William Murray MK390

As we just wrapped up our last class, I really came to appreciate what we learned this semester. The simulations that were done taught me about the trial and error of data analytics. Your'e not going to get everything right the first try. Learning how to read data will help us be successful in the future and I can already see it happening in real life. There have been times at work this year where one of my managers will ask me to help him on excel or help him with promotions. He will show me how many people we are hosting at the bar and lounge on certain nights. I give him suggestions that I think will help based off of things that I have seen on previous nights. This class has opened my mind to this and I know it will help me in the future. This class taught me that numbers are your friends. Numbers will always be your friends because they are facts, no ifs and or buts about. That also reminds me of earlier in the semester when we were talking about good vs bad data and how y...

This week we have done a lot with group work. From finishing up Group Assignment number one to starting a new assignment with the groups assigned on Tuesday. I enjoy doing group work because it involves discussing with other classmates to find the best answers or ideas. In the future, we will most likely all be working with coworkers which is just "group work" in real life. It is alway more fun to do this than working by yourself. This week we discussed a lot about the Original Least Squares Regression model, or OLS. I didn't know too much about that before this week until we read about and discussed it. OLS is a very useful tactic when looking for new parameters and predicting the future with them. Another thing that we kept discussing this week was logistic regression. We have gone over this in the past but now I just keep getting a better understanding of it the more that we talk about. This class is getting more clear each week even with the new learning that keeps ...

William Murray 10/29/2018 Letter to Boss Boss, With the given data, I was able to predict the monthly sales forecast for the upcoming months. In region one we are given the estimated unit sales mean, low and high numbers. We are able to find the estimate for every given region for each month of January, February and March. To find the estimated low and high unit sales, all we had to do was either subtract or add the standard deviation depending on which one you were looking to find. This gives us a much better range of what our sales are going to be. This means that the range is essentially just the standard deviation times two. Our sales predictions will certainly help us get a better understanding of what our sales should look like come January 2019. To do this, we find unit sales of January 2019 by multiplying January 2018 sales by one plus the growth factor. If the growth factor is positive, we will make more sales. However, if it is a negative growth factor, then we...

This week in class we dug a little deeper into our analytics. We learned how to be more descriptive with our marketing insights and learned how to read them more ineptly. This helps for coming up with facts for when we are being descriptive, especially for our project. It is very important to understand the data that you are collecting from your analysis. Reading analytical data is a very difficult thing to do and this class is preparing us to be able to do so. It is essential to know  how to read data if we are looking to work in a competitive environment. 

For class this week, we needed to read chapters 4 and 5 of Mike Grisby's Marketing Analytics textbook. These chapters speak of what is the drive for demand. It also talked about a couple of different analytical statistics that helped answer the question. The chapters also talked a lot about who your main market is. It speaks on who is buying from you and how you as a company need to know who to market to. Between creating a matrix and picking variables, there can be a lot of information that is confusing. Questions What makes LCA the gold standard? Modeling elasticity, I don't really understand much around it. What does it mean when you are closer to 0% vs 100%?

     Week four was a little different than the previous weeks in terms of structure. This week there was no reading due which gave us more time to work with Simmons and learn all about how to work with the analytical data that it provides. I enjoyed learning about this and how the website can be so helpful.      The first subject that we researched in class on Simmons was Oreo consumption. There is a whole lot of data all separated by demographics such as age, sex, or what ever it is that you plug in and are looking for. Simmons provides exact data with percents and totals.      Simmons is really good for company's because you can really research anything. In class we saw researches on Oreos, Netflix vs Hulu, and even cigarette usage. Learning how to use a database such as Simmons is huge for any kind of research. It could be for starting up a new business, doing research for a business that is already owned or anything where data is neces...