Strong Correlation Scatter Plot

A scatterplot displays the strength, direction, and form of the relationship between two quantitative variables. A correlation coefficient measures the strength of that relationship. Calculating a Pearson correlation coefficient requires the assumption that the relationship between the two variables is linear.

The strength of the relationship is determined by how closely the scatter plot follows a single straight line the closer the points are to that line, the stronger the relationship. The scatter plots in Figure 8.78 to Figure 8.84 depict varying strengths and directions of linear relationships.

This is an example of a strong relationship. In the bottom scatterplot, the points also follow the linear pattern, but much less closely, and therefore we can say that the relationship is weaker. In general, though, assessing the strength of a relationship just by looking at the scatterplot is quite problematic, and we need a numerical measure

scatter plot A scatter plot is a plot of the dependent variable versus the independent variable and is used to investigate whether or not there is a relationship or connection between 2 sets of data. strong correlation Two variables with a strong correlation will appear as a number of points occurring in a clear and recognizable linear

Comparing this scatter plot to those in Figure 8.79 to Figure 8.85, we can see that the relationship is stronger than the one in Figure 8.83 r 0.61 r 0.61 but not as strong as the one in Figure 8.84 r 0.97 r 0.97. So, the value of the correlation coefficient is somewhere between the two.

For the BMI and the body fat data, the scatterplot displays a moderately strong, positive relationship. As BMI increases, the body fat percentage also tends to increase. I used scatterplots to visualize potential correlations but with about 28 plots the points are mainly on the x and y axis. I haven't found a good explanation as to why

A note on terminology If a scatterplot is said to show a quothighquot or quotstrongquot positive correlation, this does not mean that a straight line drawn amongst the dots being a guess as to where the dots quotoughtquot to be, were life not so messy would have a high-number positive slope instead, it means that the dots are closely clustered on or near the line drawn through the dots, so that the match of

4. Negative Correlation. When the points of the scatter diagram cluster around a straight line downwardnegative slope, then the correlation is said to be negative. 5. No Correlation. When the points of the scatter diagram are scattered in a haphazard manner, then there is zero or no correlation. How to interpret a Scatter Diagram?

As a rule of thumb, a correlation greater than 0.75 is considered to be a quotstrongquot correlation between two variables. However, this rule of thumb can vary from field to field. For example, a much lower correlation could be considered strong in a medical field compared to a technology field.

The Scatter Plot The scatter diagram for the temperature versus strength data allows us to deduce the nature of the relationship between these two variables 120 130 140 150 160 170 60 50 40 30 20 Classify the strength of the correlation as strong, moderate, weak, or none Chapter 5 19