Discussion Response I need two discussion Resposes, 1 of each. Respond to the original posting for a minimum of two of your classmates in a written p

Discussion Response
I need two discussion Resposes, 1 of each.
Respond to the original posting for a minimum of two of your classmates in a written posting of 150 to 250 words addressing the followin

State your opinion regarding whether or not the use of simple or multiple linear regression analysis is appropriate for the situation discussed in the posting, including discussing why simple or multiple linear regression analysis is or is not appropriate.
State your opinion regarding the strengths and weaknesses associated with using simple or multiple linear regression analysis in the situation discussed in the posting.

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Discussion Response I need two discussion Resposes, 1 of each. Respond to the original posting for a minimum of two of your classmates in a written p
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Discussion 1
Greetings Professor and classmates,
Regression analysis is a mathematical process of identifying variables that are related and impact each other (Gallo, 2015). It identifies factors that matter most, those that can be ignored and the confidence level in those factors (Gallo, 2015). In searching for a decision making example that is complex and involves important outcomes, I thought what better example than in business. For example, in marketing, simple regression analysis could be conducted between the sales generated and the number of ads placed on Facebook. Marketing is extremely expensive and in the preparation of the marketing budget, only platforms that generate the most traffic should be invested in. The difference between advertising on the right and wrong platforms can amount to tens of millions of dollars in sales. Similarly, multiple regression could be made with several other platforms, for example, Instagram, Twitter, YouTube, etc. It can be determined which platform has the strongest impact on sales, as highlighted by the coefficient in the linear equation.
In the regression analysis, the dependent variable would be the online sales generated (in dollars), while the independent variable would be the number of leads from a particular platform website. An online ad could cause an instore sale, but to control the analysis, only online leads would be used in the regression. This would improve the decision making process as analytics are used to make informed decisions instead of using one’s gut feeling (Anurag, 2020). For instance, if the regression showed that Instagram was the variable with the strongest correlation, then more of the marketing budget should be allocated to Instagram ads. One challenge that I can identify is the time period of the leads used in the analysis. One particular platform might not be trending at its peak at the time the analysis is performed so the leads generated might not be a true indication of the relationship. To avoid this, the data should be collected over a sufficiently long period, preferably across business cycles. Additionally, to get a true sense of the magnitude of correlation between the variables, simple regression should be conducted for each platform with sales to calculate R2,and then multiple regression should be performed (Frost, 2020). Thank you all for listening.
Kezia
Reference
Anurag (Apr 18, 2020).5 applications of regression analysis in business. Retrieved from:
https://www.newgenapps.com/blog/business-applications-uses-regression-analysis-advantages/(Links to an external site.)

Frost, J. (n.d).Five regression analysis tips to avoid common problems. Retrieved from:
https://statisticsbyjim.com/regression/regression-analysis-tips/(Links to an external site.)

Gallo, A. (Nov. 4, 2015).A refresher on regression analysis. Harvard Business Review

Discussion 2( Mckenna)
An example of a decision-making situation where it might be useful to use simple or multiple linear regression analysis to improve the quality of the decision-making process that comes to my mind is an ice cream truck business. Using a multiple linear regression analysis would be very useful for the owner of the company in predicting revenue of each truck within the organization, while taking into account temperature outside, overall weather conditions (raining or not), number of workers or even pricing. The specific decision being made would be to determine which item impacts revenue of the trucks the most. This is important because each truck may need more workers, or the price might need to be adjusted. Additionally, it might be decided that hours of business for the trucks should vary depending on the weather for the day.
In this example, there is one dependent variable and multiple independent ones. Therefore, multiple regression analysis would be utilized. The dependent variable is revenue because this is what is impacted by the other variables. Revenue is what shows the strengths of the ice cream truck company. The independent variables would be those having to do with the weather like temperature outside and whether it is raining or not. Another independent variable is number of workers (number of trucks operating at a time). The last independent variable is price of the various ice cream treats.
I envision performing the quantitative analysis technique of multiple regression analysis. This would allow the owner of the business to determine the correlation among the dependent and independent variables. For example, the owner might be able to clearly see that high outside temperature and no rain results in higher revenue due to an increased number of ice cream sales and could then plan to increase the number of hours trucks work on those days that have nice weather. Additionally, the price for unpopular ice cream treats could be lowered if selling them does not result in high revenue.
A potential challenge to using multiple linear regression in this instance is that it might be a little too complex. Therefore, using similar linear regression could be more beneficial for a smaller company. Also, one of the independent variables might be hard to track, such as weather, because it can be somewhat unpredictable.