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Material Type: Exam; Class: Introduction to Statistics; Subject: Mathematics; University: Colgate University; Term: Fall 2008;
Typology: Exams
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December 16, 2008
Final Exam — Math 102 / Core 143 CX
Points are in parentheses. An unsimplified answer like 12
3 .51 + 6/7 is usually worth more than 23.3, because it is easier to understand where it came from.
(a) What kind of test (z or t or 2-sample z or χ^2 ) should be used to decide whether the new curriculum is better than the old? (b) Find the value of the variable (z or t or χ^2 ) used in this test. (c) Find the P -value, i.e., the probability that, if the new curriculum were no better than the old, her students would average 54 or more just by chance. (d) Should we conclude that the new curriculum results in higher scores than the old? (e) Suppose another researcher reaches a conclusion that is the opposite of ours. Should we conclude that his data is “tainted” (i.e., obtained by faulty methods or even perhaps falsified)?
(a) What kind of test would be used to decide whether these results are significant evidence against the genetic model? (b) In computing the value of the variable (z or t or χ^2 ) used in this test, should we use the numbers of plants with flowers of each color, or the percentages of each color? Or doesn’t it matter? (c) Compute the value of the variable. (d) Is this data significant evidence against the model?
(a) What is his regression equation for projecting N from F ?. (b) Roughly how far off should the ecologist expect the projections to be as he makes them using his equation in (a)?
(c) Suppose his regression equation was N =. 4 F + 5 (which it isn’t). If the nutrient level turns out to be 7 when 8 tons of fertilizer were used, what is the corresponding residue (or residual) relative to his projection?
(a) Find a 95% confidence interval for the percentage of households in the city that have internet connections. (b) How many households must be surveyed so that the confidence interval requested in (a) will turn out only a third as large as the one you found in (a)? (c) Find a 85% confidence interval for the average amount of money spent per meal in the city.
(a) If two cards are selected without replacement, what is the probability that both are kings? (b) If one card is selected, what is the probability that it is either a king or a club, or both? (c) If five cards are selected with replacement, what is the probability that at least one is a club? (d) If five cards are selected with replacement, what is the probability that exactly three are clubs?
For a sample of size n from a population with average μ and standard deviation σ: EV of sum of scores in sample = nμ SE of sum = σ ·
n EV of average of sample = μ SE of average = σ/
n For significance tests (especially with small samples), approximate (bootstrap) population standard
deviation σ with sample standard deviation s = SD+^ =
√ ∑ (x − x)^2 n − 1
= (SD of sample)
√ (^) n n− 1.
(The null hypothesis will give a value to use for μ.) For large samples (n ≥ 30), s is close to σ. For confidence intervals, also approximate population average μ with sample average x.
Special case: Population is 0’s and 1’s (or yeses and nos, or ins and outs, or.. .), fraction of 1’s is p, for a sample of size n: EV of count = np SE of count =
√ p(1 − p) ·
n EV of % (or proportion) = p SE of % (or proportion) =
√ p(1 − p)/
n For CIs, approximate (bootstrap) population proportion p with sample proportion ˆp.
For use with confidence interval or t-test for significance on small (n < 30) samples: degrees of freedom = n − 1
k% confidence interval for the average of a population: Let zk denote the z-value for which k percent of the data is between −zk and zk. Then the CI is
x ± zk · (SE for average)
(Similar for “proportion” in place of “average”.)
For significance test for difference of μ’s in two populations: SE for difference of averages of 2 samples =
√ (SE of first)^2 + (SE of second)^2 EV of difference = 0 by H 0. (For more than 2 samples, use one-way ANOVA.)
For deciding significance of differences in frequency distributions among categories: χ^2 = ∑ [(observed − expected)^2 /expected] degrees of freedom: in “list” distributions, # in list −1; in “table” distributions, (# of rows −1) · (# of columns −1)
t-table: column head is P (t ≥ entry) χ^2 -table: column head is P (χ^2 ≥ entry)
- 0.0 0.0 0.9 63.19 1.8 92.81 2.7 99.31 3.6 99. z Area(%) z Area(%) z Area(%) z Area(%) z Area(%) - 0.05 3.99 0.95 65.79 1.85 93.57 2.75 99.4 3.65 99. - 0.1 7.97 1 68.27 1.9 94.26 2.8 99.49 3.7 99. - 0.15 11.92 1.05 70.63 1.95 94.88 2.85 99.56 3.75 99. - 0.2 15.85 1.1 72.87 2 95.45 2.9 99.63 3.8 99. - 0.25 19.74 1.15 74.99 2.05 95.96 2.95 99.68 3.85 99. - 0.3 23.58 1.2 76.99 2.1 96.43 3 99.73 3.9 99. - 0.35 27.37 1.25 78.87 2.15 96.84 3.05 99.771 3.95 99. - 0.4 31.08 1.3 80.64 2.2 97.22 3.1 99.806 4 99. - 0.45 34.73 1.35 82.3 2.25 97.56 3.15 99.837 4.05 99. - 0.5 38.29 1.4 83.85 2.3 97.86 3.2 99.863 4.1 99. - 0.55 41.77 1.45 85.29 2.35 98.12 3.25 99.885 4.15 99. - 0.6 45.15 1.5 86.64 2.4 98.36 3.3 99.903 4.2 99. - 0.65 48.43 1.55 87.89 2.45 98.57 3.35 99.919 4.25 99. - 0.7 51.61 1.6 89.04 2.5 98.76 3.4 99.933 4.3 99. - 0.75 54.67 1.65 90.11 2.55 98.92 3.45 99.944 4.35 99. - 0.8 57.63 1.7 91.09 2.6 99.07 3.5 99.953 4.4 99. - 0.85 60.47 1.75 91.99 2.65 99.2 3.55 99.961 4.45 99.