Question

In: Statistics and Probability

Is this a true statement why or why not? Rejecting the null hypothesis that the population...

Is this a true statement why or why not?

Rejecting the null hypothesis that the population slope is equal to zero or no relationship exists concludes that the population slope is, indeed, not equal to zero; and, therefore, there is a significant relationship between x and y. That being said, this information simply provides us with the measure of the strength of the relationship between the two variables. It informs us of how the variables are correlated, if they are linearly related, and if a significant relationship exists between the two (or more) variables. However, it does NOT enable us to conclude whether a cause-and-effect relationship is present. Determining a cause-and-effect relationship requires analysts to take several other factors into consideration. We can only conclude that there is a cause-and-effect relationship if the analyst can provide some extent of theoretical justification of the relationship actually being casual. From my understanding, concluding that there is a cause-and-effect relationship requires some sort of theoretical justification and entails that the analyst make a judgment call using the given information.

Solutions

Expert Solution

Answer :

NO

Because,

Cause and effect relationship can't be induced from the significant slopet of regression equation itself since regression is about connection between the factors though the cause and effect relationship is known by examining a few different parameters of setting, subjective measures, bewildering factors, and so forth,. Cause and effect relationship is past quantitative information. Regression condition is acquired utilizing quantitative information and is restricted to such information.

In this way, on the off chance that the null hypothesis is rejected, at that point the connection between the factors is huge yet there is nothing to state about cause and effect relationship.

Additionally, any disconnected variables can have a correlation relationship and it doesn't mean change in one variable is causing the adjustment in the other variable. The specific situation and searching for the confounding effect is significant here.

Cause and efect relationship is past the information. The quantitative information is one of the parameters to discover cause and effect connection between the factors, yet not the whole thing.


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