Easier to reply when full details are given...
Either +1 (strongest possible positive correlation between the variables) or -1 (strongest possible negativecorrelation between the variables).
There appears to be a very strong negative linear relationship between the two variables. One variable increases as the other decreases following a linear relationship over the domains of measurement. A correlation coefficient can say nothing about causality. It is possible that changes in the first variable causes changes in the second or the other way around. Or, it could be that neither of them cause the other, but both are caused by something else.
There is no specific name. It could be a linear or more complicated polynomial equations, it could be trigonometric, exponential or any one of many other types. It could be a combination of these
It is an equation in which one of the terms is the instantaneous rate of change in one variable, with respect to another (ordinary differential equation). Higher order differential equations could contain rates of change in the rates of change (for example, acceleration is the rate of change in the rate of change of displacement with respect to time). There are also partial differential equations in which the rates of change are given in terms of two, or more, variables.
An equation with only one variable has only one letter used in it, and that letter is usually an "x" An equation having two variables will have two different letters representing them, usually the letters "x" and "y" The first type could be the equation 5x^3 - 3x^2 + 6x - 50 = 0 The second type could be (x +y)^2 - 7x^3 + 12x = 58.8 1 equations with only 1 variable are usually much easier to solve than an equation with 2 variables, and you cannot solve the latter unless you have two separate equations containing the two variables.
Either +1 (strongest possible positive correlation between the variables) or -1 (strongest possible negativecorrelation between the variables).
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.
Correlation determines relationship between two variables. For example changes in one variable influence another variable, we can say that there is a correlation between the two variables. For example, we can say that there exists a correlation between the number of hours spent on reading and preparation and the scores obtained in the examination. One can infer that higher the amount of time spent on preparation may result in better performance in examination leading to higher scores. Hence the above is a case of positive correlation. If an increase in independent variable leads to an increase in dependent variable, it is a case of positive correlation. On the other hand if an increase in independent variable leads to a reduction in dependent variable, it is a case of negative correlation. An example for negative correlation could be the relationship between the age advancement and resistance to diseases. As age advances, resistance to disease reduces.
Yes
correlation does not imply causation, meaning that a negative correlation between two variables does not prove that one causes the other; it could be due to other factors influencing both variables. It is important to consider other variables and conduct more research to establish a causal relationship between self-esteem and anxiety levels in students.
When a question asks you to state the relationship between variables, it is requesting you to describe how the variables are related to each other. This could include whether they have a positive or negative correlation, whether one variable causes a change in the other, or if there is no relationship between the variables.
if you can, you could always search a online calculator and use that.
Correlate means to connection one thing to another in terms of how they relate to one another. For example one could write a paper to correlate how dropping out of school leads to working jobs that do not pay well.
No. There could be no solution - no values for x, y, and z so that the 3 equations are true.
Strengths:WeaknessesCalculating the strength of a relationship between variables.Cannot assume cause and effect, strong correlation between variables may be misleading.Useful as a pointer for further, more detailedresearch.Lack of correlation may not mean there is no relationship, it could be non-linear.
There appears to be a very strong negative linear relationship between the two variables. One variable increases as the other decreases following a linear relationship over the domains of measurement. A correlation coefficient can say nothing about causality. It is possible that changes in the first variable causes changes in the second or the other way around. Or, it could be that neither of them cause the other, but both are caused by something else.
False. Correlation coefficient as denoted by r, ranges from -1 to 1. Coefficient of determination, or r squared ranges from 0 to 1. I note that x,y data points that have a high negative correlation would plot with a negative trend or a negatively sloped line if a best fit regression line is determined. I note also that x,y data points with a high positive correlation would plot with a positive trend or positively sloped line if a best fit regression line is determined. The coefficient of determination for r = 0.9 and r= -0.9 would be 0.81.