A line graph is the most useful type of graph for showing the relationship between two numerical variables. A bar graph can also be used since these two types of graphs are straightforward and simple.
It mean that there is no correlation between the two variables. The variables are the same.
"If coefficient of correlation, "r" between two variables is zero, does it mean that there is no relationship between the variables? Justify your answer".
There is no "this statement" associated with the question, but the maximum number of points which lie of the graph of a linear equation in two variables is infinite.
Scatter plots are used to investigate a possible correlation between two variables that are associated with the same “event.”
Independently associated means that two variables are related to each other even after accounting for the influence of other variables. In statistical terms, it indicates that the relationship between the two variables is significant and not influenced by any confounding factors. It suggests that the association between the variables is genuine and not spurious.
Line Graph
A line graph is the most useful type of graph for showing the relationship between two numerical variables. A bar graph can also be used since these two types of graphs are straightforward and simple.
A scatter graph.
Line Graph
It mean that there is no correlation between the two variables. The variables are the same.
"If coefficient of correlation, "r" between two variables is zero, does it mean that there is no relationship between the variables? Justify your answer".
ax + by + c = 0 where x and y are the variables, a, b and c are numerical constants.
Correlation is a statistical measure of the linear association between two variables. It is important to remember that correlation does not mean causation and also that the absence of correlation does not mean the two variables are unrelated.
It is a linear expression in two variables.
A positive correlation between two variables means that there is a direct correlation between the variables. As one variable increases, the other variable will also increase.
Qualitative (things you can describe, like categories: gender or sport, variables like sm, m, and lg, or attitudes: agree/disagree, etc) and quantitative: things you can measure and report in numbers (like mass or volume)