ONLY REPLY IF YOU COMPLETE ORIGINAL WORK!! NOT COPIED FROM THE INTERNET! I WILL DISPUTE IF WORK IS COPIED!
Week 3 Assignment: Confidence Intervals (Due Feb 19)
In this week’s reading, we discussed different types of samples, their distributions, sampling errors, probabilities, and confidence intervals. Using the appropriate support tool, sequence, and equations, complete Problem 33 in Chapter 8 on page 351.
Problem 8-33. You have been assigned to determine whether more people prefer Coke or Pepsi. Assume that roughly half the population prefers Coke and half prefers Pepsi. How large a sample do you need to take to ensure that you can estimate, with 95% confidence, the proportion of people preferring Coke within 2% of the actual value?Show work.
Week 4 Discussion 2: Correlations/Linear & Multiple Regression (Due Feb 25)
Note that this discussion is due on Day 6. Although the initial post is due on Day 6, you are encouraged to start working on it early as it includes work in Excel. Prior to beginning work on this assignment, read Chapter 10.
Complete Problem 50 in Chapter 10 on page 477.
- Assume the only factor influencing monthly sales is price. Fit the following three curves to these data: linear (Y = a + bX), exponential (Y = abX), and multiplicative (Y = aXb). Which equation fits the data best?
- Interpret your best-fitting equation.
- Using the best-fitting equation, predict sales during a month in which the price is $470.
In the discussion area, attach the Excel document showing work.
Week 5 Final Paper: (Due March 5)
The Final Paper provides you with an opportunity to integrate and reflect on what you have learned during the class.
During the course, you have applied a variety of methods to analyze data sets and uncover important information used in decision making. Having a good understanding of these topics is important to be able to apply them in real-life applications. Below are some of the key elements that were discussed throughout this course. Analyze each of the elements below. In your analyzation, consider and discuss the application of each of these course elements in analyzing and making decisions about data. Incorporate real-life applications and scenarios.
The course elements include:
- Statistical Inference
- Regression Analysis
- Time Series
- Forecasting Methods
- Decision Tree Modeling
The paper must (a) apply and reference new learning to each of the ten course elements, (b) build upon class activities or incidents that facilitated learning and understanding, and (c) present specific current and/or future applications and relevance to the workplace for each of the ten course elements. The emphasis of the paper should be on modeling applications, outcomes, and new learning.
- Must be 2000-2500 words (excluding title page and references page), double-spaced, and formatted according to APA style as outlined in the .
- Must include a separate title page with the following:
- Title of paper
- Student’s name
- Course name and number
- Instructor’s name
- Date submitted
- Must use at least three scholarly sources in addition to the course text.
- Must document all sources in APA style as outlined in the Ashford Writing Center.
- Must include a separate references page that is formatted according to APA style as outlined in the Ashford Writing Center.