1.I was looking at my notes on protein structure and I am trying to understand quaternary structures for proteins.
I understand that primary, secondary, and tertiary structures are encoded by one gene each. However, I am not entirely sure if quaternary structures are encoded by one or multiple different genes.
The reasons why I am a little confused is for two reasons. Firstly, quaternary structures are made up of more than one protein subunit (i.e. multiple polypeptides). Secondly, as I understand, Hemoglobin, for example, has different subunits, each of which is encoded by a different gene. Does this necessarily mean that all quaternary structures are composed of proteins encoded from different, separate genes?
If quaternary subunits are encoded by different, separate genes, can those different genes be located on different loci, or are all of the subunits necessarily encoded by the different gene but its mRNA molecule is spliced differently?
2.(1) The claim by a weight loss Company is that on average, the client will lose 10 pounds over
first 2 weeks. 50 people who joined the programme are sampled, their weight loss is 9 pounds with a standard deviation of 2.8 pounds. Can we conclude at the .05 level that a person joining the programme will lose less than 10 pounds?
(2) The following is a random sample of 90-day futures prices in dollars for 1 troy oz. of silver from The Wall Street Journal issues in May and June of 1997: 4.74, 4.77, 4.87, 4.91, 4.83, 4.72, 4.92, 4.86, 4.97, 4.71, 4.90, 4.93, 4.75, 4.88, 4.79, 4.83, 4.89.
a. Calculate the mean
c. Standard deviation of the 90-day future price of silver data
(3) A mining company needs to estimate the average amount of copper ore per ton mined. A random sample of 50 tons gives a sample mean of 146,75 pounds. The population standard deviation is assumed to be 35.2 pounds.
a. Give a 95% confidence interval for the average amount of copper in the population of tons mined.
b. Give a 90% confidence interval for the average amount of coper per ton
c. Give a 99% confidence interval for the average amount of coper per ton
(4) An e-commerce Website gets 2,385 visitors on a particular day. Among these, 1790 visitors explore the products by looking at more pages at the site. Among these 1790 visitors who explore the products, 387 make a purchase.
a. If a visitor chosen at random from all those who visited the site, what is the probability that the visitor explored the products
b. If a visitor is chosen at random from all those who visited the site, what is the probability that the visitor made a purchase.
c. If a visitor is chosen at random from all those who explored the products, what is the probability that the visitor made a purchase.
d. Which of the preceding three probabilities is relevant to the design of the home page that leads to product page.
3.1) (Ch. 7) Explain what a residual is (also known as residual of prediction).
e idea of “least squares” in regression (you need to fully read pp. 200-208 to understand).
3) What does it mean if b = 0?
4) What does it mean when r-squared is 0? What does it mean when r-squared is 1?
5) What is the difference in an unstandardized regression coefficient and the standardized regression coefficient?
6) If a report says test performance was predicted by number of cups of coffee (b = .94), what does the .94 mean? Interpret this. (For every one unit increase in ___,There is an increase in ___ )
7) If F (2,344) = 340.2, p < .001, then what is this saying in general about the regression model? (see p. 217)
8) Why should you be cautious in using unstandardized beta? (p. 218)
9) (Ch. 8) Explain partial correlation in your own words. In your explanation, explain how it is different from zero-order correlation (aka Pearson r).
10) (Ch. 9) What is the F statistic used to determine in multiple regression?
11) What is F when the null hypothesis is true?
12) In Table 9.4, which variable(s) are statistically significant predictors?
13) In Table 9.4, explain what it means if health motivation has b = .36 in terms of predicting number of exercise sessions per week.
14) What is the benefit of interpreting standardized beta weights? (see p. 264).
15) What happens if your predictor variables are too closely correlated?
16) Reflect on your learning. What has been the most difficult? How did you get through it? What concepts are still fuzzy to you? Is there anything you could share with me that would help me address how you learn best?