Question

Think of a problem dealing with two possibly related variables (Y and X) that you may be interested in. Share your problem and discuss why a regression analysis could be appropriate for this problem.

Specifically, what statistical questions are you asking? Why would you want to predict the value of Y? What if you wanted to predict a value of Y that’s beyond the highest value of X (for example if X is time and you want to forecast Y in the future)?

You should describe the data collection process that you are proposing but you do not need to collect any data.

Answer 1

Let us consider an example . Suppose your buisness is selling umbrellas, winter jackets, or spray on waterproof coating. You might find that sales rise a bit when there are 2 inches of rain in a month. But you might also see that sales rise 25%during the months of heavy rainfall, where there are more than for inches of rain. You could then be sure to stock up you umbrellas, winter jackets or spray on waterproof. hence you might increase your buisness during these months and possibly bring in more profit.

Here rain and sale are interrelated where rain affects sales. Let rain be X and sales be denoted as Y , then we can easily see that Y is a dependent variable on independent variable X

This example shows the benefits of regression where you are using a single line that you draw through the plot points. This line will show the comparison between these two variables and will help you to have a clear and visual look at when a company's sales crest and fall.

This example seems more obvious as more rain equals more sales of umbrellas and other rain related products.But it shows how businesses use regression analysis to make data driven predictions about the future. Putting it another way we can say that regression analysis can help tour business avoid potentially costly gut level decisions and instead base your decisions about fiture on hard data, giving you a clearer , more accurate path into future.

DATA COLLECTION PROCESS SHOULD BE OBSERVATIONAL STUDIES.

Like experiment, observational studies attempt to understand cause and effect relationships.