You can find examples by typing it in to Google. Weak positive correlation is a set of points on a graph that are loosely set around the line of best fit. The line will be positive rising up from left to right. A weak correlation can vary a lot as long as you can decipher which direction the data tends towards you have a correlation. If the points are close to the line of best fit you have a strong correlation and with a set of points perfectly lined up is perfect correlation. All three types can positive negative or perfect.
1 would be the strongest possible. 0.353 seems to be on the weak side. Above 0.4 or 0.45 may be strong enough.
# State the null hypothesis i.e. "There is no relationship between the two sets of data." # Rank both sets of data from the highest to the lowest. Make sure to check for tied ranks. # Subtract the two sets of ranks to get the difference d. # Square the values of d. # Add the squared values of d to get Sigma d2. # Use the formula Rs = 1-(6Sigma d2/n3-n) where n is the number of ranks you have. # If the Rs value... ... is -1, there is a perfect negative correlation. ...falls between -1 and -0.5, there is a strong negative correlation. ...falls between -0.5 and 0, there is a weak negative correlation. ... is 0, there is no correlation ...falls between 0 and 0.5, there is a weak positive correlation. ...falls between 0.5 and 1, there is a strong positive correlation ...is 1, there is a perfect positive correlation between the 2 sets of data. # If the Rs value is 0, state that null hypothesis is accepted. Otherwise, say it is rejected. (sourced from http://www.revision-notes.co.uk/revision/181.html)
Pearson's Product Moment Correlation Coefficient indicates how strong the relationship between variables is. A PMCC of zero or very close would mean a very weak correlation. A PMCC of around 1 means a strong correlation.
In organizational management, a strong matrix is an organizational structure arranged around projects; a weak matrix is arranged around functional roles. For example, in a strong matrix structure, the resources might be organized to support Product A or Product B, in a weak matrix structure, the resources might be organized into Development or Manufacturing.
india is very weak and has lost many wars against pakistan. Almost 40% or its children are not educated. It is very corrupt and has the smallest exporting system of any world power
An example of weak positive correlation would be the relationship between the amount of time spent studying for a test and the grade achieved. While there may be a slight increase in grades as study time increases, the correlation is not very strong. This means that studying more does not guarantee a significantly higher grade, but there is still a positive trend between the two variables.
a strong negative correlation* * * * *No it is not. It is a very weak positive correlation.
No, it indicates an extremely strong positive correlation.
yes
It is easy to find the correlation. First you see how far apart the dots are. if they are going UP like this / <---- it means its a positive correlation. if its like this \ <---- its a negative correlation. if its everywhere its a neutral (although they almost never do them in tests). To find out the strength is your opinion. If alot are grouped together almost making a line its a Strong correlation. Then you decide if its a Strong or Weak correlation depending on how close together the dots are. So put them together in a 1 mark question like::::it is a Strong Positive Correlation
yes
negative, weak
1 would be the strongest possible. 0.353 seems to be on the weak side. Above 0.4 or 0.45 may be strong enough.
If the form is nonlinear (like if the data is in the shape of a parabola) then there could be a strong association and weak correlation.
R less than 0.3
correlation which can be strong or weak
A correlation near 0 indicates a weak linear association.