Project should cover steps below:

Step 1: The Abstract: The purpose of any statistical project is to add to a new or established body of learning through experiments, data, and research. As important as the numbers themselves may be, they are of little worth if you cannot put them in the proper context and communicate their significance to others. As its name suggests, the abstract is a very short, very general paragraph or two that summarizes all the important elements of your report, such as the research methods, results, and final analysis.

Step 2: The Introduction: Begin with a purpose statement, which explains why you decided to pursue the report. You must also talk about your expectations for the project before you began it and what your original hypothesis was, if necessary. Once again, you should be clear and concise and use language that the layperson can easily understand.

Step 3: Discuss the Methods: Shifting to a more scientific mode, the methods section gives you the opportunity to explain the experiments you used to collect the data that will either prove or disprove your initial hypothesis. You should be as specific as possible in this section and talk about data collection, results tracking, and any adjustments that were made throughout the project. Make sure that you include resources, materials, or software you used in your research, and justify your work analytically and graphically where possible. You must show all your detailed calculations (where possible)

Step 4: Compile the Results Section: Anything of consequence that you discovered during your experimentation and research must be included in this section. Fact, results, findings, and measurements, whether in numeric, chart, or graph form should be added. It is also important to note that you should not editorialize or analyze any results in this section of your statistical project. This section is simply about giving readers the numbers they need to either agree or disagree with your main point.

Step 5: Make Your Conclusions: Now that they have seen the numbers, you must let your reader know what they mean to you. Your analysis should be dispassionate and should include results that help corroborate your hypothesis, and those that may negate it. Explain to the reader what impact, if any, these findings may have on subject, field of science, or even on the average person.

PART I: Data Collection 1. Select a topic from the list of available project ideas or you may come up with your own idea. Note that if you choose to use your own topic idea, it should be approved by the instructor before you begin any work for the project. Make sure to choose a topic where you can collect QUANTITATIVE DATA. 2. do a survey question that can be asked in your sample to get the results necessary to complete the tasks of this project. 3. Collect at least 50 data points and list the results into a chart. This chart can be done using any word processing program. The chart should include labels for the measure of unit. 4. Organize the data into a frequency distribution with 5-8 classes.

PART II: Data Tabulation 1. Calculate the mean and standard deviation using your TI-Graphing Calculator.

2. Use your TI-graphing calculator to find the 5-number summary for your data set. Either by hand or using Excel, do a box plot for your data.

An example of what your box plot should look like and how it should be labeled. Make sure to include a chart title and labels as seen below. Part III: Data Analysis

1. What type of method did you use to collect your sample? Describe your process in one to two sentencez. As a reminder, these are the types of sampling we covered in Hawkes Section 1.3 You can look in Hawkes Learn Slides 7-12 for further detail on each method. a. Simple random sample b. Cluster c. Systematic d. Stratified e. Convenience 2. Using the context of your survey question, restate the standard deviation and describe what it means. in 1-2 sentencez. 3. Describe your data using the vocabulary we have covered so far in this course. Is your data uniform, bimodal, skewed left, skewed right, symmetric? in 1-2 sentencez. 4. Does your data have any outliers? If yes, what are the outliers and why do you think you have them? If no, why not? in two-three sentencez 5. Are you median and mean different or the same and why? Which statistics best represents the center of your data and why? in three to four sentencez