STATISTICS FOR SOCIAL & BEHAVIORAL SCIENCES

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STATISTICS FOR SOCIAL & BEHAVIORAL SCIENCES Instructor: Prof. Amine Ouazad Office: 1135 in building A5 (1 st floor) Office phone: +971 2 628 5043 Office hours: Wednesday 4 5pm Email: amine.ouazad@nyu.

1 STATISTICS FOR SOCIAL & BEHAVIORAL SCIENCES Instructor: Prof. Amine Ouazad Office: 1135 in building A5 (1 st floor) Office phone: Office hours: Wednesday 4 5pm amine.ouazad@nyu.edu Lectures: Tuesday-Thursday pm Room: West Administration 002 Course Code: SOCSC-AD (15307) Academic Fellow: Irene Paneda irene.paneda@nyu.edu Tutorials: Sunday, pm and pm Room: Computational Research 006 Course Content This course introduces students to the use of data. How should we read data, summarize data, present conclusions, in other words tell stories using data? What stories cannot be told using the data at hand, and what stories can be reasonably told using data? We will be making statistics from very diverse sources: population censuses, movie reviews, restaurant reviews, test score data, psychology experiments. As such we will cover basic statistical concepts, inferential statistics, and regression techniques. This course also prepares students for more advanced courses in social and behavioral sciences. Upon completion of the course students should be able to perform the following tasks: Understand and prepare summary statistics: the mean, the variance, the standard deviation, the median, the mode, the quartiles. Know common data sets used in social and behavioral sciences: censuses, surveys, administrative data, online data. Make inferences regarding the population mean, standard deviation, variance using sample mean, standard deviation, variance. Understand the difference between correlation and causation, spot misuses of correlation as causation, and use regression analysis to make causal statements. 1 (see next page)

2 The course will consist of weekly lectures (Tuesday and Thursday) introducing the concepts of statistics using the Agresti and Finlay textbook, as well as real world examples, newspaper clippings, and hands-on analysis of data in class. Textbook Alan Agresti and Barbara Finlay, Statistical Methods for the Social Sciences, Fourth Edition, Pearson Publishing. The text is required and has been ordered for student purchase at the bookstore. Although the ordering of chapters will not be followed rigorously, the readings related to the topical coverage in the course have been specified below. Software We will use Stata 13, from statacorp, in recitations. Make sure you have a license of Stata on your laptop (IT should be able to help you with that), and that you learn how to handle the software during recitations: loading data, describing data, making inferences. Course website You will find course slides, links to the online quizzes, midterms and finals, and links to extra contents at Notes My goal is to have everybody on board. While the textbook and course material will seem daunting at first, not everything in the text will be covered in this course. Remember: only what is done in class is at the exam. The course will be oriented towards using statistics for hands-on applications. Homework, Exams and Evaluation Policy Grades are based solely on three elements: the quizzes, the midterms, and the final. Online quizzes (15%): throughout the term (on Thursday and they will be due the following Tuesday) and will count for 25% of the final grade. Supervised midterms and final (3 x 25%): There will be two midterm exams and a final exam. Both midterms will be in class. The final examination will take place at a time and location to be announced. Each of the three exams in this course count for 25% of the final grade. 2 (see next page)

3 Attendance (10%): Attendance and participation in lively classroom conversations is a must. We ll tackle a lot of important questions in class. However, to make the atmosphere relaxed, I don t grade participation. However, missing 3 classes or recitations without a proper written justifying it leads to the loss of the attendance grade. These points are easy to grab, so get them. The dates of the exams will be announced during the term. Non-communicating calculators (no Edge, 2G, 3G, LTE, 4G, 5G, wifi, bluetooth, etc) are recommended during midterms and finals. Class attendance & citizenship Mobile phones, tablets, and laptops are not permitted in class without prior approval. Be diligent in your studies and regular in your attendance this is the only way to make the course fun and exciting. Failure to attend would lead to the loss of the attendance grade (10%) and is at your own risk! You are responsible for any announcements made or information given during class, and as such no excuses will be accepted. Disability Accommodation Anticipating difficulties with the content or format of the course due to a physical or learning disability? We should work out a plan together. Classroom etiquette I d like to provide an excellent learning environment for everyone. This can be ensured if everybody observes certain basic ground rules. We respect every student s opinion. It s always welcome to interrupt me with questions related to the material being covered. Do not use laptops or other electronic devices for anything during class time except to take notes. If you are attending the lecture, plan on being there for the entire duration of the class. If you absolutely must leave early for a good reason, on any given day, please sit near the door and leave quietly. Food or drink within reason is fine. The really sexy job in the 2010s is to be a Statistician Hal Varian, Chief Economist, Google 3 (see next page)

4 Course Outline PART I. INTRODUCTION AND RESEARCH DESIGN Week 1: Introduction: Doing Empirical Research Asking an empirical question Collecting data Describing the data Drawing inferences Read: Agresti and Finlay Chapters 1 & 2 PAR T II. DESCRIBING DATA Week 2: Week 3-4: Describing the Data Univariate Analysis Mode, Median, Mean, Frequency distributions Graphs: Scatterplots, Histograms Variability: Range, Standard Deviation, and Variance Read: Agresti and Finlay Chapter 3 Describing the Data Bivariate Analysis Measures of correlation between two variables: Regression coefficient, Pearson s correlation, R 2 Read: Agresti and Finlay Chapter Midterm PART III. DRAWING CONCLUSIONS FROM DATA: INFERENTIAL STATISTICS Week 5: Week 6: Week 7-8: Week 9: Probability and Random variables Read: Agresti and Finlay Chapter Normal Distributions and the Central Limit Theorem Read: Agresti and Finlay Chapter Sampling Distribution of Means and Confidence Intervals Confidence interval method of hypothesis testing Read: Agresti and Finlay Chapter 5 t-distribution and t-tests for Means and Proportions One-sample and two-groups test Read: Agresti and Finlay Chapter Midterm (see next page)

5 PART IV. : CORRELATION AND CAUSATION: REGRESSION ANALYSIS Week 10-11: Univariate Regression Analysis t-tests of regression coefficients, standard errors, confidence intervals, R Squared Read: Agresti and Finlay Chapter 7 Week 12: Week 13: Week 14: Association and Causality Multivariate Regression Analysis Read: Agresti and Finlay Chapters Randomized Experiments Comparison of means, ANOVA Read: Agresti and Finlay Chapter 12 Robustness Checks Multicollinearity, Outliers, Heteroskedasticity Read: Agresti and Finlay Chapter Final Exam (see next page)

STA 225: Introductory Statistics (CT)

Marshall University College of Science Mathematics Department STA 225: Introductory Statistics (CT) Course catalog description A critical thinking course in applied statistical reasoning covering basic