Question

This course is actually Quantitative Methods and Analysis

In Unit 2, you have learned about three different types of distributions: Normal, binomial, and Poisson. You can take data that you collect and plot it out onto graphs to see a visual representation of the data. By simply looking at data on a graph, you can tell a lot about how related your observed data are and if they fit into a normal distribution.

For this submission, you will be given a series of scenarios and small collections of data. You should plot the data or calculate probabilities using excel. Then, you will create your own real or hypothetical scenario to graph and explain.
Answer the following:
• The mean temperature for the month of July in Boston, Massachusetts is 73 degrees Fahrenheit. Plot the following data, which represent the observed mean temperature in Boston over the last 20 years:

1998 72
1999 69
2000 78
2001 70
2002 67
2003 74
2004 73
2005 65
2006 77
2007 71
2008 75
2009 68
2010 72
2011 77
2012 65
2013 79
2014 77
2015 78
2016 72
2017 74

a. Is this a normal distribution? Explain your reasoning.

b. What is an outlier? Are there any outliers in this distribution? Explain your reasoning fully.
c. Using the above data, what is the probability that the mean will be over 76 in any given July?
d. Using the above data, what is the probability that the mean will be over 80 in any given July?

• A heatwave is defined as 3 or more days in a row with a high temperature over 90 degrees Fahrenheit. Given the following high temperatures recorded over a period of 20 days, what is the probability that there will be a heatwave in the next 10 days?

Day 1: 93
Day 2 : 88
Day 3: 91
Day 4: 86
Day 5 : 92
Day 6: 91
Day 7 : 90
Day 8: 88
Day 9: 85
Day 10: 91
Day 11: 84
Day 12: 86
Day 13: 85
Day 14: 90
Day 15: 92
Day 16: 89
Day 17: 88
Day 18: 90
Day 19: 88
Day 20: 90

Customer surveys reveal that 40% of customers purchase products online versus in the physical store location. Suppose that this business makes 12 sales in a given day

a. Does this situation fit the parameters for a binomial distribution? Explain why or why not?

b. Find the probability of the 12 sales on a given day exactly 4 are made online

c. Find the probability of the 12 sales fewer than 6 are made online

d. Find the probability of the 12 sales more than 8 are made online

Your own example:

o Choose a company that you have recently seen in the news because it is having some sort of problem or scandal, and complete the following:

 Discuss the situation, and describe how the company could use distributions and probability statistics to learn more about how the scandal could affect its business.

 If you were a business analyst for the company, what research would you want to do, and what kind of data would you want to collect to create a distribution?

 Would this be a standard, binomial, or Poisson distribution? Why?

 List and discuss at least 3 questions that you would want to create probabilities for (e.g.,What is the chance that the company loses 10% of its customers in the next year?).

 What would you hope to learn from calculating these probabilities?

 Assuming that upper management does not see the value in expending the time and money necessary to collect data to analyze, make an argument (at least 100 words) convincing them that the expenditure is necessary and explaining some dangers the company could face by not knowing what the data predict.

Answer 1

The issue at hand was whether there was illegal trade of sensitive customer data that could possibly influence geo-political events around the world. With this, lot of users had taken their profile off the portal. The company could use statistics to determine whether this event has had a significant erosion in the user base of the company. Or even, would such an event in the future cause similar user erosion from the website.

The company could use the concept of hypothesis testing. Null hypothesis is always the negative of the hypothesized statement. Hence in this case, null hypothesis (H naught) would be the said event has not caused any decrease in business of Facebook where as H1 would be just the reverse of H naught. By looking at rho levels, we can determine whether the hypothesis is true or not.

If upper management was not willing to invest in such an activity, then one would have to point out to them the risks associated with not knowing the aftermath of such an event. This could have serious business implications that could work against the interest of the company