernible pattern in the distribution of data points, indicating a relationship between the two variables. If the points trend upward from left to right, it suggests a positive correlation, while a downward trend indicates a negative correlation. The strength of the correlation can be assessed by how closely the points cluster around a line or curve. If there is no apparent pattern, the variables are likely not correlated.
If you remove certain data points from a dataset, the correlation coefficient may be affected depending on the nature of the relationship between the removed data points and the remaining data points. If the removed data points have a strong relationship with the remaining data, the correlation coefficient may change significantly. However, if the removed data points have a weak or no relationship with the remaining data, the impact on the correlation coefficient may be minimal.
To determine the type of correlation shown in a scatter graph, you would typically look at the pattern of the plotted points. If the points trend upwards from left to right, it indicates a positive correlation. Conversely, if the points trend downwards, it suggests a negative correlation. If the points are scattered without any discernible pattern, it indicates little to no correlation.
A scatterplot shows a correlation when there is a discernible pattern or trend in the points plotted on the graph. This can be a positive correlation, where points trend upwards, indicating that as one variable increases, the other does too; or a negative correlation, where points trend downwards, indicating that as one variable increases, the other decreases. If the points are randomly scattered without any clear pattern, it suggests little to no correlation. The strength of the correlation can be assessed visually or quantified using correlation coefficients.
A positive correlation is where the data has an increasing pattern. As X increases, Y also increases.
A scatter plot shows a correlation when there is a discernible pattern in the distribution of data points, indicating a relationship between the two variables. If the points trend upward from left to right, it suggests a positive correlation, while a downward trend indicates a negative correlation. The strength of the correlation can be assessed by how closely the points cluster around a line or curve. If there is no apparent pattern, the variables are likely not correlated.
B. V. K. Vijaya Kumar has written: 'Correlation pattern recognition' -- subject(s): Correlation (Statistics), Pattern recognition systems
they are frequency, pattern, correlation and statistical technique.
Auto correlation is the correlation of one signal with itself. Cross correlation is the correlation of one signal with a different signal.
No correlation. Answer provided by
If you remove certain data points from a dataset, the correlation coefficient may be affected depending on the nature of the relationship between the removed data points and the remaining data points. If the removed data points have a strong relationship with the remaining data, the correlation coefficient may change significantly. However, if the removed data points have a weak or no relationship with the remaining data, the impact on the correlation coefficient may be minimal.
positive correlation-negative correlation and no correlation
No. The strongest correlation coefficient is +1 (positive correlation) and -1 (negative correlation).
In a scatter plot, a positive correlation is indicated by points that trend upwards from left to right, suggesting that as one variable increases, the other does as well. A negative correlation is shown by points that trend downwards from left to right, indicating that as one variable increases, the other decreases. If the points are scattered randomly without any discernible pattern, it suggests no correlation between the variables. The strength and direction of the correlation can also be visually assessed by how closely the points cluster around an imaginary line.