A university wants to determine if students from different majors are equally likely to be cited for academic dishonesty. They average instances of documented cheating across 10 years for three different academic majors and find the following results: Business Administration (M = 5.1; N = 10), Fine Art (M = 1.9; N = 10), and Nursing (M = 3.2; N = 10). They also calculated the between-group and within-group sum of squares (SS_B = 301.6; SS_W =1343.8). Use a one-way ANOVA (where α = .01) to determine if there are significant differences in cheating rates by major.

1. Identify the alternative hypothesis (F tests are always nondirectional)
2. List your degrees of freedom (within)
3. List your degrees of freedom (between)
4. List your mean squares between groups
5. List your mean squares within groups
6. List your F test statistic
7. List your critical F value(s)
8. Are there statistically significant differences in cheating between majors? (Yes/No)
9. Calculate partial eta-squared (η²) for the effect of major on cheating rates (list as decimal, 0.xx)
10. Calculate Tukey’s HSD
11. Use Tukey’s HSD to determine if the difference in cheating rates between Business Administration majors and Fine Art    majors is statistically significant (Yes/No)

A random sample of 20 college professors is selected from all professors at a university. The following list gives their ages: 27, 52, 49, 63, 60, 57, 48, 53, 47, 42, 51, 54, 62, 60, 49, 58, 56, 47, 44, 49 [a] (1 point) What is the POPULATION in this problem? [b] (1 point) What is the SAMPLE? [c] (1 point) State whether the ages of professors are QUANTITATIVE or QUALITATIVE? [d] (1 point) If quantitative, is it DISCRETE or CONTINUOUS? [e] (6 points) Using the age sample data above, find the mean. median, mode, range. standard deviation and variance. Be sure to include the units in your answers. You do not need to show work. Round to 1 decimal place. [f] (2 points) Find the inter-quartile range of the age sample data. Show work. [g] (4 points) Find the "fences". Then use the fences to determine whether there are any outliers. Show all work. [h] (4 points) Draw a boxplot. Draw a boxplot. (Label the 5 number summary. Be sure to include outlier(s) if any.)

A random sample of 4 college students was drawn from a large university. Their ages are 22, 17, 23, and 20 years.

a) Test to determine if we can infer at the 5% significance level that the population mean is not equal to 20.

b) Interpret your conclusion.

From a survey of 120 students attending a university, it was found that 48 were living off campus, 57 were undergraduates, and 25 were undergraduates living off campus. One student is selected at random.
Let event A be “The student is an undergraduate”

Let event B be “The student is living off campus”.

a. Sketch the Venn diagram and show number of students for all parts of the diagram

b. Find the probability that selected person is an undergraduate student or he/she lives off campus

c. Find the probability that selected student is an undergraduate living on campus

d. Selected person is a graduate student living on campus.

The president of a University wishes to find the average age of students presently enrolled. From past studies, the standard deviation is known to be 2 years. A random sample of 50 students is selected and the mean is found to be 23.2 years.

A. Find the 95% confidence interval for the population mean.

B. Find the 99% confidence interval for the population mean?

. A teaching assistant for Statistics course as a university collected data from students in her class to investigate whether study time per week (average number of hours) differed between students who planned to go to graduate school and those who did not. The data were as follows:

Graduate school: 15, 7, 15, 10, 5, 5, 2, 3, 12, 16, 15, 37, 8, 14, 10, 18, 3, 25, 15, 5, 5

No graduate school: 6, 8, 15, 6, 5, 14, 10, 10, 12, 5.

State the null and alternative hypotheses using statistical notations. Use R command t.test to test whether study time differed between two groups. Include your R output and identify the test statistic and p-value. Draw the conclusion.

A group of high-school parents in Tucson, Arizona, in conjunction with faculty from the University of Arizona, claim that young women in the Tucson high schools not only are called on less frequently, but receive less time to interact with the instructor than do young men. They would like to see the school district hire a coordinator, spend money (and time) on faculty workshops, and offer young women classes on assertiveness and academic communication.

To make things simple, assume that instructor interactions with young men average 95 seconds, with standard deviation 35 seconds. (Treat this as population information.)

The null hypothesis will be that the average interaction time for young women will also be 95 seconds, as opposed to the alternate hypothesis that it is less, and will be tested at the 2.5% level of significance.

1. Give interpretations in context of Type I and Type II error in this situation. (Your discussion should not focus on “Null” and “Alternate”.)
2. What are the social, economic, and other consequences of (separately) Type I and Type II error?
3. Find the rejection region for this test. That is, what interaction time bounds the lower 2.5% of the distribution?
4. Assume the true mean interaction time for young women is 90 seconds. Find the power of the test.
5. Repeat part 4 for a true mean interaction time of 80 seconds.
6. What do the results in parts (4) and (5) mean in terms of your previous answers?

- Researchers from the University of Toronto Scarborough conducted two experiments that looked at the effect of two different types of motivational intervention on prejudice reduction. Summarize the two types of motivational intervention that were used in their experiments.

-If programs focusing on reducing prejudice are actually increasing prejudice, how should the issue of prejudice be addressed?

-Even though researchers now know that reducing prejudice needs to focus on motivational interventions that are more personal in nature, the authors suggest that controlling prejudice reaction practices are tempting. What benefits do controlling motivational interventions have for prejudice programs?

This comes from the Columbia University website: “As an equal opportunity and affirmative action employer, the University does not discriminate against or permit harassment of employees or applicants for employment on the basis of race, color, sex, gender (including gender identity and expression), pregnancy, religion, creed, national origin, age, alienage and citizenship, status as a perceived or actual victim of domestic violence, disability, marital status, sexual orientation, military status, partnership status, genetic predisposition or carrier status, arrest record, or any other legally protected status.”

1. Looking at this list of characteristics that Columbia doesn’t discriminate against, can you quickly put in your own words what each of them means, or are some ambiguous? If there is ambiguity, is that an ethical problem?
   
2. What’s the difference between unintentional and intentional discrimination?
   • Are some of these characteristics more vulnerable than others to unintentional discrimination? Which ones? Why?
   • Are some of these characteristics more vulnerable than others to intentional discrimination? Which ones? Why?

3. Which of the protected characteristics are concealable, meanings that in most cases a job applicant could fairly easily hide or not reveal whether he or she has the trait? Which aren’t so concealable?

Someone sent me this link to a talk by Prof. Klaus Schulten from the University of Illinois: (my emphasis)

Quantum Computing and Animal Navigation

Quantum computing is all the rage nowadays. But this type of computing may have been discovered and used by living cells billion of years ago. Nowadays migratory birds use a protein, Cryptochrome, which absorbs weak blue light to produce two quantum-entangled electrons in the protein, which by monitoring the earth's magnetic field, allows birds to navigate even in bad weather and wind conditions. The lecture tells the story of this discovery, starting with chemical test tube experiments and ending in the demonstration that the navigational compass is in the eyes and can be affected by radio antennas. The story involves theoretical physicists who got their first paper rejected as "garbage", million dollar laser experiments by physical chemists to measure the entangled electrons, and ornithologists who try to 'interrogate' the birds themselves. This work opens up the awesome possibility that room-temperature quantum mechanics may be crucial in many biological systems.

Now here's my question: What's the big deal with entangled electrons? I mean, if I do not neglect electron-electron interaction, then pretty much all electrons in a condensed matter system are entangled, are they not? Electrons in the same angular momentum multiplet are entangled via Hund's rule, electrons on neighboring sites in a tight-binding (or, in the interacting case, Hubbard) model can all be entangled due to an antiferromagnetic exchange coupling, etc. etc.

Sure, for a quantum computer I'd like to have physically separated electrons maintain their entanglement, and I'd like to have fine-grained control over which of the electrons are entangled in which way etc, but for chemical processes in molecules such as these earth-magnetic-field receptors, is it not a bit sensationalist to liken such a process to quantum computing?