STAT 200 All Sections
| Please send e-mails to: [email protected]. | |||||||||||||||||||||||||||||||||||||||
| 3 Course Websites |
1. General Course Website
2. LON-CAPA site
3. Compass/Canvas site |
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| Office Hours | We'll be using ZOOM for individual help. See the ZOOM office hour schedule on the Compass/Canvas course site. | ||||||||||||||||||||||||||||||||||||||
| Technical Issues |
If you experience a glitch in Lon Capa/Compass/Canvas, first, try logging out and logging back in. If this doesn't work, send me an email describing the problem to our e-learning specialist Yuk Tung Liu at [email protected]. Please make sure to include a screenshot of the error in your e-mail. |
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| Course Materials |
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| Homework |
There will be two homework sets each week. See the HW schedule in the course calendar on the Compass/Canvas site. Ask questions on Lon Capa discussion boards and if you need more help, don't hestitate to come to the office hours. (No late hw accepted but lowest 3 hw scores dropped) |
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| Exams | There will be 2 midterm exams and a cumulative Final. See the exam schedule on the Compass/Canvas site. | ||||||||||||||||||||||||||||||||||||||
| Grade for Required Work |
Click here for a grade calculator.Grade for required work
Overall Grade is Translated into a Letter Grade as follows:
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| Bonus Work |
Bonus Points — You may earn between 0 and 100 Bonus Points.Everyone may earn between 0 and 100 Bonus Points. Every bonus point earned helps your overall grade, but even if you do no bonus work, you can still get 100% for the course. In other words, bonus points can only help you. Bonus points are extra credit.Bonus Points (100 total bonus points): 1. Pre-Lecture Bonus problems 30 bonus points 2. Completed Notebook 40 bonus points 3. Lon Capa Surveys 30 bonus points Descriptions Pre-Lecture bonus points There will be short pre-lecture videos posted on Lon Capa followed by a few questions. The pre-lectures are designed to give you a preview of the basic concepts you'll see in the actual lectures. Completed Notebook We will look over your notebook at the final. You'll get full credit if you have all the pages from lecture filled in. Lon Capa Surveys There will be 5 surveys (see the course calendar for due dates). Each survey is worth 6 bonus points. The surveys are all anonymous. Lon Capa just records whether or not you submitted a survey, not who submitted which answer. You must answer every question on the survey to get the 6 points. *Bonus points can only help you. You can still get 100% in this class without doing any bonus work. Bonus points are figured into your grade as follows: 
 So your grade would be raised from a 75% (C) to an 80% (B-). |
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| Course Outline |
Study Design - observational studies vs. randomized experiments, why randomized controls are key, what the possible confounders in observational studies are. Descriptive Statistics - mean, median, SD, histograms, box plots, normal curve, etc. Probability - multiplication rule, addition rule, conditional probability, Bayes rule Statistics for Random Variables - expected value and Standard error of chance processes, probability histograms and convergence to normal curve. Focus is on developing simple chance models (box models- drawing numbers at random from a box) that more complicated sampling processes can be translated into. Sampling and Statistical Inference - using sample means and percents to estimate population means and proportions, and attaching margins of errors to our estimates by computing confidence intervals. Why randomized sampling is key. Significance Tests - one sample and two sample Z-tests and t-tests and chi-square tests for goodness of fit and independence. Focus is on understanding how these tests depend on chance models. Experimental Power - Type I and II errors and the Power of Significance tests. Simple Linear Regression - correlation coefficient, regression equation, etc. Inference for Simple Linear Regression - Understanding the Simple Linear Model and Assumptions, Confidence Intervals and Significance tests for the Slope, Analysis of Variance for regression, etc. Binary Variables in Multiple Linear Regression - Causal Inference, Controlling for likely Confounders by including them as covariates in the regression model. Interactions between Binary and Quantitative variables . Models with 2 binary predictors. Multiple Regression with Quantitative X's - 3-D scatter plots and interpreting slopes graphically, Interactions, F-tests for overall regression effect and t-tests for slopes. Re-randomization Methods - Randomization Tests to calculate p-values for ANOVA and regression. ANOVA for Comparing Group Means Transformation of Variables- Fitting a Linear Model to non-linear data, log and square root transformations Logistic Regression -The log odds equation, making predictions and interpreting the slopes, the odds ratio, multiple logistic regression, maximum likelihood methods to estimate the slopes. Non-Parametric Statistics- Transform data into ranks to compute p-v values using Wilcoxon Mann-Whitney test (rank sum or U stat), Kruskal-Wallis test and Spearman's Rank-Order Correlation Coefficient |