Questions and Topics We Can Help You To Answer:
Paper Instructions:

Data will need to be plugged in and graphed on excel to fit the curve.  We are looking at acceleration take off and landing a right sided leap.

Abstract
            This project guide introduces you to why you are doing projects, gives guidance on selecting projects, and discusses the main things that are expected: ambition and explanations.  It finishes with the grading rubric I use.  Details on the specifics of writing a paper are left to another document.
Goals
Why am I requiring you to do a project?  I want you to
Get real-world modeling experience,
Train for a potential future job,
Exercise what we’ve learned in class,
Go deeper than what we’ve learned in class,
Actually improve the real world,
Think about how you might teach a topic (for education majors),
Learn and practice good academic writing,
Prepare you for the Math Contest in Modeling (MCM),
Perhaps submit your project to a peer-reviewed journal,
Have fun, and
Show me that you’ve done most of that.
Good and Bad Projects
What makes for a good project?  Many people draw from their job experience.  If you’ve ever cursed or grumbled about some aspect of your job that seemed suboptimal, that might be a good topic for a project.  One student based his project on his job as a pizza delivery driver, optimizing routing and dispatch decisions.  Other students have worked on demand prediction and staffing at restaurants where they were servers or managers.  Your project could also focus on a topic related to a job you want to have, like being an actuary, K-12 teacher, stock market analyst, etc.
What makes for a bad project?  Sadly, some projects turn into exercises in basic accounting, adding up a collection of various simple cost terms.  This does not reflect the skill required of someone in a 300-level (or above) college math class.  I try to catch these projects at or before the proposal stage, and suggest some appropriately advanced analysis to bring them up to par.  While some of the mini-projects we do at the start of the semester might be like this, we do them to focus on the form of the report, while keeping the math deliberately simplistic.
I encourage you to base your projects on real problems, often involving real data.  You will learn things from analyzing large sets of real data that I could never summarize for you in class.  In the 500-level courses, though, it is sometimes appropriate to work on extending an existing model without having real data.  I will try to guide you to find the right balance.
The Proposal
To help you along the way, I require more than just a project report.  I require a project proposal (just as many agencies that make grants require a proposal), to get you thinking and exploring some topics before it’s too late.  I also require a presentation, as many research projects are disseminated via conference talks long before they appear in the academic literature.  We will discuss each item (proposal, report, and presentation) in more detail later.

As you are organizing your thoughts to write a proposal, it is often very helpful to sketch the graphs you expect to include in your final report.  They don’t have to be precise or even match what you actually get, but they will help you formulate the questions you want to ask.  For example, you might want to see how cost varies when you vary the number of servers—does it go down initially and then back up?  Or does it just go up?  Is it concave up or down?
Project Scope
How big should your project be?  First, let’s look at some quantitative measurements:
·        I don’t count pages, but most good projects are at least 6 pages, and often more like 12, including graphs, tables, and appendices.
·        This is a 3-credit class, and you will devote at least two weeks’ worth of homework time (at 3 or 4 homework hours per credit) to doing and writing up the project.  Add to that the time spent on the proposal and the presentation.
More important, though, is what you do in your project.  Excellent projects involve either doing a substantial extension of existing mathematical models or analyzing a real (and moderately large) data set.  By moderately large, I really mean large enough to have outliers, and usually with multiple related variables.  In practice, this means that the data set fills more than one page.  Most of the time, students can get a good data set electronically.  I am not asking you to take data by hand with a stopwatch and a clipboard, for example.  Sometimes, you might need to type in data that you have found in printed form (for example, a printout from a restaurant computer system that can’t export electronically).  If you get a data file in a strange format, I can often help decode it in an automated way so you don’t have to retype it, but let me know early on in the semester.

In some cases, you might not be able to get the data you want.  One way around this is to simulate data yourself (with my help), then analyze it as if you didn’t know how it was generated.  This is also a possibility even when you have a real data set; it can help you figure out how your analysis works when you know exactly what is going on in the data set.  Of course, you include in your report how you generated the data set.

You are given two scores for ambition: one for ambition in your topic selection, and another for what you accomplish.  The best papers solve the problem they pose in more than one way, and compare the results.  For example, a project on staffing at a restaurant might start by assuming that the future demand is known precisely, and find the optimal schedule.  Then, the project would include some measure of randomness in the future demand, re-optimize, and compare the resulting schedule and bottom-line cost to the simpler model.  Good-but-not-great papers solve the problem in only one way, and interpret the results rather than simply presenting the final numbers and ending.  Below-average papers impose unreasonable assumptions that make the problem too simple (sometimes they do this unwittingly).  Often, the line that divides “too simple” from “just simple enough” is not as clear to students as it is to professors, so check with me often to make sure you are on the right track.

Explanations
It is important to be able to do good mathematical work; it is just as important to be able to explain it well.  If you can’t explain it well, nobody will know about the good work you did.  One of the first rules of writing is to know your audience.  I want you to write as if your reader is someone in a similar class but in a different U.S. state.  That is, they have covered the same sort of material we have, but probably not the specific examples that we have done in class.  Also, they have not heard the discussions that you and I had in office hours about your project.  This has two effects: anything you explained to me in person should be written again in your project, and anything I explained to you or asked you to do should be explained again in your project.  For example, one student did a project involving ride-on lawnmowers.  I said that for a rough estimate, we could use the gas mileage of a subcompact car as the mileage of the mower; that student should think about why I suggested that, and write the reasoning into the paper.

Your paper should be understandable to a reader who has not seen your spreadsheet or other program.  When papers are published, they usually do not include the software that the author(s) used to generate results.  While I encourage you to submit your calculation files along with your project paper, I will not use them to determine your grade for correctness or explanation.  The project paper must be able to stand alone.  It should include some explanation of how you verified that your computations give reasonable and error-free results.  For example, you might use a set of pretend data where it is obvious what the outcome should be, and then see if you get that from your software.  Then, describe that verification experiment in the paper.  For example,  if you are trying to compute the center of a circle defined by a bunch of data points on the perimeter, you could create some testing data where you know exactly where the center of the circle is, and see if you get that from your estimation procedures.

Many times, students are not very clear on how they did their computations.  A collection of equations doesn’t always show which ones are used first, which are second, etc.  It might help to create a flowchart that highlights what your input values are, how you process them, and what your output values are.  For example, here is how the computations go for an M/M/c queueing system:












Lesson Plan Projects
Secondary-Education majors may do a project that consists of creating a set of lesson plans that could be used in a future job.  I encourage this.  The danger is that, since the lessons are oriented toward pre-college students, it takes an extra effort for you to show that you have learned the material at the college level.  Here are my requirements for lesson-plan projects: you should have
·        A list of learning outcomes,
·        Alignment with standards (where possible),
·        New examples:
o  Not just taken from another source
o  Entirely worked out (show work, not just answers)
o  Practical examples, with sensible values,
·        Worksheets or homework set(s) with an answer key,
·        Samples of forecasted student responses:
o  Excellent responses,
o  Good responses,
o  Common errors, and suggestions for helping students overcome them,
·        Material that goes beyond the lesson, in case your original material runs short, or in case a smart student asks a probing question.
Prof. Carla Tayeh, one of our EMU math education professors, says that she looks for the following things in a lesson plan:

activities that are mathematically rich,
questions that ask students to think deeply about the material,
problems that are engaging, and
teaching in a meaningful way using visual models.
.
If two projects are required in a semester, then at least one of them must be an ordinary modeling project rather than a set of lesson plans.  This is because I want secondary-ed teachers to have experience in doing real applied mathematics, so they can share that experience with their students in the future.

Grading Rubric
Roughly speaking, scores near 4 are like an A grade, scores around 3 are like a B grade, etc.  When the bottom end of a scale is not specified, it is still possible to achieve those low scores.

Proposal:
4 points for describing the system,
            4 points for clarity of objectives,
            4 points for references (should have roughly 3 references, mostly academic rather than Wikipedia-like)

Report:

Ambition in Topic Selection, weight = 1
            4: real-life project w/large data set, or substantial extension of other's work, or MCM or CUMCM problem
            3: minor extension of other's work, or very good explanation w/ new examples, or HiMCM problem.
            2: re-implementation of other's work
            1: situation is somewhat too simple for this class
            The professor may choose to give a higher topic ambition score that normal for projects that he particularly wants to see done.

Ambition in output, weight = 1
            4: multiple solutions based upon different assumptions about or interpretations of the task(s), usually with comparisons between solutions.
            3: one good solution and interpretation of those results
            2: solution is too simple for the situation
            1: no actual solution achieved

Correctness (not including explanation of work, mostly), weight = 1
            4: simple cases are given that show correctness
            3: appears to be correct in almost every detail
            2: a few minor details are wrong
            1: one major error
            0: two or more major errors, or explanation is insufficient to convince the professor that the work is correct.

Explanation: weight = 1
            4: Understandable by anyone with course prerequisites
            3: clear at first reading to most people in our class
            2: clear at first reading to the professor, with no gaps left.
            1: professor understands after several readings, or: at first reading, but with some gaps in explanation.
            0: professor is unable to understand it

Organization, weight = 1
            4: sections with clear/big headings, appropriate material in each section, logical flow.
            3: sections with harder-to-see headings, appropriate material in each section, mostly logical flow
            2: few section divisions, inappropriate placement of material, mixed-up flow
            1: little or no sectioning, no logical flow.

Grammar/Spelling/Sentence style, weight= 0.5
            Style refers to intricacy of sentences, passive voice, etc., apart from professional language.
            4: One or two grammar or spelling errors, good style
            3: half a dozen (or so) grammar or spelling errors, acceptable style
            2: a dozen errors or so, style is hard to read
            1: more errors, style inhibits comprehension

Professional Language, weight = 0.5
            (avoiding most contractions, informal words, slang, etc.; using We where appropriate)
            4: no changes needed
            3: a few changes needed
            2: half a dozen (or so) changes needed

Short abstract (< 50 words), weight = 0.25
An abstract is a concise summary of the whole paper, not just the conclusions, and is understandable without reference to the rest of the paper. It should contain no citation to other published work.  It should contain little or no math notation.  It should include key words that someone might be searching for using Google, etc. (but no need to repeat what’s already in the title).

            The abstract is listed last here because it has a low grading weight, but it comes before the paper (just after the title/author).
            4: summarizes the problem and solution, proper style
            3: summarizes the problem and solution, style is unsatisfactory
            2: fails to describe the problem or fails to describe the solution, but style is satisfactory
            1: like 2, but the style is unsatisfactory


Medium abstract (< 200 words), weight = 0.25
            (same grading as Short Abstract)

Here is a sample weighted average:
3: Ambition in Topic Selection,
4: Ambition in output,
4: Correctness
3: Explanation
4: Organization
3: Grammar/Spelling/Sentence style,
2: Professional Language,
3: Short abstract (< 50 words)
4: Medium abstract (< 200 words)
Total score: 3*1+4*1+4*1+3*1+4*1+3*0.5+2*0.5+3*0.25+4*0.25 = 22.25; the sum of the weights is 6.5, so 22.25/6.5 gives 3.42, which is roughly a B+ or A-.

References
Krantz, Steven G. Primer of mathematical writing,1997

Higham, Nicholas J. Handbook of writing for the mathematical sciences, 1998

Knuth, Donald E., Tracy Larrabee, and Paul M. Roberts, Mathematical writing,1989

Questions and Topics We Can Help You To Answer:
Paper Instructions:

Initial Analysis
In this assignment you will, do an initial analysis of the data to get familiar with the dataset and the audience needs, including identifying business questions. 
Following key learning outcomes from the course syllabus: 
•  Communicate complex data information and insights using data visualizations via creating dashboards, scorecards, spatial data representations and use of annotations 
•  Apply strategies for systematic visual exploration of multidimensional and heterogeneous data sets using XY plots, histograms, bar charts, etc.; 
•  Evaluate, propose and implement appropriate visualizations for a specified audience using key informational design concepts; 
Assignment Summary 
As part of the project you must do an initial analysis of the data set to get familiarize with the data and the needs of the dashboard audience. Once you understand the data it’s much easier to ask appropriate questions of the data and design a dashboard knowing the key variables. 
Requirement
Review the data set and start looking for valuable information to determine key drivers of the analysis. Analyze the columns, information and seek correlations that complete a basic analysis. In your analysis, answer the following questions. It also needs to clean dataset (Include data cleaning part).
Dataset: User_Projects.csv  (Read the ProjectDescription.pdf before use it).
Please analyze correlation for Viewers Website and Images, focus on these two variables.
1. What do you see as variables in the data? 
2. What correlations, patterns and trends do you see? 
3.    Who will be the audience of the data? What question would they ask about the data? 
4. How could a dashboard be used to show data clearly? 
5.    What types of graphs and charts can be used to clearly explain the data?

3-page paper. Presents the findings of your initial analysis and identifies the business questions that will be answered by the final project. Be sure to answer the above questions and provide visuals including graphs, charts and outputs that demonstrate your findings. 

Questions and Topics We Can Help You To Answer:
Paper Instructions:

The Normal Distribution, also called the Gaussian Distribution, is a representation of the distribution of many different types of data, especially when considering large amounts of data.  Some examples of data that are normally distributed are IQ scores, heights, blood pressure measurements and GPAs.  The empirical rule, along with the Normal Distribution provides information about the data that is easy to calculate.  Follow this link to understand the empirical rule and see an example regarding IQ scores:  http://cfcc.edu/faculty/cmoore/Empirical_Rule.htm

For Discussion #2, find an example of data that is normally distributed and cite your source.  Do not use IQ scores as an example.  Then write a Discussion post with the following information:

·      provide a brief description of the data

·      state the mean

·      state the standard deviation 

·      identify the data values that represent the middle 68% of the normal curve

·      identify the data values that represent the middle 95% of the normal curve

·      identify the data values that represent the middle 99.7% of the normal curve

Questions and Topics We Can Help You To Answer:
Paper Instructions:

Regularization can be an indirect form of variable selection. When regularization makes coefficients very small, it is suggesting you drop those variables from the model entirely. There are many other forms of variable selection (also known as feature selection) out there. Some are direct i.e. they tell you explicitly to not include the variable in the model. Others are indirect, like regularization which will shrink the coefficients of unwanted variables to zero. 

Please choose one of these variable selection methods and discuss it. Tell us what it does exactly. Discuss its pro's and con's versus other variable selection methods. Discuss a situation where you might apply this method. Cite references. If it's from the internet, a link to the analysis will suffice for citation purposes.

Questions and Topics We Can Help You To Answer:
Paper Instructions:

Part 1: Interpreting Correlations
A meta-analysis (Anderson & Bushman, 2001) reported that the average correlation between time spent playing video games (X) and engaging in aggressive behavior (Y) in a set of 21 well-controlled experimental studies was r+ = .19. This correlation was judged to be statistically significant. In your own words, what can you say about the nature of the relationship? Write a one-page response to this question.

Part 2: Correlations
You will use the following resources for this assessment. They are linked in the Resources.

Complete this part of the assessment using the DAA Template.
Read the SPSS Data Analysis Report Guidelines for a more complete understanding of the DAA Template and how to format and organize your assessment.
Refer to IBM SPSS Step-By-Step Instructions: Correlations for additional information on using SPSS for this assessment.
If necessary, review the Copy/Export Output Instructions to refresh your memory on how to perform these tasks. As with your previous two assessments, your submission should be narrative with supporting statistical output (table and graphs) integrated into the narrative in the appropriate

place (not all at the end of the document).
You will analyze the following variables in the grades.sav data set:

gender.
gpa.
total.
final.
Step 1: Write Section 1 of the DAA
Provide a context of the grades.sav data set. Include a definition of the specified variables and corresponding scales of measurement. Indicate the type of correlation for each X, Y pair (for example, Pearson's r, Spearman's r, point-biserial r, et cetera). Specify the sample size of the data set.

Step 2: Write Section 2 of the DAA
Test the assumptions of correlation for gpa and final. Paste the SPSS histogram output for each variable and discuss your visual interpretations. Paste SPSS descriptives output showing skewness and kurtosis values and interpret them. Paste SPSS scatter plot output with gpa set to the horizontal axis and final set to the vertical axis. Conduct a visual inspection of the scatter plot to analyze other assumptions of correlation. Summarize whether or not the assumptions of correlation are met.

Step 3: Write Section 3 of the DAA
Specify a research question related to gpa and final. Articulate the null hypothesis and alternative hypothesis. Specify your alpha level.

Step 4: Write Section 4 of the DAA
Paste the SPSS output of the intercorrelation matrix for all specified variables.

First, report the lowest magnitude correlation in the intercorrelation matrix, including degrees of freedom, correlation coefficient, p value, and effect size. Interpret the effect size. Specify whether or not to reject the null hypothesis for this correlation.
Second, report the highest magnitude correlation in the intercorrelation matrix, including degrees of freedom, correlation coefficient, p value, and effect size. Interpret the effect size. Specify whether or not to reject the null hypothesis for this correlation.
Third, report the correlation between gpa and final, including degrees of freedom, correlation coefficient, p value, and effect size. Interpret the effect size. Analyze the correlation in terms of the null hypothesis.
Step 5: Write Section 5 of the DAA
Discuss the implications of this correlation as it relates to the research question. Conclude with an analysis of the strengths and limitations of correlational analysis.

Reference
Anderson, C. A., & Bushman, B. J. (2001). Effects of violent video games on aggressive behavior, aggressive cognition, aggressive affect, physiological arousal, and prosocial behavior: A meta-analytic review of the scientific literature. Psychological Science, 12(5), 353–359.

Questions and Topics We Can Help You To Answer:
Paper Instructions:

The administrators and parents at the school that you teach at are questioning why you are incorporating quantitative reasoning into the curriculum of your third grade classroom. They have asked you to prepare a persuasive research paper that supports and argues for why the instruction of quantitative reasoning is important. 
Your paper should consist of the following sections:
Introduction/Motivation: This is where you introduce to the reader what QR is (make sure you are clear in defining quantitative reasoning!) and how it comes up throughout daily life. Try to help the reader feel a personal connection to QR by showing them how they use it (perhaps without realizing it) in their daily life. You can also draw on some articles that speak about the quantitative literacy gap and how it is a key member holding students back from future success in mathematics (and possibly other disciplinary fields?). Be as creative as you want in this section; let your personality help the reader feel comfortable and relate to the argument you are presenting. This section should be about 1-2 paragraphs. 
Summary of articles/argument: This is where you bring in the findings from some of the readings and research you have done. Make your argument for how and why QR should be presented in the classroom. Then use some of the readings (either from the list or from your own searches) to back up your claim. Remember, you are trying to convince the administration and the parents that what you are doing in the classroom is beneficial to their students. This should be 2-3 paragraphs. 
Conclusion: One final section that summarizes what you have already said. This is where you lay your last words and do the best you can to create an unbeatable argument in defense of QR at the elementary levels. Again, relate to the human you are addressing (keeping in mind your audience) and try to convince them that quantitative reasoning skills are best introduced at a younger age and can prove to help students for years to come. One paragraph should be enough for this section.
It may seem odd to have a paper assigned as a project in a mathematics course, however, it is important to be able to defend what you are teaching and why you are teaching it. This assignment will hopefully expose you to articles and ways of viewing quantitative reasoning that will stick with you long after this semester is over. I am not as concerned about who has the most eloquent writing (this is a math class after all), but rather I want to see how you can convince the reader that you are aware of the deeper implications of certain topics and lessons that are presented in the elementary classroom. 

I will be grading using the following rubric:
Organization (20 points)
Clarity of argument (30 points)



Links she would like us to use: (not sure if you will have access)

https://blackboard.sdsu.edu/bbcswebdav/pid-5453404-dt-content-rid-108751032_1/courses/MATH313-02-Spring2020/Falkner_Foundations.pdf

https://blackboard.sdsu.edu/bbcswebdav/pid-5453406-dt-content-rid-108751037_1/courses/MATH313-02-Spring2020/Philipp.pdf

Questions and Topics We Can Help You To Answer:
Paper Instructions:

Choose an article with statistics that relate to your life.  Reflect on how these stats represent you or not.


-The article should have relevant stats (find an article that is current; something in the last couple of years)


-You should cite the article somewhere in your reflection 


-Times New Roman, 12, double spaced, APA or MLA (I prefer APA)


Example:


-Article states that 20% of the world hates the color purple and that is your favorite color.  I would then make an argument that the color purple is actually popular because then 80% of the world loves the color.  I would then discuss how that makes me part of a majority. (This is a very elementary example, but I hope it gives you some guidance).

Questions and Topics We Can Help You To Answer:
Paper Instructions:

Hypothesis tests are a powerful tool, with a solid mathematical foundation. However, criticisms of hypothesis testing abound.

For example, one criticism of hypothesis testing is that statistical significance is often confused with scientific significance. Suppose we run this t-test: H0: mean IQ of Boston residents = mean IQ of NYC residents, Ha: they are not equal. Say we get a very small p-value and conclude that residents from Boston and NYC have different IQ's, on average. We then proceed to actually check the two different sample means: mean Boston IQ = 125.3 and mean NYC IQ = 125.2. While our data finds that 0.1 difference *statistically* significant, it's not *scientifically* significant. That 0.1 IQ difference is definitely there, but it's too small for us to notice in our every day lives. For all intents and purposes, Bostonians and New Yorkers have the same mean IQ. Our t-test does not address the magnitude of the difference, just whether a difference exists.

There are many other criticisms of hypothesis testing. Please pick one (different from the example I gave; I encourage you to Google "criticisms of hypothesis testing") and discuss an example of it. You can either make up your own example or find someone else's example on the internet. If you do the latter, be sure to cite references. Then comment on two of your classmate's posts. Either suggest how you would rectify the situation, or discuss why the situation can't be rectified i.e. why a hypothesis test isn't the correct tool for the job.

Questions and Topics We Can Help You To Answer:
Paper Instructions:

The research firm LL Research collected data from 200 client businesses. They want to determine how the businesses compare among four variables:

2015 Profit in millions of dollars
2016 Profit in millions of dollars
2015-2016 Two-Year Change in Daily Average Customer Visits

Two-Year Average Number of Employees

Data collected for the sample of 200 businesses is contained in the file named Businesses, linked at the bottom of the page. Be sure to use all 200 data points.

Managerial Report
Prepare a report (see below) using the numerical methods of descriptive statistics presented in this module to learn how each of the variables contributes to the success of a client’s business. Be sure to include the following three items in your report.

    Compute descriptive sample statistics (mean, median, two quartiles Q1 and Q3 (using QUARTILE.EXC), minimum, maximum, range, interquartile range, sample standard deviation, and coefficient of variation) for each of the four variables along with an explanation of what the descriptive statistics tell us about the client businesses.

    In this case, which measure of central tendency would be best for this application? Explain why.
    Which measure of variation would be best for this application? Explain why.
    How can one use the above descriptive statistics to understand the businesses better?
    Which graphical displays of data would you use to help understand or complement the above descriptive statistics? Explain how and why.

Note: QUARTILE.EXC works only for Excel 2010 or newer.

    Compute the percent change in profit from 2015 to 2016 for each business.

    What would the percent change tell us in this application?
    Then use the z-score to determine which businesses were outliers with respect to percent change in profit.
    How would identifying outliers be useful in this application?
    What advice might you give to businesses that are outliers?
    How else would you determine outliers?

    Compute the sample correlation coefficient, showing the relationship between percent change in profit and each of the other two variables (2015-2016 Two-Year Change in Daily Average Customer Visits and Two-Year Average Number of Employees).

    Explain what the correlation coefficients tell us about the three pairs of relationships. Use tables, charts, or graphs to support your conclusions. 

Write a report that adheres to the formatting and APA expectations outlined on the Citing & APA Resources page (Links to an external site.) in the CSU-Global Writing Center. As with all written assignments at CSU-Global, you should have in-text citations and a reference page.                                                                    

Submit your Excel file in addition to your report.

Requirements:

    The paper must be written in third person.
    Your paper should be four to five pages in length (counting the title page and references page) and cite and integrate at least one credible outside source. The CSU-Global Library (Links to an external site.) is a great place to find resources.
    Include a title page, introduction, body, conclusion, and a reference page.
    The introduction should describe or summarize the topic or problem. It might discuss the importance of the topic or how it affects you or society as a whole, or it might discuss or describe the unique terminology associated with the topic.
    The body of your paper should answer the questions posed in the problem. Explain how you approached and answered the question or solved the problem, and, for each question, show all steps involved. Be sure the Word document is in paragraph format, not numbered answers like a homework assignment.
    The conclusion should summarize your thoughts about what you have determined from the data and your analysis, often with a broader personal or societal perspective in mind. Nothing new should be introduced in the conclusion that was not previously discussed in the body paragraphs. Your conclusion should emanate (be aligned with) your findings.

Questions and Topics We Can Help You To Answer:
Paper Instructions:

You will create and give a presentation on your lesson plan. Your instructor will provide suggested lesson plan topics.

Area, Pythagorean Theorem, and Volume

Include the following:

Overview:Write an introduction to the class activity. Include the purpose of the activity and desired outcome.
Objectives:The objectives should be specific and measurable.
Time: How long will the activity take when implemented in the classroom?
Materials: Describe any materials that are needed to conduct the lesson.
Activity:Provide a detailed description of the activity. Write all steps from the instruction of the assessment.
Presentation:Complete a PowerPoint presentation to be used in class to teach the lesson plan.
Notes:The PowerPoint should include presentation notes.
Your lesson plan may be in any form approved by the instructor.

Questions and Topics We Can Help You To Answer:
Paper Instructions:
* Do you find the use of college rankings like "US News" are useful in comparing universities? Why or why not?
* Why did you choose to attend CSUMB? Were you influenced by rankings or reputation? (Did you look up CSUMB's US News Rankings?) Discuss.
* What does O'Neil claim is the "spectacular failure" of the U.S. News college ranking? Do you agree or disagree? Why?
* How does the current state of college rankings and admissions standards promote inequity and/or equity in our country? Share your own point of view.

Questions and Topics We Can Help You To Answer:
Paper Instructions:

Choose a gambling game. Describe it, and find the expected value of playing the game. Is it fair? Why or why not?