He doesn't seem to care whether you grasp the material and the labs are extremely stressful. Lots of homework Skip class? You won't pass. Aug 21st, This class was such a terrible experience. He tried to secretly change the grading scale after we had already paid for Proctor-U. Was extremely hard to reach outside of class. Lectures were so confusing for absolutely no reason.
Do the practice tests and go to the out-of-class review and you'll be fine. The labs were a beast. Should've done calc 2. Jul 21st, When a whole week's worth of class was cancelled due to ice on the roads, instead of postponing the lectures, he just skipped them all together and expected us to teach ourselves.
Does not respond to emails. He changed the grading format and number of tests 5 times, and then complained to us that his email was overloading with parent complaints.
Jun 3rd, This class is a mess and one of the most disorganized classes I've ever taken. McKenna is super nice and cares, but he cannot teach to save his life. Never went to lecture, labs, or bought the textbook and still made an A. All you need is to watch the test reviews and work the practice exams!
Super easy A if you put in the bare minimum effort. Clear grading criteria. Apr 30th, Accessible outside class. Apr 29th, Do not waste your time going to the lectures. I went to the first one of the semester and never again. If you want to do well on the exams, just watch the exam review video and do the practice exams before you take the real exam.
I loved McKenna as a person, but his lectures are terrible. You can make up labs and exams though. Lots of homework Graded by few things Clear grading criteria.
Apr 23rd, Seriously do not waste your time going to the lectures. His lectures are hard to follow and all over the place. If you want to finish this course with a good grade watch the exam review videos on moodle and do the practice test. You can finish with an A by doing that alone. Apr 9th, Graded by few things. Mar 25th, Wish I could write more! Skip class? Mar 10th, McKenna is all over the place with his lectures. I assume that is is due to being online, so the learning experience is inhibited by that.
I am sure he would be better with in person classes. He makes some funny jokes, but makes some remarks that are a bit inappropriate. I wish he would actually explain the material in one go. Jan 29th, Mckenna devoted his life to becoming a teacher, however he cant teach.
McKenna is a shell of a teacher, with online school going on he has just gotten worse. Get ready to read Graded by few things. Jan 13th, Respected Amazing lectures Caring. Jan 11th, Exam review videos are an amazing tool to prep for the exams which are through ProctorU. Basic concepts of statistical models and use of samples; measures of variation and central tendency, normal, t, chisquare, and F distributions; tests of hypothesis; analysis of variance, regression, and correlation; emphasis on field oriented life science research problems.
Nonparametric one and two-sample location and distribution tests, including binomial, chi-square, Kolmogorov-Smirnov, Mann-Whitney U, Wilcoxon; analyses of variance, including Cochran's Q, Kruskal-Wallis, Friedman; correlation and regression, including Kendall's tau, Spearman's rho, and point biserial. Simple and stratified random sampling; ratio and regression estimation; cluster, multistage and multiphase sampling procedures; systematic sampling; nonresponse and nonsampling errors; links between methodology and application emphasized.
Analyses of variance and experimental designs; completely randomized and complete block designs; latin square designs; split plot; arrangements of treatments; multiple comparisons; covariance analysis; multiple and curvilinear regression techniques; emphasis on social and behavioral sciences research problems.
Multiple classification analyses of variance and covariance, sampling designs, parameter estimation, multiple regression and correlation, tests of specific hypothesis, and factorial experiments; emphasis on field-oriented life sciences research problems. Multiple classification analyses of variance and covariance; sampling designs, parameter estimation, multiple regression and correlation, tests of specific hypotheses, and factorial experiments; emphasis on field-oriented life science research problems.
Extensive development and application of statistical techniques to parameter estimation in population dynamics; principles of model building and role of model building in population management. Comparison of designs, models, and analyses; emphasis on factorial experiments, complete and incomplete block designs, and confounding. Fundamentals of regression analysis, stressing an understanding of underlying principles; response surfaces, variable selection techniques, and nonlinear regression.
Statistical techniques used in analyzing data from discrete distributions; contingency tables, loglinear and logit models, logistic regression, and repeated measures for nominal and ordinal data; emphasis on computer analysis and interpretation.
Comparison of multivariate techniques and analyses; emphasis on discriminant analysis, factor analysis and principal component analysis, canonical correlation, cluster analysis, and multivariate analysis of variance.
Characteristics of lifetime data; non-parametric methods including Kaplan Meier estimation; lifetime parametric models, parametric methods for single distribution data; planning life test; system reliability concepts; failure time regression; accelerated testing. Probability, random variables, discrete and continuous distribution functions; expected values, moment generating functions; functions of random variables.
Point estimation; hypothesis testing; interval estimation; large sample theory; new developments in statistical inference. Supervised application of statistical techniques to research problems; readings, oral presentations, and discussions on statistical consulting; problem-solving; mock-consulting sessions; participation in real-life statistical consulting sessions under faculty supervision.
Primary responsibility for statistical consulting projects under the supervision of graduate faculty. Pass-fail grading. A technical paper on an advanced topic in statistics is required. Development of a topic in advanced statistics under faculty supervision.
May be repeated for credit when topics vary. Lectures on advanced topics in statistics not covered in other experimental statistics courses.
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