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What type of correlation does the scatter graph show?
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.
When does a scatterplot show a 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.
What is a postitive correlation between data sets?
A positive correlation is where the data has an increasing pattern. As X increases, Y also increases.
When does a scatter plot show a correlation?
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.
When you removed from the data set how would the correlation coefficient be affected?
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.
What type of correlation does the scatter graph show?
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.
When does a scatterplot show a 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.
What is a postitive correlation between data sets?
A positive correlation is where the data has an increasing pattern. As X increases, Y also increases.
When does a scatter plot show a correlation?
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.
What has the author B V K Vijaya Kumar written?
B. V. K. Vijaya Kumar has written: 'Correlation pattern recognition' -- subject(s): Correlation (Statistics), Pattern recognition systems
Which of the following is technique for regonizing an attack signature?
they are frequency, pattern, correlation and statistical technique.
Auto correlation and cross correlation?
Auto correlation is the correlation of one signal with itself. Cross correlation is the correlation of one signal with a different signal.
When the data points on a graph are scattered with no clear pattern there is between the two variables?
No correlation. Answer provided by
When you removed from the data set how would the correlation coefficient be affected?
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.
What are three types of correlations?
positive correlation-negative correlation and no correlation
Is 0.5 the strongest correlation coefficient?
No. The strongest correlation coefficient is +1 (positive correlation) and -1 (negative correlation).
How can you tell from a scatter plot whether two variables have a positive correlation a negative correlation or no 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.
