AVOIDING THE PITFALLSEmail campaigns, are still vibrant and active parts of company marketing and you as the web analyst may be asked to make sense of this data. One of the biggest pitfalls in web analytics is how to understand and deal with the data from these email campaigns. Explain the three (3) pitfalls and how you would avoid them with specific examples for each.Helpful Readings: Be sure to review the Week 5 Lessons for additional reading materials and information. (You may use these or select your own from your own research)Helpful Reading Materials:http://www.kaushik.net/avinash/email-marketing-campaign-analysis-metrics-practices/http://www.kaushik.net/avinash/multichannel-analytics-tracking-online-impact-offline-campaigns/Additional and helpful resource: Locate book “Web Analytics an Hour a Day” by Avinash Kaushik in the APUS Library, pages 224 – 225 will be most helpful.2007 - Web_Analytics_An_Hour_a_Day_Chapter9_.pdf(Pages 224 – 225 will be most helpful regarding the 3 Pitfalls in Email)Please use APA formatting and in text cititation Please No Plagiarism

Details:The purpose of this assignment is to apply data distributions to discrete and continuous data and justify the selection of the distributions.For this assignment, you will use the "Random Variables" dataset. You will use SPSS to analyze the dataset and address the questions presented. Findings should be presented in a Word document along with the SPSS outputs. Part 1: Identify if the following random variables are discrete or continuous.Number of defected items in a shipment.Height of males (in mm) who attend Grand Canyon University.Yearly income among all people in the United States.Whether or not a high school graduate is accepted into a college.Time that it takes for a person to run a mile.The number of emergency hospital visits that each person had in the last 12 months. Part 2: Let X be a random variable of the outcome after rolling a six-sided die one time that is not fair. In fact, the die is designed to never result in a 1 or 6, while the other outcomes (i.e., 2, 3, 4, and 5) are equally probable.What are the individual probabilities for all possible values of X?What are the cumulative probabilities for all possible values of X?What is P[X = 3]= ?What is P[X ≤3] = ? What is P[3 ≤X ≤5] = ? Part 3:The dataset provided consists of the following random variables: BMI: The body mass index of a random set of people. Distance: The distance (in feet) that a baseball player hit the ball. Height: The height of males (in mm). Income: The income (in dollars) of people in a large company. Pass: The outcome when taking an exam (1=Pass; 0=Fail). Wait Time: The time (in minutes) that it takes when waiting for the train.Answer each question below. Use SPSS as needed, and include the software outputs as part of the Word document you submit.What is a Q-Q plot?Given a set of realized values of a random variable, how can a Q-Q plot be used to assess the distribution of the random variable?Using histograms and Q-Q plots (except for binomial), match each random variable to one of the following distributions: Binomial (with N=1, P=0.7), Chi-square (with d.f.=20), Exponential, Lognormal, Normal, and Uniform.APA format is not required, but solid academic writing is expected.This assignment uses a grading rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion. PLEASE REMEMBER THE ASSIGNMENT MUST HAVE THE FOLLOWING... Identification of random variables as discrete or continuous is complete and correct.,Solutions for probability questions are complete and correct.,Answers to multiple regression analysis questions and supporting SPSS output charts are complete and correct. AND ANSWER THE FOLLOWING TWO QUESTIONS...QUESTION 1 Summarize key data distribution concepts including probability mass functions (PMF), probability density functions (PDF), and cumulative distribution functions (CDF). Based on your organization or any organization you are most familiar with, provide an example of a PMF, an example of a PDF, and an example of a CDF, based on the type of data used in the organization. How would you summarize each of these to someone who is not familiar with each of these functions? QUESTION 2- Suppose you had a six-sided die where each number (1, 2, 3, 4, 5, and 6) has the same probability of showing up (1/6). If the die is rolled an infinite number of times and the number recorded, what will be the average value that shows up? Is the average value one of the actual possibilities (1, 2, 3, 4, 5, or 6)? Why or why not?