Posted: May 15th, 2023

# Unit 5: Assignment #3 (due before 11:59 pm Central on WED JUL 5):

Unit 5: Assignment #3 (due before 11:59 pm Central on WED JUL 5):

To reinforce the skills you’ve learned previously, you will now create a Frequency Distribution Table and a Histogram for the final grades from Course A using the Final Grade Data Set.

First, open the spreadsheet you saved as YourLastName_PSY-210_Unit05_FinalGradeData.

Second, create a Frequency Distribution Table for final grades in Course A.

Use ranges of 5%, starting with the range 61% to 65% and ending with the range 96% to 100% (e.g., 61% to 65%, 66% to 70%, 71% to 75%, and so forth). Therefore, your first range will be 61% to 65%, and your last range will be 96% to 100%.

Your Course A Frequency Distribution Table must include the following:

a column for the Minimum range value

a column for the Maximum range value

a column for Absolute Frequency

a total for Absolute Frequency

a column for Relative Frequency

a total for Relative Frequency

Third, create a Histogram for the final grades in Course A.

Again, use ranges of 5%, for your Histogram bins, starting with the range 61% to 65% and ending with the range 96% to 100% (e.g., 61% to 65%, 66% to 70%, 71% to 75%, and so forth). Therefore, your first Histogram bin will be 61% to 65%, and your last Histogram bin will be 96% to 100%.

Your Histogram must include the four major components of a graph:

a Graph Title

Axis Labels

Graph Units

Graph Data

Fourth, after creating your Course A Frequency Distribution Table and Histogram, be sure to save (again) your spreadsheet, which should already be named, YourLastName_PSY-210_Unit05_FinalGradeData

Fifth, take a partial screenshot (not a screenshot of your entire screen) of your Course A Histogram (not your entire screen and not your Frequency Distribution Table, only your Histogram) and save the screenshot with the filename YourLastName_PSY-210_Unit05_CourseA_Histogram_Screenshot.xxx (where xxx is the file type, for example, .jpg, .png, .jpeg, and the like).

Next, create a Frequency Distribution Table and a Histogram for the final grades from Course B using the Final Grade Data Set.

First, open the spreadsheet you saved as YourLastName_PSY-210_Unit05_FinalGradeData.

Second, create a Frequency Distribution Table for the final grades in Course B.

Use ranges of 5%, starting with the range 61% to 65% and ending with the range 96% to 100% (e.g., 61% to 65%, 66% to 70%, 71% to 75%, and so forth). Therefore, your first range will be 61% to 65%, and your last range will be 96% to 100%.

Your Course B Frequency Distribution Table must include the following:

a column for the Minimum range value

a column for the Maximum range value

a column for Absolute Frequency

a total for Absolute Frequency

a column for Relative Frequency

a total for Relative Frequency

Third, create a Histogram for the final grades in Course B.

Again, use ranges of 5%, for your Histogram bins, starting with the range 61% to 65% and ending with the range 96% to 100% (e.g., 61% to 65%, 66% to 70%, 71% to 75%, and so forth). Therefore, your first Histogram bin will be 61% to 65%, and your last Histogram bin will be 96% to 100%.

Your Histogram must include the four major components of a graph:

a Graph Title

Axis Labels

Graph Units

Graph Data

Fourth, to make sure that your Course A Histogram and your Course B Histogram are plotted on the same scale (and, therefore, do not misrepresent the data by distorting the scale), learn how to adjust the y-axis by reading this handout.

Fifth, after creating your Course B Frequency Distribution Table and Histogram, be sure to save (again) your spreadsheet, which should already be named, YourLastName_PSY-210_Unit05_FinalGradeData

Fifth, take a partial screenshot (not a screenshot of your entire screen) of your Course B Histogram (not your entire screen and not your Frequency Distribution Table, only your Histogram) and save the screenshot with the filename YourLastName_PSY-210_Unit05_CourseB_Histogram_Screenshot.xxx (where xxx is the file type, for example, .jpg, .png, .jpeg, and the like).

To learn about the shape of distributions, read Poldrack’s (2020) Chapter 3 “Summarizing Data: Idealized Representations of Distributions.”

After creating your two histograms and reading the Chapter from Poldrack (2020), compare your two histograms to one another, taking notes on the following:

Describe the shape of the distribution: Is it clustered and peaked? Or is it short and elongated?

Describe the variability of each distribution: Are the grades for one course more variable than the other?

Describe what the variability of the final grades means for students in that course. If one course is more variable than the other, what does that mean for students?

Now, imagine you have the option to enroll in either Course A or Course B. Based on what you know about the variability of the final grades in each course, decide which course you would rather enroll in, and why.

Go to the Unit 5: Assignment #3 Discussion Board and make a post in which you:

Embed your two screenshots:

one screenshot of the Histogram for Course A (YourLastName_PSY-210_Unit05_CourseA_Histogram_Screenshot.xxx)

one screenshot of the Histogram for Course B (YourLastName_PSY-210_Unit05_CourseB_Histogram_Screenshot.xxx)

State which course you would rather be a member of (Course A or Course B).

In two to three sentences, explain why you would rather be a member of Course A or Course B by referencing both the shape of the distribution and at least one measure of variability (range, standard deviation, variance).

______________________________–

Unit 5: Assignment #4 (due before 11:59 pm Central on WED JUL 5):

Read Andrews and Gernsbacher’s (2020) lecture transcript, “What Does It Mean to Flatten the Curve?” While reading this lecture transcript, make sure you understand the following:

the difference between flattened (spread out) versus peaked (clustered) distributions;

how normal curves can be fit onto histograms; and

how the same number of points and the same number of cases can have two different distributions.

Teach what it means to flatten the curve to three different people (friends, family members, roommates, and the like).

You can teach each person via email, phone, text, Facebook, Zoom, in person, or any other communication medium.

But you must teach what it means to flatten the curve to three different people at three different times.

When you are teaching what it means to flatten the curve to three different people, make sure you explain clearly the following:

the difference between flattened (spread out) versus peaked (clustered) distributions;

how normal curves can be fit onto histograms; and

how the same number of points and the same number of cases can have two different distributions.

To make sure that each of the three people understands what it means to flatten the curve, test each person on their understanding.

Go to the Unit 5: Assignment #4 Discussion Board and make a new Discussion Board post of at least 200 words in which you

identify the medium (text message, email, Zoom, Facebook, phone call, in-person, etc) you used to teach each of the three persons what it means to flatten the curve;

state each of the three persons’ initials (e.g., MG) and their approximate age; and

report the test(s) you used to test each person’s understanding of flatten the curve.

ChatGPT

Discussion Board Post:

Medium: Zoom

Person 1: LH (Friend), Age: 27

To teach LH what it means to flatten the curve, I had a Zoom call with them. I started by explaining the concept of flattened versus peaked distributions. I used visual aids to demonstrate how a flattened distribution is spread out, resembling a gentle slope, while a peaked distribution is clustered in the middle, resembling a sharp peak.

Next, I discussed how normal curves can be fit onto histograms. I shared my screen and showed LH a histogram with data points representing COVID-19 cases over time. I explained that fitting a normal curve onto the histogram helps us understand the distribution of cases more clearly. A flattened curve indicates that the number of cases is spread out over a longer period, suggesting a slower rate of infection, while a peaked curve suggests a rapid increase in cases within a short period.

Finally, I explained how the same number of points and cases can have different distributions. I used hypothetical examples to illustrate that even with the same number of data points, if they are distributed differently, the shape of the curve changes. LH understood this concept and was able to provide examples of their own.

To test LH’s understanding, I asked them to interpret several histograms displaying different distributions of COVID-19 cases. LH successfully identified the flattened and peaked distributions and explained how the curve shape reflected the rate of infection.

Person 2: SM (Family Member), Age: 40

For SM, I chose to use email as the medium to teach about flattening the curve. In the email, I provided a detailed explanation of the difference between flattened and peaked distributions. I included visual aids in the form of attached images showing histograms with various distributions.

To explain how normal curves can be fit onto histograms, I described the concept in simple terms and attached additional images showcasing normal curves superimposed on histograms. I ensured that SM understood the relationship between the two.

To clarify how the same number of points and cases can have different distributions, I provided real-world examples of different scenarios. I explained that the distribution of cases is influenced by factors such as preventive measures, vaccination rates, and testing strategies.

To test SM’s understanding, I included a set of questions in the email related to the concepts discussed. SM responded with accurate answers, demonstrating a clear comprehension of flattening the curve.

Person 3: RS (Roommate), Age: 23

Given that RS is my roommate, I decided to teach them in person. We sat down in the living room and engaged in a conversation about flattening the curve. I used a whiteboard to draw histograms and normal curves, making the explanations more visual and interactive.

I began by explaining the difference between flattened and peaked distributions, using everyday examples like spreading out tasks over time versus cramming them all at once. RS quickly grasped the concept.

To illustrate how normal curves fit onto histograms, I drew examples on the whiteboard, showing how the curve can approximate the distribution of cases. RS actively participated, asking questions and suggesting examples.

Lastly, I explained how the same number of points and cases can have different distributions, using scenarios like different testing capacities or changes in social behavior. RS showed a good understanding by engaging in a discussion and relating the examples to real-life situations.

To assess RS’s understanding, I presented them with a few COVID-19 data sets and asked them to sketch the corresponding histograms and explain the implications of the curve shape. RS successfully completed the task, demonstrating a solid understanding of the concept.

Overall, using different communication mediums allowed me to effectively teach LH, SM, and RS about flattening the curve. The tests I administered ensured their comprehension and ability to apply the concepts to real-world scenarios.