POSITIVE CORRELATION IS CORRELATION THAT IS LINKED. REPHRAISED IT MEANS:POSITIVE CORRELATION IS CORRELATION IN WHICH BOTH AXIS ARE LINKED. SO IN SOME EXTREME CASES IT WOULD BE, (X=Y).BUT ON WITH THE QUESTION ANSWERING.HERE ARE A FEW EXAMPLES OF POSITIVE CORRELATION:1. THE AMOUNT OF COFFEE DRUNK AND THE NUMBER OF HOURS STAYED AWAKE.2. THE NUMBER OF PEOPLE FLYING TO AUSTRALIA AND THE NUMBER OF PLANES FLYING TO AUSTRALIA.THESE CAN EASILY BE CHANGED INTO SCATTER DIAGRAMS. IF YOU WANT TO KNOW MORE ABOUT POSITIVE CORRELATION THAN COME TO HAWLEY PLACE SCHOOL nd ask to see mr freeman.OTHER EXAMPLES OF POSITIVE CORRELATION IS THAT1.MARKS OF STUDENT AND HIS QUOTIENT. IN THIS CASE THERE IS POSITIVE CORRELATION BETWEEN THESE TWO VARIABLE.ON OTHER HAND IN SOME OTHER SITUATION "INCREASE IN VALUE OF ONE VARIABLE IS ASSOCIATED WITH INCREASE IN VALUE OF ANOTHER VARIABLE OR DECREASE IN VALUE OF ONE VARIABLE IS ASSOCIATED WITH DECREASE IN VALUE OF ANOTHER VARIABLE IS CALLED POSITIVE CORRELATION".
One common example of a correlation method is Pearson's correlation coefficient, which measures the linear relationship between two continuous variables. For instance, researchers might use this method to analyze the correlation between hours studied and exam scores among students. A positive value close to +1 indicates a strong positive correlation, while a value close to -1 indicates a strong negative correlation. This method helps in understanding how changes in one variable may relate to changes in another.
An example of correlation in statistics is the relationship between hours studied and exam scores. Typically, as the number of hours a student studies increases, their exam scores also tend to increase, indicating a positive correlation. This means that the two variables move in the same direction, though it does not imply causation. Correlation is often measured using Pearson's correlation coefficient, which quantifies the strength and direction of the relationship.
An example of positive correlation is the relationship between hours studied and test scores. Generally, as the number of hours a student studies increases, their test scores tend to improve as well. This indicates that both variables move in the same direction—higher study time correlates with higher scores.
An example of a positive correlation is: the number of cars on the road and the number of greenhouse gas emissions there are. As one of those rises, so does the other. A negative correlation is when one statistic rises causing the other to drop. Look at a few scatter plots and you will easily be able to see positive and negative correlations
A Pearson correlation measures the strength and direction of a linear relationship between two continuous variables, ranging from -1 (perfect negative correlation) to 1 (perfect positive correlation). An example could be studying the correlation between the amount of rainfall and crop yield in agricultural research to understand how variations in rainfall affect crop productivity.
Demand and quantity sold is an example of positive correlation. As the number of people in demand of a product increases, the quantity sold of that product also increases.
Positive Correlation- Age - Amount of medical conditions Negative Correlation- Television Watching- Grades No Correlation- Height of a person- Number of shoes they own Hope this was helpful!
Of course imagined is not and attitude.For example(I have a positive attitude)its basically like that i believe.
The number of pounds increases as the number of calories consumed increases.
To turn a negative attitude into a positive one it is important to lead by example. A child will watch you and if they see you being positive they will also be positive.
A person with a positive attitude has an optimistic outlook on life. An example would be someone who has had numerous obstacles in life but still continue to have hope for the future and keep a smile on their face.
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.
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.
POSITIVE CORRELATION IS CORRELATION THAT IS LINKED. REPHRAISED IT MEANS:POSITIVE CORRELATION IS CORRELATION IN WHICH BOTH AXIS ARE LINKED. SO IN SOME EXTREME CASES IT WOULD BE, (X=Y).BUT ON WITH THE QUESTION ANSWERING.HERE ARE A FEW EXAMPLES OF POSITIVE CORRELATION:1. THE AMOUNT OF COFFEE DRUNK AND THE NUMBER OF HOURS STAYED AWAKE.2. THE NUMBER OF PEOPLE FLYING TO AUSTRALIA AND THE NUMBER OF PLANES FLYING TO AUSTRALIA.THESE CAN EASILY BE CHANGED INTO SCATTER DIAGRAMS. IF YOU WANT TO KNOW MORE ABOUT POSITIVE CORRELATION THAN COME TO HAWLEY PLACE SCHOOL nd ask to see mr freeman.OTHER EXAMPLES OF POSITIVE CORRELATION IS THAT1.MARKS OF STUDENT AND HIS QUOTIENT. IN THIS CASE THERE IS POSITIVE CORRELATION BETWEEN THESE TWO VARIABLE.ON OTHER HAND IN SOME OTHER SITUATION "INCREASE IN VALUE OF ONE VARIABLE IS ASSOCIATED WITH INCREASE IN VALUE OF ANOTHER VARIABLE OR DECREASE IN VALUE OF ONE VARIABLE IS ASSOCIATED WITH DECREASE IN VALUE OF ANOTHER VARIABLE IS CALLED POSITIVE CORRELATION".
One common example of a correlation method is Pearson's correlation coefficient, which measures the linear relationship between two continuous variables. For instance, researchers might use this method to analyze the correlation between hours studied and exam scores among students. A positive value close to +1 indicates a strong positive correlation, while a value close to -1 indicates a strong negative correlation. This method helps in understanding how changes in one variable may relate to changes in another.
97.4 percent of men who consume ice in more than four drinks leave the bar drunk. That 97.4% is a significant positive correlation between the consumption of ice and its temporary deliterious effects on the human male physiology.