I need two secondary posts in response to the two initial disucssion posts. Resp
ID: 3238608 • Letter: I
Question
I need two secondary posts in response to the two initial disucssion posts.
Respond to the TWO posts about correlation and regression.....150-200 MINIMUM
1. Correlation and regression are the most commonly used techniques for investigation; also correlation and regression are relationships between two quantitative variables (Correlation and Regression, 2017). Some people may believe that it is very important to not use the terms “cause and effect” when describing these two relationships because correlation does not mean cause and example that people might use is that your weight does not cause your height, so on and so on. Cause and effect are two terms that can define correlation and regression because you can be able to test a hypothesis by using cause and effect. For example, “giving people different amounts of drugs and measuring their blood pressure” (Correlation and Regression, 2017). The cause of this is the different types of drugs and the effect is what happened to the people blood pressure by using these drugs. The reason for using correlation and regression is to find the association between two variables and to study the relationship between one dependent variable and independent variable. Yes, I believe we can use correlations and regression or “cause and effect” in the business world because companies has to test things out to see what approach work better for the company. Overall, some people will not use the terms cause and effect when describing these relationships but it depends on what you are testing or trying to figure out. I still believe it is still too early to figure out what is not working. The only problem is with the SPSS software on all the laptops I tried in on it did not work. I am sending my laptop to store to see if the computer tech can help me. I just been busy with work and I finally have some time off to actually get the software on my laptop. I just wish that this software was already built in with the course like the other classes that I had this year. Some of my accounting classes they already had a system built in so we can to do assignments we just had to log in. Hopefully for the future Tiffin can make this possible for other students. Other than the software the class is pretty cool I do not have any other problem with doing the assignments, I am able to do those and turn them in on time. I am just looking forward in finishing this semester strong.
Correlation and Regression. (2017). Explorable.com. Retrieved 25 May 2017, from https://explorable.com/correlation-and-regression
2. Correlations and regressions, when interpreted correctly, can produce indicators of future events. That said, they will never be a definite cause and effect. Correlations show a relationship between two variables, while a cause and effect relationship would mean when one variable is altered, it will have a definite effect on the other. Regressions explain whether or not there is a variable that could be used as a determining factor, but does not label on variable as causation for future outcomes.
In my simplified explanation, cause and effect are scientific in that applying enough heat to something flammable will cause it to burn, and statistical studies allow for one to make predictions about outcomes but will never be 100% true or repeatable.
I think there are too many variables at play in the business world to apply cause and effect reasoning. Any number of unpredictable incidents could combine to totally contradict the statistical prediction, or the prediction could be spot on, but the variables will never be the same a second time.
Haan, P., & David, R. A. (2008). Practical statistics for business: An introduction to business statistics. Lanham, MD: University Press of America.
AGAIN.. I NEED AN ANSWER TO BOTH POSTS (1 and 2), 150-200 Each MINIMUM
Explanation / Answer
1)
Correlation determines whether there is any statistical dependence between two variables. Correlation can be both - causal as well as non-causal. Yes the weight may not be an direct cause for the height of a person - this is absolutely true. But similarly there are certain cases where there is a causal relationship. For example, in a hot country during summer, demand of electricity increases as the weather is more hot. Because many people use Air conditioner, Air coolers etc. more then to get relief. Hence demand of electricity spikes up.
This means the more the heat, the more the demand of electricity and vice versa. We can use this information to model relationship between electricity demand and daily temperature. Regression technique allows us to model this relationship using some mathematical modelling.
In different companies, correlation and regression is used for future prediction of various things, for example, demand of their products etc. But we should keep in mind the randomness part also, while modelling. Because due to sudden causes, these predicitons can go wrong sometimes.
2)
Correlation can be thought of as a mutual relationship between two or more things. And yes correlation can produce indicators of future events - the reason for this is they can indicate a predictive relationship that we can exploit in practice. Correlation can be "casual" as well as "non-casual". Cause and effect may be present in relationship of two variables - but the main point is that a random part will always be there in real life. And it is this random part ( also called random error ), that sometimes make prediction wrong. In business world also we can apply correlation and regression with cause and effect reasoning.
But the main fact is that, using correlation and regression we will not be able to predict the sudden incidents, also the outcome of certain news or political situations - thus making the predictions a bit random.
But in certain cases, if we are able to correctly identify the variables then our prediction will be more correct - but the main challenge is to correctly identify the variables for proper prediction of future.