Here are the requirements for the project term paper:

Here are the requirements for the project term paper:

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Inferential Statistics

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1) You will need some statistical plots and tables to display all or part of your data. For example, you could get the means or percentages for each subcategory. Other statistics such as the subtotals and standard deviations would also be useful. Organizing a table in a logical way leads to readability and improved communication. You could use these tables to construct a pie chart that has the slices of the pie totaling to 100%. Or you could pick any variable and make a FREQUENCY TABLE, and then make a histogram from that. Please review the connection between histograms and frequency tables! Another plot that might be useful if you have category data, is to make a bar chart showing the frequency in each category. Also, maybe the MEAN per day from each category (ie men vs women). if you have bivariate data (such as 100 salmon with length and weight of each) then this is a plot that should really be done: plotting the weight versus the length in a scatter plot. It is a point for each salmon with length and weight as the XY coordinates like in Algebra. (Look it up to see what that looks like!)

2) Use INFERENTIAL STATISTICS methods from chap 8 onwards to analyze your data (required). You could get CONFIDENCE INTERVALS (look-ahead) to look at the mean of each variable. You could use HYPOTHESIS tests for any pair of variable means to see which is significantly bigger. You can compute the correlation coefficients for any pair of variables. If needed you can get the linear regression model between a pair of variables.

3) Write a term paper (required) about your project using the plots, summary tables and results of the inferential statistics.

Some warnings: Do not paste your entire data set into the project term paper. This rookie move is clearly designed to fill up space with a phonebook like spread of unreadable data. The idea is to SUMMARIZE your data set with tables and plots which are required.

Histograms require a frequency table first. The idea for the rectangles is that they summarize a bunch of data points in a category. That’s what the frequency table does for you, it makes the categories. If you skip that step then you get a single rectangle for each data point. What this looks like is lots and lots of skinny rectangles each representing a single data value. If it looks like that, I know immediately that you have skipped the frequency table step which is where things get grouped together in categories.

Label your axis. Use things like variable names and units of measurement.

Don’t display un-needed digits. Instead of $1.75083548451232 just present that as $1.75. Otherwise you look like a schmuck. A similar comment applies to axis labels on plots: instead of 45,000 use 45, and then on the label, you say it’s in ‘thousands’.

Must use methods from Inferential statistics (Ch8-12). Look ahead in the ebook to see what those look like. Most people will use confidence intervals and hypothesis tests. If you have bivariate data then linear regression is a likely approach. There are also chi-square tests and non-parametric tests.

Applying the wrong method to your data type. There are methods for MEANS and other methods for PROPORTIONS (percentages). Using a method for MEANS on a PROPORTION is meaningless. And impossible. And confusing. Look for those key words using any method.

Must be a term paper. If you turn in a spreadsheet only, then we are headed towards an F on the required term paper. Any analysis does need an introduction, background information, description of results, discussions, interpretations, and summary. A spreadsheet doesn’t do that by itself.

If your plots are messed up (ie that No Frequency Table Histogram mistake, or no labels on the axis) you will get a bunch of points off and you paste in your entire data set. If you don’t round off numbers then that’s worth a few points.

Here is a way that many people fail: NOT DOING ANY INFERENTIAL STATISTICS. Grammar and Spelling: WORD provides automatic spell check and grammar.

Misusing statistical words and phrases. Words such as CORRELATION have a very specific definition. Same for SIGNIFICANT. You can’t just go around saying those words for no reason. You would need to have some inferential statistics to back up those words!

This is an odd one: Some people go thru the trouble of conducting a hypothesis test procedure (which answers yes/no type of questions) and do all the statistics correctly and then conclude the exact opposite of what their statistics just showed. I take this as evidence that the person has no idea what they just did. I have to consider this as a fairly egregious mistake worth a bunch of points.

Here are the steps to get started:

1) Locate a data set of your choice. People find data at work, their hobbies, family life, their favorite sports etc. Typically 20-1000 data values. Maybe two, three or four variables. Example might be: 100 Salmon, Length and Weight of each, location caught, gear type, sex etc.

There are oodles of existing data sets on the web that you could use.

ALTERNATIVE: Conduct a survey on any topics of your choice. For this you need 10 questions or so and about 30-100 people to respond.

You could ask questions about any issue that interests you.

Another alternative: Use data from a previous course such as chemistry or ecology (DATA must already exist because we are using it right away!) Another alternative is probability experiments using dice cards or spinners. This would look like chapters 4&5&6.

Things that are not data sets: Tables of numbers displayed in somebody else’s report. If you find an article relevant to your project you can describe it in your introduction or discussion. But the tables of values in such reports are not data. They are summaries of data, which the author has already done. I am asking you do stuff like that with your own raw data.

Types of data that students have had trouble with and which I now exclude: Time Series data (ie stock prices). We just don’t have the tools for it in STAT200. Weather data, or other attempts to prove or disprove global warming. Same reason. We lack the tools. Global Warming is a real thing, it’s just that you can’t use local data from a few days to go after a global phenomenon lasting centuries. It is an issue of feasibility.

Also not allowed: Anything that would result in a Nobel prize or a Phd. We simply don’t have time for such huge undertakings. This thing has to be cooked and eaten by early May. Keep it simple and get it done. A small compact term paper that hits all the highlights is going to get a better grade than a huge scatter brained project, with a brilliant idea behind it, but sadly not finished.

2) Send me your data in EXCEL, along with a description of what you are thinking about. If you are planning a survey, I will need to see detailed plans, such as a list of questions, and how you will get the responses.

3) Conduct your analysis including summary tables, plots, and especially inferential statistics. For more information on Inferential Statistics check out : https://www.britannica.com/science/inference-statistics

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