Homework answers / question archive / Part 1 - In-depth decision analysis Problem Note: This is very similar to what you did in Part 2 of Assignment 2 but please read carefully as there are some minor changes

Part 1 - In-depth decision analysis Problem Note: This is very similar to what you did in Part 2 of Assignment 2 but please read carefully as there are some minor changes

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Part 1 - In-depth decision analysis

Problem

Note: This is very similar to what you did in Part 2 of Assignment 2 but please read carefully as there are some minor changes.

Monte waited patiently for the board of directors of AgroPest International to absorb his presentation. The company, which produced agricultural fertilisers and pesticides, had a new CEO who values evidence-based decision making. The board of directors were used to presentations that relied on instinct and theory. They were not used to what they just saw, which was a decision tree with probabilities, expected values, and return to risk ratios. They just wanted to hear how staying with the old pesticide was the best idea because that’s what they were comfortable with. But Monte had just shown that the best decision based on evidence would be to go with the new pesticide. He knew there were concerns with the new product being delayed for release because it was still in development. But the current product might get banned due to health concerns! Shouldn’t that be reason enough? He shook his head and leaned back to wait for the comments to begin.

Figure 1: Decision tree Monte presented to the board.

 

 

 

“Well, I’ll start,” said John, one of the older board members. “This new product mumbo jumbo doesn’t make sense to me. But I vehemently disagree with your numbers for the current product. Yes, there are health concerns, but they are being blown out of proportion by those hippies. It’s way too high at 30%. It needs to go down.” He looked at a sheet in front of him. “Yes, that’s more reasonable: a 20% chance of an out-and-out ban, all other assumptions saying the same.”

 “Yeah, and even if there were a ban, the current product still has value. We can sell it in other countries that have more favourable ‘regulations’. Even with a ban, it must be worth $300,000 at least,” added Pete, a close friend of John’s. “With a banned value of $300,000 plus John’s belief in the smaller chance of a ban, surely we’re better off with the current product.”.

 Marla cleared her throat, “You both have a better handle on the current product, but as I’ve been working on the new product, I’d like us to be more conservative with our estimates. I can live with Pete’s valuation of the current product, if banned, at $300,000. I can also live with John’s belief that there is only a 20% probability of the current product being banned. But the value of the new product based on different sales conditions seems a bit high. They should all be reduced by $100,000 just to play it safe. Hmmm … I also think the probability of high sales is very optimistic. We should lower it to 50% regardless of whether there is a delay or not. I would rather we err on the side of caution.”

 Steve piped up, “I agree with everything said so far, except on the overly optimistic chance of a delay for the new product. I think your probabilities about a delay are backwards, Monte. Let’s be realistic. With a new product, we know that a delay is likely. It should be a 60% chance of a delay. So, let’s go with John’s 20% probability of a ban; Steve’s valuation of the current product being worth $300,000, if banned; and Marla’s two suggestions of 50% probability of high sales and reduction in value of the new product by $100,000. But let’s change the probability of a new product delay to 60%.

 Monte made notes on what was said. He ran different analyses based on each individual suggestion and a combination of suggestions. After a few moments, he let out a sigh of relief, “If we make all of your changes, the evidence still looks like it favours going with the new product. Let me explain.”

 (This case is taken and modified from pp. 228-9 of Clemen, R. T., & Reilly, T. (2014). Making hard decisions (3^rd edition). Cengage Learning.)

IMPORTANT NOTE: You should notice that each member builds on the previous member’s input. That is, each of the board member’s input adds a new change to the scenario but does NOT affect any previous board member’s input.

Added (April 6th): Part highlighted in green above to clarify that we are building on all the previous work (including both parts of Marla’s suggestion).

 

 

Summary of steps

1. Use the decision tree in Figure 1, to create a payoff table. (Tab 1 Payoff & Probability)
2. Use the decision tree in Figure 1, to create a probability table. (Tab 1 Payoff & Probability)
3. Show how Monte arrived at his recommendation by doing a thorough decision analysis. (Tab 2 Monte’s Analysis)
4. Run the analysis multiple times using the suggestions provided by the board members. (Tab 3 Additional Analyses - Board Member Analysis)
5. Create an automated, interactive spreadsheet that allows the board members to put in their own values to explore the decision analysis.  (Interactive - Spreadsheet)
6. Run your own what if analysis to explore whether there is a situation where the current product is a better option than the new product. (Tab 3 Additional Analyses – What if Analysis)
7. Put together a succinct argument for why, regardless of the suggestions made, Monte’s original recommendation stands. (Final Exam Submission.docx)

 

NOTE: For all of the Excel work that you are asked to do. Do NOT remove formulas. For example, leave your EMV cell as something like “=A2*B2+A3*B3” instead of typing in the final answer. You will be marked on your answers and your work. The work is what is written in the cells.

 

Detailed instructions

 

329332. 1. Create a payoff table. Put your answer in the tab entitled “Tab 1 - Payoff & Probability” in Excel. (same as step 1 in Part 2 of Assignment 2 - but ensure that you update the values to align with THIS decision tree)

 

In class, the conditions were always the same regardless of the decision (e.g., demand of clams). But in this case, the conditions are different depending on the decision.

• Stop: What are the TWO decisions?
• Stop: For each decision, what are the associated conditions? Hint: One decision has two conditions. The other has four conditions.

Due to this, you cannot create a table where each row represents the same thing. See the Excel template for what the payoff table will look like for this question. 

 

329332. 2. Create a table with the probabilities. Put your answer in the tab entitled “Tab 1 - Payoff & Probability” in Excel. (same as step 2 in Part 2 of Assignment 2 - but ensure that you update the values to align with THIS decision tree)

 

See the Excel template for what the payoff table will look like for this question.

 

Hint: The probabilities for Decision 1 can be read straight off the tree. But the probabilities for Decision 2 need to be calculated. Look at p. 165 of the textbook (Section 4.2: Conditional Probability - Multiplication rule) for help on how to do this. You may also find this article useful.

 

 

329332. 3. Show how Monte arrived at his recommendation by doing a thorough decision analysis. Put your answer in the tab entitled “Tab 2 Monte’s Analysis”. (same as step 3 in Part 2 of Assignment 2 - but ensure that you update the values to align with THIS decision tree)

To do this, engage in decision making by finding the optimal decision using each of the following decision methods.

 

• Maximax
• Maximin
• Expected monetary value
• Coefficient of variation
• Return to risk ratio

 

329332. 4. Run the analysis multiple times using the suggestions provided by the board members. Put your answer in the tab entitled “Tab 3 Additional Analyses” under the heading “Board Member Analysis”.

 

Run four additional analyses that calculate the same values for the decision methods in Step 3. Each analysis is based on one of the board members suggestions:

1. John’s suggestion.
2. Pete’s suggestion.
3. Marla’s suggestion.
4. Steve’s suggestions.

 

Clarification (Added April 5th): You do not need to show your work for this. Simply fill out the tables in Tab 3. Hint: Start thinking about how you could automate this process so it would be quick and easy.

 

 

329332. 5. Create an automated interactive, spreadsheet that allows the board members to put in their own values to explore the decision analysis. Put your answer in the tab entitled “Interactive – Spreadsheet”. Put your work in the tab “Work for interactive”.

 

When creating the interactive spreadsheet, set it up so that the board members can input different values for the following:

1. the probability of a ban,
2. the probability of a delay,
3. the probability of high sales (if no delay),
4. the probability of high sales (if delay), and
5. increase/decrease the value of the new product by a fixed amount (e.g., increase by $100,000). That is, if you increase by $100,000 then all four profits for the new product will increase by $100,000.

 

Then the output should be relevant values of the decision method (e.g., EMV for both products) and the best decision for the decision method (e.g., “New product”).

 

In other words, the goal of the automated, interactive spreadsheet is that a board member can input various options for the probabilities and payoffs (as listed above) and the spreadsheet automatically calculates the decision analysis.

• The interactive part is that the board members get to enter values. (Tab: Interactive – Spreadsheet)
• The automated part is the calculation of the decision analysis. (Tab: Work for Interactive)
• For the list above, not all values in the decision tree are listed. Assume any unlisted values are staying constant. E.g., the payoff for the ban is not listed as an option so assume that it stays fixed.

 

Suggestions steps/process:

• In Excel, a template to start is provided. The template is just a starting point and a basic starting point. It was meant as a hint to get started. Feel free to change it completely.
• Hint: Build off the work you did in Steps 3 & 4 but make it general for anyone’s suggestions.
• Format the interactive part of the spreadsheet to make it user-friendly. It is suggested that the template is a starting point, and you work to improve it. Remember your audience is board members who are unfamiliar with evidence-based decision making. Provide them help/guidance so they understand what to do.
• Make sure to include a way to show which decision is the best for the company for each different analysis.
• Think about:
  • When someone is using the interactive spreadsheet, how are you communicating the results of the analysis and what the best decision is? Is it clear and easy to understand?
  • Is there a way that you could visualise the results of the analysis for the board members?
  • Are instructions needed? If so, did you provide them? Are they succinct?

 

329332. 6. Run your own what if analysis to explore whether there is a situation where the current product is a better option than the new product. Put your answer in the tab entitled “Tab 3 Additional Analyses” under the heading “What if Analysis”.

 

Use your spreadsheet from step 5 to run multiple analyses. Your goal is to investigate:  Is the optimal decision always the new product? If not, when is the current product better? Is that a realistic situation?

 

To do this: 

• Run multiple different analyses by changing the probabilities and payoffs outlined in Step 5 but choosing your own.
• Try to find situations where the current product is the best option.
• Hint: Try extremes (e.g., probability of ban is very high vs. very low).
• Keep track of our analyses (perhaps copy of and paste the different answers in a tab).
• Then choose four of the one’s you did that demonstrate either that the current product is never the right choice, or the situations where it is the right choice.

 

329332. 7. Put together a succinct argument for why, regardless of the suggestions made, Monte’s original recommendation stands. Put your answer in the Word Document “Final Exam Submission.docx”

 

Based on your analysis, write a final report to the board that

• highlights why the new product is the optimal decision, and
• explains when the current product is better (or if that is not possible) but why this is not likely to occur.

 

Organise the report as follows:

1. Descriptive title. Write a succinct title that lets the reader know exactly what the report is about. For example, “Analysis of new product” is too vague. While “Detailed What if Decision Analysis of the state of the old vs. new pesticide based on probability of ban, probability of high sales, and probability of delay” is not succinct. Find something in between.
2. Executive summary: In point form, provide an overview of why, after your thorough analysis, you are recommending they go with the new product. Write this as a TLDR (too long, didn’t read). That is, the goal is to give a very quick, to the point summary that a person can quickly read and get the bit highlights. Your TLDR should include the following sections:

• Recommendation:
• Reasons:
• Caution: (if appropriate - see below)
• Evidence (in the form of a table)

 

Then for each point include the following:

• Recommendation:  State the recommendation.
• Reasons:
  • Two or three high level reasons for why the new product is best. Your answer should reference the multiple decision methods and not just rely on one. Make sure to discuss both expected return and risk.
    • In Assignment 2, we did one analysis. Therefore, we presented a summary of that analysis by picking two or three decision methods and explaining how they support that the new product is better than the current product.
    • Here we have done multiple analyses. Thus, this summary should still focus on two or three decision methods but needs to succinctly summarise how the new product was best even with the changes in the multiple parts of the decision tree.
      • It is expected that the four what if analyses shown in Tab 3 are used here.
      • Think about providing ranges to summarise: “The expected return for the new product ranges from $X to $Y when …”.
  • Do not get into details of listing multiple numbers: “the EMV for the new product is $123,456 and the EMV for the current product is $98,765. Therefore the EMV for the new product is $20,000 more than the current product”. That is too long. Instead, simply state which one is bigger and whether it is by a small or large amount. Use appropriate business language (instead of statistical language).
• Caution: Is there a time when the current product is a better decision? If so, state it here. If not, do not include a caution and instead include an additional bullet in the “Reasons” section stating that there were no instances where the current product was a better option.
  • Please note: If the maximax or maximin contradict the decision, please ignore. We are referring to when the EMV or RTRR or CofV suggest the current product is better.
  • If you say there is no situation where the current product is better, there better be evidence to support that!

3. Evidence: Insert a table that contains specific values to support your executive summary. This is based on your work in Step 3, 4 and 5, but focusing on the values you used in your executive summary. Include a relevant summary of your work from steps 3, 4 and 5.

• Note: Four is the minimum number of additional analyses and you can provide more but do not go overboard! The goal here is to show that you understand what a “what if analysis” entails - in the sense that it is providing an overview of what could happen. 
• Title your table: “Table 1: [clear title that indicates what it is about]”. Put the title above the table.
• Include headings on your table.
• Organise it so it is easy to read.
• After reading the executive summary, the reader should be able to look at the table and go “ahh, I get what they are saying.” That is, there needs to be a clear connection and alignment between the executive summary and the work in the table.

 

Part 2 - Model building in linear regression

Problem

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts.

They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know:

 

• Which variables are significant in predicting the price of a car?
• How well do those variables describe the price of a car?

 

Based on various market surveys, the consulting firm has gathered a large data set of different types of cars across the America market.

 

Business goal

We are required to model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent variables. They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. Further, the model will be a good way for management to understand the pricing dynamics of a new market.

 

Question modified from: https://www.kaggle.com/datasets/hellbuoy/car-price-prediction

Summary of steps

1. Identify potentially good predictors of the dependent variable. (Tab 1 Predictors & Final Exam Submission.docx)
2. Determine if collinearity exists in the model and eliminate any problematic variables. (Tabs 2 Collinearity &  Final Exam Submission.docx)
3. Using the remaining variables, run all possible models. (Tabs 3 “Name of variable & Final Exam Submission.docx)
4. Determine the best model and explain why it is the best model. (Final Exam Submission.docx)
5. Provide a detailed summary of the best model to management. (Final Exam Submission.docx)

 

Detailed instructions

329332. 1. Identify potentially good predictors of the dependent variable. Put your Excel work in Tab 1 Predictors and write the explanation of your work in Final Exam Submission.docx.

 

• In the Excel spreadsheet, make a tab called “Predictors”. Do an appropriate analysis to determine if they are potentially good predictors of the dependent variable. If a variable is not a good predictor, eliminate it from the list of candidates.
• In the Word document,
  • copy and paste the results of your analysis. For example, if you calculated a confidence interval, do not include the steps to get the confidence interval but only include the final confidence interval.
  • Then write a short summary (two to three sentences) that explains what you found out and how you know it. That is explain the results of your analysis. This portion is written for your instructor. Thus, it is ok to use statistical language.

 

329332. 2. Determine if collinearity exists in the model and eliminate any problematic variables. Put your Excel work in Tabs 2 Collinearity &  Final Exam Submission.docx.

 

• In the Excel spreadsheet, make a tab called “Collinearity”. Do an appropriate analysis to determine if collinearity is a problem. If there is a problem with collinearity, remove the offending variable and rerun the model. Repeat until collinearity is no longer a problem.
  • You might have multiple “Tab 2”s. If so, name them “Tab 2a – Collinearity”, “Tab 2b – Collinearity”, etc.  
  • At the end of this analysis, you will have your final list of candidate independent variables.
• In the Word document,
  • copy and paste the results of the work you did. For example, if you calculated a confidence interval, do not include the steps to get the confidence interval but only include the final confidence interval.
  • Then write a short summary (two to three sentences) that explains what you found out and how you know it. This portion is written for your instructor. Thus, it is ok to use statistical language.

 

329332. 3. Using the remaining variables, run all possible models. Put your Excel work in Tabs 3 “Name of variables” &  Final Exam Submission.docx.

 

• In the Excel spreadsheet, using the variables that have minimal collinearity and are good predictors of the dependent variable (i.e., the results of your work from Steps 1 and 2), build ALL possible models.
  • • For each set of variables, make a new tab and name it based on the dependent variables used in that specific model. Put the analysis you did to build that model in that tab.
• In a Word document, make a table of the relevant portions of the analysis for each model that you would use to determine if it is the best model.

 

329332. 4. Determine the best model and explain why it is the best model. Put your work in Final Exam Submission.docx

 

• Write a paragraph that states the best model and explains how you determined it was the best model. Include evidence to support your decision. This portion is written for your instructor. Thus, it is ok to use statistical language. It is expected that the analysis is thorough.

 

329332. 5. Provide a detailed summary of the best model to management. (Final Exam Submission.docx)

 

Now that you have the best model, provide a detailed summary of the model (one or two paragraphs) that helps management understand the business situation predicted by the model and the accuracy of the model. The goal here is to demonstrate that you can effectively communicate the results of an analysis by:

1. Providing a brief summary of how the best model was determined (one or two sentences at most).
2. Explaining as each independent variable changes, how does the dependent variable change?
3. Explaining how good is the model? (consider both accuracy and appropriateness).
4. how the model can be used within the business context. When discussing accuracy, include a prediction of the dependent variable with a discussion of its accuracy.

 

• When writing the summary, ensure you are showing the following:
  • Can you demonstrate that you can communicate the results of your analysis using business language (rather than statistical language)
  • Can you demonstrate that you can articulate “why should someone care?” about the results of your analysis?
  • Can you demonstrate a depth of understanding of the course content?

What do I submit?

1. Completed Excel spreadsheet for Part 1 that shows Steps 1, 2, 3, 4, 5, which includes the following tabs, in this order:
   1. Tab 1: Payoff and probability
   2. Tab 2: Monte’s analysis
   3. Tab 3: Additional Analyses
   4. Interactive Spreadsheet
   5. Work for Interactive
2. Completed Excel spreadsheet for Part 2 that shows Steps 1, 2, 3, which includes the following tabs, in this order:
   1. Original data (nothing to do here, but make sure the dataset you are using is included)
   2. Predictors
   3. Collinearity (multiple tabs)
   4. Multiple tabs that show the model building for Step 3.
3. Word document: See Final Exam Submission.docx for a template of what this needs to look like.
   1. Report from Step 6 of Part 1
   2. Results and summary for Step 1 of Part 2
   3. Results and summary for Step 2 of Part 2
   4. Table for Step 3 of Part 2
   5. Explanation for Step 4 of Part 2
   6. Detailed summary of best model for Step 5 of Part 2
   7. List of “Outside sources”.

Breakdown of marks

For each learning outcome, you will be assessed on how effectively you demonstrated your understanding and ability to do the learning outcome. These criteria were created using the grading system found on p. 4 of your course outline. Note: A “B” is an “above average performance”. If you do exactly as asked and demonstrate that you can do the work, you will earn a B. To earn an A or higher, you need to go above and beyond. That is, you need to show you really get it (not just that you get it). In class, we will discuss what an A looks like. 

• Superior performance– A+: The answer is correct, complete, and demonstrates a very strong understanding of the relevant course content. (100%)
• Excellent – A: The answer is correct, complete, and demonstrates a strong understanding of the relevant course content. (85%)
• Good - B: The answer is mostly correct and complete, with no errors or only small ones. Understanding of relevant course content is generally demonstrated. Above average performance. (75%)
• Satisfactory - C: The answer is mostly correct and complete, with either multiple small errors or a significant error. Basic understanding of course content is demonstrated. (65%)
• Marginal Performance - D:  There is more than one significant error. The response suggests a lack of understanding of course content. (55%)
• Fail – F: There are multiple errors and overall, the answer does not demonstrate understanding of the course content. (25%)
• Not done: The component is missing. (0%)

 

Learning outcomes	Where it will be evaluated	How it will be evaluated	Weight
1. Summarise business problems through the creation and interpretation of payoff tables, and decision trees. 2. Solve business problems by applying probability, expected value, and standard deviation in multiple situations.	Excel for Part 1: All tabs	The payoff table and probability table are correctly created and accurate based on the decision tree and the suggestions of board members. The maximax, maximin, EMV, coefficient of variation and RTRR are correctly calculated for both decisions, in Monte’s analysis and the minimum of eight additional analyses (4 board members + minimum 4 what if analyses). The minimum of four additional analyses are appropriately chosen to convince the board that the new product is the best choice. The interactive spreadsheet is correctly made and set up in a way to support the board of directors’ understanding of the decision analysis.	20%
3. Use various techniques to analyse large data sets and to find meaningful patterns. 4. Effectively communicate the results of an analysis.	Word - Report for Step 7	The title is descriptive but succinct. The executive summary clearly, efficiently and appropriately states the reasons behind the recommendation and uses language appropriate to the audience. Appropriate choices are made to highlight the most important decision methods for this analysis. The table is nicely organised, easy to read, and aligns with the reasons. Further, the table shows the multiple scenarios. The caution is appropriate.	25%
5. Choose appropriate variables to include in a regression model within a given business situation. 6. Assess which model is a better fit for the data.	Excel for Part 2 – All tabs   Word - Steps 1 to 4 for Part 2	Potentially good predictors are well chosen based on the evidence from the analysis. Collinearity is effectively addressed. All possible models of the remaining independent variables are investigated. The reasoning for the best model is appropriate and well explained.	25%
7. Interpret the results of the model in a business situation.	Word - Step 5 of Part 2	The summary of the best model answers the questions: 1) what does the model indicate, and 2) why should management care? The   language used is appropriate for the audience.	30%
			100%

 

Outside sources

NOTE: If you directly use an outside source (e.g., paraphrase or quote), you still need to do a proper APA (American Psychology Association) citation. What is described below is only for outside sources that you looked at for help and not for directly writing your final work.

 

As I am absolutely convinced that most of you are using outside sources and failing to cite them, let us make it easy. If you look at a website outside of the class (i.e., not our textbook or from D2L), insert the URL in the table below, state which step of the assignment you used it for, and very briefly how you used it. In the first row, I’ve provided an example of what I’m expecting. Please delete it before submitting.

 

URL	Step	How used
https://www.statisticshowto.com/probability-and-statistics/correlation-coefficient-formula/	2	Read about r and how to interpret it.

 

If you claim you used no outside sources OR you directly used all outside sources, instead of submitting the above table, include the following sentence in your final work (see submission guidelines for where):

“We, [insert full names of all group members], solemnly swear that we did not use any outside source (except those properly cited using APA referencing) to complete this assignment. I understand that failure to indicate outside sources is an act of academic misconduct and could result in getting 0 on this assignment.”

 

There are no marks associated with completing this but failure to do so could have a very negative impact on your grade (hint: academic misconduct).

 

Notes on plagiarism (and how to avoid it)

Plagiarism is any act where you present work as your own when it is not. When plagiarism is found, a letter is sent to the Office of Student Conduct. 

 

When you submit anything at MRU with your name on it, you are stating that you are comfortable with all the work presented and you agree that it is your group’s work. Therefore, if plagiarism occurs, then all group members are held accountable. 

 

Here are two common scenarios that I have seen in this class. 

 

Scenario 1: Suppose you do not quite understand standard deviation. So you google “standard deviation” and then you click on Standard Deviation Definition on Investopedia (https://www.investopedia.com/terms/s/standarddeviation.asp). One sentence makes sense to you “Standard deviation measures the dispersion of a dataset relative to its mean.” What is the right way to deal with it, so you are not engaging in plagiarism?

 

Options	Result
We found the standard deviation of income to be $4000. Standard deviation measures the dispersion of a dataset relative to its mean.	Plagiarism! This is a direct copy and paste without any indication of the source. This is work presented as your own when it is not.
We found the standard deviation of income to be $4000. Standard deviation measures the scatter of a dataset relative to its mean.	Plagiarism! Though it is not a direct copy, it is still close to the website's wording and it is still presented as your work when it is not.
We found the standard deviation of income to be $4000. Standard deviation measures the scatter of a dataset relative to its mean (Hargrave & Westfall, 2020).	Not obviously plagiarism but still borderline. A correct in-text citation was used, but the quote was insufficiently paraphrased. Changing one word is not paraphrasing.
We found the standard deviation of income to be $4000. This measure indicates how much the incomes vary from the mean (Hargrave & Westfall, 2020).	Not plagiarism : ) There is a correct APA in-text citation and the sentence was paraphrased.
We found the standard deviation of income to be $4000. “Standard deviation measures the dispersion of a dataset relative to its mean” (Hargrave & Westfall, 2020, para. 2).	Not plagiarism : ) Direct quote is used (and indicated by quotation marks) and a correct APA in-text citation was used. BUT in this assignment, you should avoid using direct quotes and instead focus on what these definitions mean in the context.

Note: An APA proper reference at the end of the document needs to be included if outside sources are used. For this example, it would look like: 

 

Hargrave, M. & Westfall, P. (2020, July 21). Standard deviation definition. Investopedia. https://www.investopedia.com/terms/s/standarddeviation.asp

 

Here are some good habits: 

• Never copy and paste a sentence straight into your assignment document. Instead, immediately paraphrase it and include the reference. A lot of students copy and paste and then forget to change it –it is still plagiarism. 
• If you spend any time on a website as you are doing this assignment, write down the website's name and URL in a document. Then include the websites in your references (even if you did not directly cite it). 

 

Scenario 2: Your friend asks to see your exam because they just want some ideas on what they could do. 

 

Do NOT share your work with anyone as this is an individual assessment.

 

Consequences of cheating

It is very important to note that in this course we do not use software to find plagiarism. Instead, we read through your work carefully and notice when the language suddenly changes from expected first-year to graduate level. Or suddenly what is being talked about does not align with what was taught in the course. At that point, we take the “strange” section and Google it. If it is taken from a website, we usually find it very quickly after that. 

 

Additionally, we do notice when a student’s work aligns too closely with other students’ work. That is, we do notice when it appears that students have collaborated to complete the final exam.

 

As this is a final exam, there is a zero tolerance policy on cheating. If any part of your final exam is plagiarised or too closely aligns with another students, the following actions will occur:

1. The student gets 0% on their final exam.
2. A written warning of the violation. A copy of the written warning will be kept by the Office of Student Community Standards as a record in case further violations are committed.

 

For your reference, here is the Code of Student Conduct and Code of Student Academic Integrity Policy which were used to determine our policy.