STAT 509 (Statistics for Engineers)

Fall 2011


David Hitchcock, associate professor of statistics


Syllabus: (Word document) or (pdf document)

Office Hours -- Fall 2011

MWF 10:10-11:00 a.m. and Tu-Th 10:30-11:30 a.m., or please feel free to make an appointment to see me at other times.

Office: 209A LeConte College
Phone: 777-5346

Class Meeting Time

MWF 1:25 p.m.- 2:15 p.m., LC 210A

Current Textbook:

Statistical Methods for Engineers, by Geoffrey Vining and Scott M. Kowalski, Thomson, Brooks/Cole. (Either the second edition or third edition is OK.)

Course Description

509–Statistics for Engineers. (3) (Prereq: Math 142 or equivalent) Basic probability and statistics with applications and examples in engineering. Elementary probability, random variables and their distributions, random processes, statistical inference, linear regression, correlation and basic design of experiments with application to quality assurance, reliability, and life testing.

Learning Outcomes: By the end of the term successful students should be able to do the following:

  • Understand and be able to correctly use basic statistical terminology.
  • Recognize and evaluate variation in data using basic parameter estimation and hypothesis testing.
  • Compare data sets using parameter estimation, hypothesis testing and analysis of variance.
  • Recognize and evaluate relationships between two variables using simple linear regression.
  • Apply basic 2K design of experiments in order to study and improve engineering processes.
  • Understand and be able to apply simple principles of probability, parameter estimation, hypothesis testing, analysis of variance, simple linear regression, and design of experiments to engineering applications.

    Course Notes

    Computing Tips: R


    DateAssigned Homework Problems Date of Quiz that
    Includes This Material
    Friday, August 19#1(a,b,c,e), #3(a) on
    Probability Handout
    Wednesday, August 24
    Monday, August 22#1(d), #2, #3(b,c), #4, #5
    on Probability Handout
    Wednesday, August 24
    Wednesday, August 24 #1(a,b,c,d,e) on Bayes' Rule Handout
    3.1(a,b), 3.3(a,b), 3.5(a,b), 3.7(a,b)
    Friday, August 26
    Friday, August 263.1(c,d), 3.3(c,d), 3.5(c)
    Also: Sketch graphs of the probability distribution
    AND the cdf for 3.1 and 3.3.
    Monday, August 29
    Monday, August 293.9, 3.11, 3.15, 3.17 Wednesday, August 31
    Wednesday, August 31 3.21, 3.22(d), 3.23, 3.25, 3.29, 3.36, 3.37(a,b) Wednesday, September 7
    Friday, September 2 3.43(a,b,c), 3.45(a,b)
    For 3.45, you can initially plug in
    lambda=0.4 and beta=2.
    Hint for 3.45: Integration by substitution.
    Wednesday, September 7
    Wednesday, September 7 3.39, 3.41, 3.42, 3.43(d) Friday, September 9
    Friday, September 9 3.40(a,b,d), 3.46(b)
    Answers: 0.259, 0.861, 0.383, 0.198
    Monday, September 12
    Monday, September 12 3.49(a,b,c), 3.51, 3.53(a,b) Wednesday, September 14
    Wednesday, September 14 3.49(d), 3.54, 3.55
    Answers to 3.54: 0.5 0.0021, 0.0161
    Monday, September 19
    Friday, September 16 3.50
    Answers: 0.8415, 0.9612, 61.43
    Monday, September 19
    Monday, September 19 3.57, 3.59, 3.61, 3.65
    The "rough rules of thumb" in 3.65 are on
    p. 148, or you could use R to
    calculate an appropriate probability.
    Wednesday, September 21
    Wednesday, September 21 3.67, 3.69. In addition:
    Find the probability that a chi-square random
    variable (10 d.f.) is greater than 18.307.
    Find the probability that a chi-square random
    variable (14 d.f.) is greater than 4.66.
    Find the probability that an F random
    variable (5,24 d.f.) is greater than 3.9.
    Find the probability that an F random
    variable (6,20 d.f.) is greater than 2.6.
    Monday, September 26
    Friday, September 23 4.5(a,c)
    Monday, October 3
    Friday, September 30 4.7(a,c)[data are times between eruptions],
    For these (and 4.5), assume sigma is
    unknown and find and use s.
    Your answers will differ slightly from the
    book's answers in this case.
    Monday, October 3
    Monday, October 3 4.1(b), 4.3(b), 4.7(b), 4.9(b)
    Wednesday, October 5
    Wednesday, October 5 4.25(a,b,d,e)[s=6.351], 4.28(a,d,e)[s=1.934],
    Monday, October 10
    Friday, October 7 4.11(b), 4.13(b), 4.15(b)[s=10.598], 4.17(b)[s=0.243]
    For 4.15 and 4.17, do the problems in two ways:
    Assume sigma is known and given and we're doing a z-test;
    assume sigma is unknown and we're using s and doing a t-test.
    Monday, October 10
    Monday, October 10 4.11(d),4.12(d), 4.13(d), 4.14(d), 4.15(d)
    Answers: 4.12: 0.095. 4.14: 0.181.
    Assume sigma is known and as given in these problems.
    HINT: Pay attention to which alternative hypothesis you have.
    Wednesday, October 12
    Wednesday, October 12 4.35, 4.37 Monday, October 17
    Friday, October 14 4.31, 4.65[s=3.6976], 4.67[s=8.8741] Monday, October 17
    Monday, October 17 4.49[s_d=0.5036], 4.53[s_d=0.8145], 4.55[s_d=0.08367] Wednesday, October 19
    Wednesday, October 19 4.39[s_1=0.339, s_2=0.497], 4.43[s_1=1.225, s_2=1.010],
    4.47[s_1=69.411, s_2=83.292]
    Monday, October 24
    Monday, October 24 4.57, 4.59, 4.61 Wednesday, October 26
    Wednesday, October 26 #1, #2(a,b), #3 on ANOVA Handout Monday, October 31
    Friday, October 28 #2(c) on ANOVA Handout Monday, October 31
    Friday, November 4 6.3(a,b), 6.5(a), 6.7(a) Monday, November 7
    Monday, November 7 6.3(c), 6.5(b), 6.7(b)
    For each problem, perform the t-test for "model usefulness",
    find a 95% CI for the slope, and find and interpret the
    correlation coefficient for the pair of variables.
    Wednesday, November 9
    Wednesday, November 9 6.3(d), 6.5(c), 6.7(c)
    Get the 95% CI for E(Y) and 95% PI for a new Y for:
    6.3: x_0=0.65; 6.5: x_0=50; 6.7: x_0=7
    Answers: 6.3: (.349,.609), (.089,.868);
    6.5: (.799, .888), (.719, .968);
    6.7: (.118, .121), (.114, .124)
    Monday, November 14
    Friday, November 11 6.15, 6.16, 6.19
    For each, use R to get the estimated prediction equation
    and find the statistics/P-values we looked at in class:
    i.e., F-statistic, R^2, individual t-statistics.
    Output for 6.16, 6.19 in back of book; compare with R output.
    Monday, November 14
    Monday, November 14 6.28, 6.30, 4.76(a,c,d,e)
    Wednesday, November 16
    Wednesday, November 16 7.1, 7.2, 7.4(a)
    Monday, November 21
    Friday, November 18 7.12(a)
    You can estimate all of the effects with the help of R,
    or simply estimate the main effects, using the table of contrasts
    like we did in class. Note the table of contrasts for 7.12 is the
    same as the one for the example in class.
    Monday, November 21
    Monday, November 21 7.12(a) [see above instructions], 7.27(a)
    Monday, November 28
    Monday, November 28 7.27(b,c)
    Wednesday, November 30

    Information about Project

    Data Sets

    Review Sheets for Exams

    Formula Sheets for Exams