Experiencing Statistics Assignments (ESA's)
(Last updated February 9, 2018)
Contents  
ESA 8  
ESA 10 
Due dates for ESA's are shown on the class calendar for your section. (If an ESA is shown on a calendar date, that is the date it is due.) 
You may always turn in ESA's before the due date. 
ESA's are due on the dates shown whether or not Dr. Gurney asks for them in class. 
With the exception of ESA 1, if you do not turn in an ESA by the due date, it will lose a point for each class period that starts after the due date. 
If you are unable to turn in an ESA in class on the due date, you may turn it in at Dr. Gurney's office before the next meeting of this class without losing points for being late. 
All graphs that are required for an ESA should be created using a statistical software program like Minitab, SPSS or R. 
Write each ESA as if the reader will be one of your classmates who has not done that ESA and has no access to the assignment material. 
When you submit an ESA for grading, you will either receive a numeric score for the ESA or you will be asked to "REDO" the ESA. 
If an ESA submission is missing requested information, has a major error or enough minor errors  but it is still worth possible points  you will be asked to "REDO" it. 
A "REDO" will be marked by the word "REDO" and a date  instead of a numeric score  at the start of the ESA. 
To "REDO" an ESA, you just need to correct any errors or add any missing information. You DO NOT need to rewrite or reprint the entire ESA. 
If you are asked to "REDO" an ESA, you have two class periods from the date written next to "REDO" to make the needed changes to the ESA. If you turn in a "REDO" more than two classes after the date shown by the "REDO", you lose a point for each class period that has started after the first two classes. 
ESA’s that you are asked to "REDO" are worth no points until a corrected version has been resubmitted and assigned a numeric score. 
If you receive a numeric score for an ESA, you cannot rework that ESA for more points. 
If you are asked to "REDO" an ESA, it may still receive the full 5 points. 
You must receive points for ESA 1 before you can receive points for many of the other ESA's. 
Whether you must "REDO" an ESA or are trying to finish ESA's 10 through 12, the last possible day to turn in ESA's for points is the last Thursday before finals week. 
With the exceptions of ESA 1 and ESA 2, you may work ESA's either by hand or with computer software, but your work must be submitted on paper. 
Turning in ESA’s on time and receiving some points is better than turning them in late and receiving no points. 
ESA Descriptions: Each of the twelve ESA’s is described below.
ESA 1: Hand Measurements and Favorite Colors For each of 32 adult residents of the United States, do the following. One of the adult residents should be yourself.
Make your measurements to the nearest tenth of a centimeter. Put the information you gather in an Excel spreadsheet with column headings "Subject Number", "Left Hand Span", "Right Hand Span", "Right Thumb Length", and "Favorite Color". Mark the row containing the measurements you made on yourself by using bold, italicized font. NOTE: If you measure correctly, most of the digits from 0 to 9 should appear to the right of the decimal point for each of your handspan measurements, and also for your thumb length measurements. Also, the left hand span is usually NOT the same as the right hand span for a subject. You must receive credit for ESA 1 before you can receive credit for many of the other ESA’s. Once you receive credit for ESA 1, you should make a copy of it and keep the copy in a safe place, because you will need the information from ESA 1 for many of the following ESA's.


For this ESA, you will choose your semester project data set. Your data set should contain three columns of quantitative data, and one column of qualitative data, and there should be at least 32 items in each column. Two of the quantitative variables should have similar values. That is most of the values of both these variables should be about the same size, and some of the values of the first variable should be smaller than the corresponding values of the second variable, while some of the values of the first variable should be larger than the corresponding values of the second variable. These two variables will be your comparable variables. Examples would be a student's grade point average for two different semesters, or the number of points a basketball player scores in two different seasons. The third quantitative variable can be almost anything, but it must be different than the first two. Use at most four significant digits for your quanitative data. If a variable has large values, you may need to record the values in thousands, or millions, or billions, etc. The qualitative variable should have at least four possible values. When using weather data, the outlook is qualitative and four possible values are sunny, rainy, cloudy, and thunderstorms. When looking at movies, the producers are qualitative and four possible values are Paramount, 20th Century Fox, Dreamworks, and Screen Gems. Some examples of qualitative and quantitative varibles are available in the Qualitative vs Quantitative section of this website. Examples of acceptable project data sets are also found in the Semester Project Information section of this website. You MAY NOT use the data sets in the textbook, or the sample project data sets or data from the sample semester project on the website, and you may not use ESA 1 data for ESA 2. You may, however, use data that is similar to these data sets. You may use data from newspapers, magazines, scholarly journals or the internet. Books are not allowed. You will need to state the source of your data. For newspapers, magazines and journals, include the title, the issue of the publication (volume and issue number or date published), and the page numbers. For websites, include the name of the website, the web address, and the date you obtained the data from the website (This is the date the data was accessed). Be specific when referencing websites, if your data is not on the homepage of the website, then you will need to reference the subpage of the website where you found your data. You should usually have between one and four sources for your data. Usually, you can find all the values for one variable in a single location. Sometimes all the data needed can be found in one location, but most students need to use two or more sources to obtain all the necessary data. You may use search engines like Google to find data on the internet, but your data source or sources will have to be the website or websites that result from your searches. You will need to create a single table containing all of your data. If you cannot fit all of your data on one or two pages, you are doing something very wrong. A complete ESA 2 will contain this table along with the sources of information for your data.
The following are general descriptions of some data sets you could use.
A blank row or column element WILL NOT count as one of the 32 items you need for your variables. Use sources of data that are less than six months old. I do like to encourage creativity, so if you come up with a data source unlike anything I have seen before, I will give you up to 4 bonus points for originality. If you have a data set you would like to use that does not fit all the requirements given above, check with me. I will either tell you how to adapt the data to the project, or give you permission to use your data set as is. If you have not received credit for ESA 2 by the last day to drop for this semester, you will receive no points for ESA 2 and I will choose your semester project data for you. If you want to change your project data after you receive points for ESA 2 or have been assigned a data set by me, you will need my approval. If you change your data without my approval and your data set does not meet the criteria given above, you will lose 10 points from your semester project score. There will be no change to your ESA 2 score if you change your data set after ESA 2 has received a numeric score. 

ESA 3: Qualitative Frequency Distribution and Bar Chart Do the following with the favorite color data obtained in ESA 1.


ESA 4: Quantitative Frequency Distribution and Histogram Using the right thumb length data from ESA 1, do the following.
See the Quantitative Frequency Distribution and Histogram section of this website for information on the various aspects of this ESA. 

Create and fill in a table like the following using your left handspan data, your right handspan data, and your right thumb length data.


ESA 6: Paired Horizontal Boxplots Using the left hand span and right hand span data you obtained in ESA 1, do the following.
See the Comparing Boxplots section of this website for information on the various aspects of this ESA. 

Using a statistical software program like Minitab, SPSS or R, make a scatter diagram of your right hand span and right thumb length data showing the regression line. Your scatter diagram should have (i) a descriptive title, (ii) a subtitle saying that you created the scatter diagram, and (iii) descriptive labels for both axes giving the units of measure for each axis. State the equation of the regression line and the value of Rsquare for your scatter diagram. Write a paragraph which contains the following information about your scatter diagram.


First obtain a thumb tack. The two figures below illustrate a thumb tack from two different perspectives. Dr. Gurney usually carries thumb tacks in his book bag. Next, find a smooth level surface like a table top or a linoleum floor, and do the following.
See the Die Toss section of this website for an experiment with data recorded in a similar table and a similar plot of percent versus toss number. WARNING: DO NOT use a push pin for this ESA. A push pin is not a thumb tack. The figures below illustrate a push pin from two different perspectives. 

In this ESA, you will be "curving" the Test 2 scores from your class. You will find the Test 2 score distribution on Moodle in the same location as the syllabus and the chapter notes. See the Curving Grades section of this website for examples of how to curve test scores.


ESA 10: Confidence Interval for Mean Use your right thumb length statistics from ESA 5 to find and state the 95% confidence interval for the population mean of right thumb lengths for adult residents of the United States. State the mean and standard deviation of your right thumb length measurements. Finally, give an interpretation of your confidence interval. 

ESA 11: Sample Size for Proportion Use your favorite color data from ESA 1 to find the minimum sample size needed to estimate the proportion of adult residents of the United States whose favorite color is blue to within 4 percentage points at 95% confidence. State the formula you use and the values of any quantities you needed to do this computation. 

ESA 12: Hypothesis Test of the Mean Use your right thumb length statistics from ESA 5 to test the claim that the population mean for right thumb lengths of adult residents in the United States is more than 5.5 cm at 95% confidence. State the mean and standard deviation of your thumb length measurements, the test statistic, and the Pvalue. Finally, state your conclusion in English. 