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Two variables are found to have a strong negative lincar correlation. Pick the correct statement; An increase in one variable does not cause an increase in the othe...

Question

Two variables are found to have a strong negative lincar correlation. Pick the correct statement; An increase in one variable does not cause an increase in the other: An increase in one variable potentially causes decrease in the other: An increase in one variable potentially causes increase In the other An increase in one variable causes an increase in the other; An increase in one variable does not cause decrease In the other; An increase in one variable causes decrease In the other

Two variables are found to have a strong negative lincar correlation. Pick the correct statement; An increase in one variable does not cause an increase in the other: An increase in one variable potentially causes decrease in the other: An increase in one variable potentially causes increase In the other An increase in one variable causes an increase in the other; An increase in one variable does not cause decrease In the other; An increase in one variable causes decrease In the other



Answers

Two variables have a negative linear correlation. Does the dependent variable increase or decrease as the independent variable increases?

In this problem were given that two variables have a positive linear correlation. I'm not supposed to tell whether the dependent variable will increase or decrease. As the independent variable increases no. For positive correlation, we find that the points would appear to follow up a certain pattern. Maybe this diagram illustrates the pattern that we expect to have the positive correlation. And if the value off X, which is the independent variable increases, then the value off why which is the dependent variable also increases.

Mhm. Two variables X and Y are negatively correlated. We want to determine whether when the explanatory variable increases, the response variable will increase or decrease. Generally based on our expectation from the correlation. It's important to remember that a negative correlation corresponds to a correlation coefficient are less than zero. Remember that we have are less than zero. We can think of this in terms of slope, where as X increases, we expect why to decrease. So for negative correlation. When the explanatory variable increases, we predict the response variable, why will also decrease. So let's take a look at the plot on the left, which provides an example of a scatter plot where negative correlation exists between the data. We see generally that as X increases, moving from 930 all the way right, 2000 R Y axis is decreasing or data points are decreasing in the response variable from 150 all the way down to 40.

Mhm. Two variables X and Y are negatively correlated. We want to determine whether when the explanatory variable increases, the response variable will increase or decrease. Generally based on our expectation from the correlation. It's important to remember that a negative correlation corresponds to a correlation coefficient are less than zero. Remember that we have are less than zero. We can think of this in terms of slope, where as X increases, we expect why to decrease. So for negative correlation. When the explanatory variable increases, we predict the response variable, why will also decrease. So let's take a look at the plot on the left, which provides an example of a scatter plot where negative correlation exists between the data. We see generally that as X increases, moving from 930 all the way right, 2000 R Y axis is decreasing or data points are decreasing in the response variable from 150 all the way down to 40.

The correlation between two variables is positive. That is the correlation between an explanatory variable X. And response variable. Why is positive? We want to answer the following question as the explanatory variable increases, so as X increases, what happened to the response variable? Why remember that positive correlation means are greater than zero. And you can remember that when R. Is greater than zero. That as X increases, you're going to observe that. Why increases so in this particular problem, as the explanatory variable X increases, the response variable is expected to increase as well. We don't know for sure that why will increase but based on our prediction given by the positive correlation, we will expect for it to do so. An example is given on the right to the scatter plot. See how generally as X increases. Why? Also increases. Note that the point at the bottom around 8.6 and under why equals 10 is an example where why doesn't have to increase as X increases. But we see a general trend in the data.


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