Business Decision Making Project Part 2 Michael Locke QNT/275 APRIL 12, 207 Vahid Keyhani Identify the types of descriptive statistics that might be best for summarizing the data, if you were to collect a sample. Descriptive statistics are used to identify the basic features of the data in a study. They provide simple summaries about the sample and the measures. Descriptive statistics basically tries to sum up a considerable set of observations and give an idea about the data set. Together with simple graphics analysis, descriptive statistics form the basis of just about every quantitative analysis of data. My focus is on the decline of iPhone sales at Apple, Inc. One of the methods Apple, Inc. can do to collect data is conduct a count of iPhones sold and/or upgraded during each quarter. Some factors that should be considered when conducting the study are the demographics of the consumers making the purchases, the area the iPhones are being sold, the average age group of the buyer and if the consumer is purchasing a new phone or upgrading their old one. Once all of the data is collected, it will be easier to see where most of the purchases are coming from and go from there to come up with a game plan to try and increase sales. Conducting focus groups usually focuses on a certain number of people with a specified age range. A focus group study would give a more concise summarization based on the sample of let's say 35 people. Focus groups would give a more thorough understanding of why people of specific groups decline to purchase the newer model iPhones or opt out of upgrading to the newer systems. Analyze the types of inferential statistics that might be best for analyzing the data, if you were to collect a sample. Inferential statistics determine the probability of characteristics of population based on the characteristics of the sample provided. Inferential statistics evaluates the strength of the relationship between causal and dependent variables. We use inferential statistics to judge whether or not the observation of different groups qualifies as a dependable sample or one that may have just happened during this study, basically inferential statistics are used to draw conclusions about a large group of people. By conducting a count of iPhones sold and/or upgraded during each quarter, Apple, Inc. will be able to compare the purchases of each variable in the study. For example, once all of the data is collected, Apple, Inc. can clearly see where and who are making the most purchases. It could be looked at as the number of new activations vs. the number of iPhone upgrades in the age group 21-30 in Philadelphia, PA. Analyze the role probability or trend analysis might play in helping address the business problem. Trend analysis is the collection of information and trying to point out a pattern or trend. Trend analysis is often used to predict future events or give an estimate of an outcome based on past events. By using trend analysis, Apple, Inc. will be able to monitor when their sales are at their highest and when they begin to decline. Having that knowledge and keeping track of that trend will ultimately help Apple, Inc. to come up with better ways to keep their sales up during the time when sales start to fall. Analyze the role that linear regression for trend analysis might play in helping address the business problem. Linear regression is the approach for showing the relationship between the dependent variable Y and one or more independent variables known as X. Linear regression shows the relationships modeled by using linear predictor functions whose model parameters are unknown and are estimated through the data provided. After the collection of data, Apple Inc. would need to apply it to regression analysis. Regression analysis analyzes the relationship between the variables and will give Apple, Inc. a better timeframe of when sales reach their peaks and when they start to fall. Analyze the role that a time series might play in helping address the business problem. A time series is a sequence of the same variable taken over time. In this particular case, Apple, Inc. has a decline in sales during certain times at certain quarters. Past effects of the time series affect the present percentage of sales. What usually happens in the past, repeats itself. A seasonal time series study would work great to collect data for sales for iPhones. Smart phones are a hot commodity, but because of the many other brands of smart phones and the different processors, it definitely affects the sales of Apple's iPhone. For example, sales during Christmas time are more likely to improve because more people are purchasing devices as Christmas presents, more deals are given around that time of year and a lot of companies release the newest versions of their smart phones. In 2015, Apple dominated phone sales during the Christmas season according to the NY Post. \"Christmas is traditionally the biggest day of the year for new smart-device activations and app downloads, and 2015 was no exception, with new device activations and app installs shattering record after record,\" Jarah Euston, research outfit Flurry's vice president wrote in a blog post. A time series will be able to show the difference in sales during Christmas as oppose to any other time of the year. Forecasting is also a time series model that can be used to predict future values or sales based on the number of sales that were driven in a specified quarter. For example, Apple, Inc. sells 150,000 iPhone 6S series phones in quarter 3 of 2015 which is 5 percent more than it sold in quarter 2 of 2015. By comparing the data between the two quarters, Apple, Inc. may predict that the sales of the iPhone 6S will go up another 2-5 percent at the end of the 4 th quarter in 2015. References Apple dominated phone sales again this Christmas. (2015, December). New York Post, (), . Retrieved from http://nypost.com/2015/12/29/apple-dominated-phone-sales-againthis-christmas/ Explorable. (2016). Descriptive Statistics. Retrieved from https://explorable.com/descriptive-statistics Jaggia, S., & Kelly, A. (2014). Essentials of business statistics: Communicating with numbers. New York: McGraw-Hill Irwin. Research Methods Knowledge Base. (2006). Retrieved from http://www.socialresearchmethods.net/kb/statinf.php Stati sti cs Soluti ons. (2013). What is Linear Regression [WWW Document]. Retrieved from htt p://www.stati sti cssoluti ons.com/what-is-linear-regression/