Linear regression is a powerful tool widely used in both research and business,

Question

Linear regression is a powerful tool widely used in both research and business, which implies that there is correlation between two variables. Thus, there is a tendency for many people to assume that if information is presented in this manner, then it must be valid. This can lead to erroneousand/or unethical presentations. The assignment is to start a thread where you propose a relationship (real or imagined) between two variables and state an inference that can be made. EXAMPLE: "I am an avid sports fan, especially when it comes to baseball. My team, the Houston Astros, are currently the best team in baseball. The prompt given for this activity brought up something in my mind, is the line up of players for each game determined by how many home runs the player has hit or their batting average. The reason I say each game and not the entire season is that there is never a solid line up for the season. For this example I am using the line up from the game that occurred on June 11 th. The lead off batter, George Springer, does not have the best batting average on the team but has hit the most runs this season. Most of those home runs have been lead off runs, which means that he was the first batter of the game. He has consistently been first because of how much confidence the head coach has in him. The last batter in the line up is Norichika Aoki. He has the second worst batting average which puts him towards the bottom of the line up automatically, but those players are not always the last batters. The reason he is the last batter is that through 64 games he has only hit I home run. From this game is it very easy to tell that the line up has been based off of homeruns instead of batting averages. The line ups do change from time to time depending on if a player is injured, sick, or has a day off, but the idea of home runs over baiting averages still stays the same."

Explanation / Answer

I propose the relationship of "number of hours spent studying" and "scores obtained in SAT"

We have observed those who spend more hours studying score better. There can be many contributors to better scores besides the time spent studying, like IQ level of the student , prior kknowledge of the topic , grasping power and mode to teaching , level of clarity , conceptual clarity and so on.

if the time spent is studying is high , but the IQ level is low , the scores obtained in SAT would surely be less. If IQ level is high , but less time is spent , there are chances to score good. If there is no conceptual clarity of the subject , no matter how much time you spend it may not result is a better score , Thus we can observe that there are various other factors whihc definitely yes influence the scores obtained in SAT

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Likewise in so many real life situations you may find that it is not just one factor that may influence the dependent factor , there may be various predictor or independent factors whose variations may lead to variation in the response variable or dependent variable.

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Thus when we need to conduct linear regression , we need to consider the rest of the influencing factors to be constant , and only then can be analyse the relation of the independent and dependent variable in pure sense. Hence in this example we need to purely focus on the number of hours spent studying and the scores obtained in SAT thereafter. This will tell us the relation between the two variables.

And as observed practically and in various academic researchs we get to know the more the number of hours spent in studying , the better the scores are obtained in any test be it SAT.

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