MKTG755 Harrisburg Negotiation Case Questions Project

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I need a word file for coding and screenshot from JPEG. Please review all 4 files which is attached below

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MKTG 755 Fall 2019 Negotiation Case Analysis, 20% of Total Course Evaluation Student Name: Nahidparveen Pathan Student Number:301057789 Instructions: • This assignment covers content related to the negotiation component only of your MKTG 755 course. • The assignment is out of 20 marks, each question is worth 4 marks. • Please write the answers in Word, save the file as a PDF and upload it to the “Negotiation Case Analysis” dropbox folder by Saturday November 30th. 1159pm (late means 0) • This assignment should only take you 15-20 minutes to complete, but being an out of class assignment you have as much time as you need. However, my advice is that in answering the questions, look beyond the obvious answer. PART A) Read “The Piano” and answer the questions (20 marks) The Piano When shopping for a used piano, Orvel Ray answered a newspaper ad. The piano was a beautiful upright in a massive walnut cabinet. The seller was asking $1,000, and it would have been a bargain at that price, but Orvel had received a $700 tax refund and had set this windfall as the limit that he could afford to invest. He searched for a negotiating advantage. The seller is not willing to sell the piano for less than $400. He was able to deduce several facts from the surroundings. The piano was in a furnished basement, which also contained a set of drums and an upright acoustic bass. Obviously, the seller was a serious musician, who probably played jazz. There had to be a compelling reason for selling such a beautiful instrument. Orvel asked the first, obvious question, “Are you buying a new piano?” The seller hesitated. “Well, I don’t know yet. See, we’re moving out of the province and it would be very expensive to ship this piano clear across the country.” Orvel “Did they say how much extra it would cost?” Seller “They said an extra $300 or so.” Orvel “When do you have to decide?” Seller “The packers are coming this afternoon” QUESTIONS: 1. Provide the BATNA for both the seller and Orvel. (4 Marks) 2. What is the target point for both the seller and Orvel? (4 Marks) 3. Based on the information in the case, what is the resistance point for both parties? (4 marks) 4. What is the Bargaining Zone in this case? (4 marks) 5. Name one tangible and one intangible factor of this negotiation (4 marks) Tangible factor – Intangible factor – Try to explain BATNA, target point, the resistance point , Bargaining Zone, Tangible factor and Intangible factor Energy efficiency in many cases is seen as the most effective way of reducing carbon emission to the environment as well as improving the security of the energy supply. The main aim of bringing in ICT in the government actions to provide energy to its citizens and businesses was to ensure that the hazardous nature of manual energy production is reduced and the energy short which is affecting the continent is addressed maximally (Kurfalı et al., 2017). Through Information communication and technology, it is very rare to emit carbon dioxide to the environment as compared to the manual method of extraction such as the fossil fuel extraction. In Its recommendation of October 9th 2009, the European commission identified that the ICT plays a vital role in the deliverance of energy which is very efficient and effective. It is therefore the efficiency and effectiveness of the ICT oriented energy that attracted the European countries as well as other countries which have adopted this system of energy production. Environment is the habitat for all earth creatures, and it is the responsibility of the human beings to ensure that its maintained all the times. Therefore, developing ways of generating efficient and effective energy without necessarily tempering with the environment is an import step by the government. It is there responsibility of the government or the ruling body of a nation or a continent to ensure that there is sufficient energy to its citizens as well as enough energy to facilitate the businesses. On the other side, upon generation of the energy, the government should also be aware that conserving the environment is its responsibility (Kurfalı et al., 2017). This would then lead to producing enough and efficient energy which has no negative effects on the environment either through production or during use. In some years, there was a case back which affected the Volkswagen motor company and lead to burning its vehicles in the European markets. The reason behind this case was that the company manufactured vehicles which emits excess carbon dioxide to the environment and this was against the energy and regulation laws set in the region. The Volkswagen’s vehicles rate of emitting carbon dioxide to the environment was above the set standards and this lead to its burning from operation which saw the company recalled millions of its cars which were sold as well as compensating their customers. The argument behind this action was that when the Volkswagen vehicles are taken to testing stations or when they move close to authorities, they tend to emit less carbon dioxide but when in free regions, they emit excess gas which effectively pollutes the environment (Sen & Ganguly, 2017). Despite the company denying this action, their cars were in the long last tested and found to have broken energy and regulatory set laws hence burning their products in the market. School of Computer & Information Sciences ITS 836 Data Science and Big Data Analytics Text Analysis Part 2 ITS 836 1 Submission Guidelines • Submit the document with your name, id – Clearly mark the question # for all answers text and figures – Submit the code in a separate text file marking Question # • Do not – Submit a screen shot of the code in Rstudio • Export – The picture in a jpg that you copy into your document – the code into a .R file and attach to your submission ITS 836 2 Week 13 Homework Text Analysis Part 2 https://www.kaggle.com/nicapotato/womens-ecommerce-clothing-reviews A women’s Clothing E-Commerce site revolving around the reviews written by customers. This dataset includes 23486 rows and 10 feature variables. Each row corresponds to a customer review, and includes the variables: • Clothing ID: Integer Categorical variable that refers to the specific piece being reviewed. • Age: Positive Integer variable of the reviewers age. • Title: String variable for the title of the review. • Review Text: String variable for the review body. • Rating: Integer variable for the product score granted by the customer from 1 Worst, to 5 Best. • Recommended IND: Binary variable stating where the customer recommends the product where 1 is recommended, 0 is not recommended. • Positive Feedback Count: Positive Integer documenting the number of other customers who found this review positive. • Division Name: Categorical name of the product high level division. • Department Name: Categorical name of the product department name. • Class Name: Categorical name of the product class name. Q1 Perform: a. Text extraction & creating a corpus b. Text Pre-processing c. Create the DTM & TDM from the corpus d. Exploratory text analysis e. Feature extraction by removing sparsity f. Build the Classification Models and compare Logistic Regression to Random Forest regression https://medium.com/analytics-vidhya/customer-reviewanalytics-using-text-mining-cd1e17d6ee4e ITS 836 3 Week 13 Homework Text Analysis Part 2 Analyze Customer Reviews b. What is the classification question? c. Use CM for Random Forest classifier to calculate: Q2 – Analyze the customer reviews in the file Restaurant_Reviews.tsv a. Explain each step for the following text clean-up commands TP = # True Positives, TN = # True Negatives, FP = # False Positives, FN = # False Negatives): Accuracy = (TP + TN) / (TP + TN + FP + FN) corpus = VCorpus(VectorSource(dataset_original$Review)) corpus = tm_map(corpus, content_transformer(tolower)) corpus = tm_map(corpus, removeNumbers) corpus = tm_map(corpus, removePunctuation) corpus = tm_map(corpus, removeWords, stopwords()) corpus = tm_map(corpus, stemDocument) corpus = tm_map(corpus, stripWhitespace) d. Apply the logistic regression classifier to the problem – recalculate “Q2c” i.e. TP, TN, FP, FN, Accuracy e. Apply SVM classifier to the same question – recalculate “Q2c” Uncomment in order to see the impact: #as.character(corpus[[841]]) #as.character(corpus[[1]]) ITS 836 4 Week 13 Homework Text Analysis Part 2 https://quanteda.io/articles/pkgdown/comparison.html Q3: Study the quanteda toolkit for R Q3a: Compare quanteda to: alternative R packages for quantitative text analysis (tm, tidytext, corpus, and koRpus) Q3b: Install(quanteda) and then library(quanteda) – and explain different features of the quanteda package for text analysis ITS 836 5 Week 13 Homework Text Analysis Part 2 Spam Text Message Classification Q4 Spam Text Message Classification – Use the quanteda package to perform “spam” classification on the text message file in Q4 The file name: Q4.spam-text-message-classification.zip a. Create the ”word” cloud for spam and ham messages b. Apply a Naïve Bayes Classifier and compute TP, TN, FP, FN, Accuracy c. Use a Logistic Regression Classifier and compute TP, TN, FP, FN, Accuracy d. Use a Random Forest Classifier and compute TP, TN, FP, FN, Accuracy ITS 836 6 Week 13 Homework Text Analysis Part 2 https://www.kaggle.com/rtatman/state-of-the-union-corpus-1989-2017 Q5. The State of the Union is an annual address by the President of the United States before a joint session of congress. In it, the President reviews the previous year and lays out his legislative agenda for the coming year This dataset contains the full text of the State of the Union address from 1989 (Regan) to 2017 (Trump). a. Topic modelling: Which topics have become more popular over time? Which have become less popular? b. Sentiment analysis: Are there differences in tone between different Presidents? Presidents from different parties? ITS 836 7 Questions? ITS 836 8 …

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