L1 and Online: Ellen Fireman   Email: fireman@illinois.edu

S1: Yan Liu   Email: yanl5@illinois.edu

S2: Runmin Wang   Email: rwang52@illinois.edu

S3: Yuan Yubai   Email: yubaiy2@illinois.edu

S4: Xinming Yang   Email: xyang104@illinois.edu

S5: Yinyin Chen   Email: ychen409@illinois.edu

1. Required Textbook: Stat 200 Incomplete Lecture Notes Workbook Spring 2019 edition by Ellen Fireman, Karle Flanagan, and John Marden
   • Only available at the Illini Union Bookstore.
   • You will fill out this notebook while watching the lectures or lecture videos for extra credit. It's essential to understanding and remembering the course material for both in class and online students.
   • Only the Spring 2019 version will be accepted for extra credit (no previous versions).
2. Required Calculator: We strongly recommend this one, but any non-programmable calculator is acceptable (no phones, graphing calculators, etc.) during exams. For homework you can use any calculator you wish.

Homework is due every Monday and Wednesday at 11:59pm (see calendar) on Lon-Capa. Ask questions on Lon Capa discussion boards and if you need more help, don't hestitate to come to the office hours in 23 Illini Hall.

(No late hw accepted but lowest 3 hw scores dropped)

Grade for required work

Overall Grade is Translated into a Letter Grade as follows:

Bonus Points — You may earn between 0 and 100 Bonus Points.

Here's the filled in notes for the pages you were supposed to complete on your own for bonus points.

So your grade would be raised from a 75% (C) to an 80% (B-).

Click here for a grade calculator.

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.

ANOVA for Comparing Group Means

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.

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