Drawing on the subfields of social, community, cultural, and self psychology, the Social Psychology Specialization examines the individual in the context of the broader community and culture. Specific attention is paid to issues of race, class, gender, and other important social categories. Research, individual study, and internship opportunities will involve applications of theory to real-world settings. Social Specialization Handout.

Others may apply as a Psychology Option. Barnack-Tavlaris collaborates with students on projects that seek to understand the complex factors that influence reproductive and sexual health attitudes, knowledge and behaviors.

Joe Maxwell LB STA 305 Bench

The research team aims to inform interventions and public health campaigns that can increase sexual well-being and reduce stigma. Students are involved in all aspects of the research process from conceptualization through dissemination of the research findings.

Crawford works with students on research investigating the origins and predictors of political beliefs and religiosity, as well as how and when individuals will allow their political or religious biases to influence their judgment. Students assist with many different facets of the research process, including literature reviews, study design, data collection, data entry and management, and writing of research reports. Lisa Grimm and her students in the MISC Lab focus on improving our understanding of how cognitive representations and processes are influenced by motivation, stereotype threat, individual differences, and a variety of other topics.

Students are involved in every stage of the research process. Recently, the lab has been investigating the use of video game training to improve cognitive skills and how exercise environments can be created to maximize health benefits, and started a new line of research on the psychology of magic.

Kim-Prieto and her students conduct research on the effects of various emotions on behaviors and beliefs. The research team works collaboratively throughout the research process, from research conceptualization to presentation.

Wiley conducts research with students on how people cope with low status and discrimination and manage their commitments to multiple groups. Students help conceptualize and design studies; collect, enter, and analyze data; and disseminate findings in posters and written reports.

Psychology School of Humanities and Social Sciences. Search Search. Jessica Barnack-Tavlaris Dr. Jarret Crawford Chair Dr. Lisa Grimm Dr.

Chu Kim-Prieto Dr. Experiential Learning Opportunities Dr.I don't understand why there are so many negative comments. Compared to the other stat courses I took so far specifically STAher slides are extremely clear and good choice of the textbook. And her test questions are pretty straightforward. Professor in the Statistics department at University of Toronto - St. George Campus. Professor Sue-Chee's Top Tags. Tough grader Test heavy Lecture heavy Tests are tough Gives good feedback.

Flag this rating. Check out Similar Professors in the Statistics Department 4. Feb 27th, For Credit: Yes.


Terrible lecturer. Stupid test questions. Seems to not understand her own material. A professor who puts the most difficult question in the first half during a test and makes you have no time for the second half. I might improve my time management skill a lot at the end of the class. I might thank her in the future, but absolutely not now when I see my mark. Feb 17th, I can't believe she made such difficulty out of material that should have been straightforward. Magical isn't it. Dec 17th, She said multiple choices on final exam on Quercus.

Bubble sheet on the table of the exam room. And then TA took the bubble sheet when the test begin and no multiple choices on the test paper at all. Dec 16th, Participation matters Test heavy Lecture heavy. Test heavy Tough grader.

Dec 13th, The concept of this course is easy, but an instructor who does not know the difference between estimate and estimator and her note which reveals this fact made the course extremely hard. Dec 4th, This person does not know what she is teaching. She have messed up random variables with constants during lecture, made arguments that are easy to counter-proved, etc.

If you would like to take this course, just self study by reading text book. Get ready to read. Nov 19th, For some unknown reason, this person argues that our answer on the tests should use plain English that can be understood by people who never learned stats.

Simply bs. I think this is the reason why she doesn't have any publication since she writes paper understandable by elementary school students.The rush is on for data scientists who can mine data and discover the wealth that it contains.

Data scientists in applied statistics collect, organize, display, analyze and infer from data. Applied statistics is doing what counts! At CBU, you will work side-by-side with full-time professors with real world experience.


Using state-of-the-art computers and software, you will analyze local and global life-changing issues and apply your results to save lives and help people. Basic concepts of analytical geometry, limits and derivatives, differentials and rates, integration, definite and indefinite integrals, differentiation of logarithmic and exponential functions.

Continued study and applications of integration: volumes, lengths, surface of revolution; derivatives and integrals involving trigonometric functions, infinite series, expansion of functions, hyperbolic functions, law of the mean, partial fractions, polar coordinates, and conic sections.

Prerequisite: MAT Mathematical theory and applications, development of formulae, principles of statistical decision theory, descriptive measurements, probability concepts, random variables, normal distribution, inferential statistics, sampling distributions, confidence intervals, hypothesis testing, chi-squared procedures, linear regression, and the use of computers in statistics.

This course represents a basic concepts and methodology course in regression analysis using application of general linear regression models to real-life situations. Regression models and model building typical of problems used in the social and behavioral sciences, the natural and health sciences, and many other disciplines are covered. Prerequisite: STA Students learn exploratory data analysis, coding and manipulation of variables, database management applying statistical concepts.

Modeling and simulation experiments on a variety of applied data sets. A calculus based course covering discrete and continuous distributions, expectations, the normal distribution, the central limit theorem, the binomial distribution, and various topics in statistical theory such as point estimation, hypothesis testing, and linear regression.

This course studies experimental designs with corresponding models and analyses critical for students in the empirical sciences. Course topics include estimation, test of hypothesis, analysis of variance and a variety of topics in experimental design. Decisions and practical considerations which minimize experimental error and avoid confounding results are dealt with in real life contexts.

Sampling theory and practice are presented in this course through a study of simple random samples, stratified random samples, cluster sampling, estimating sample size, ratio estimates, subsampling, two-state sampling and analysis of sampling error. This is a critical course for students in education and the social, medical, biological and management sciences where sampling is a fundamental step in virtually every statistical procedure and critical to meaningful survey research.

The first semester of a two-semester course providing a systematic development of the theories of probability and statistics. Students learn and use fundamental concepts of probability models, random variables and their distributions, reduction of data, estimation, testing of hypotheses, univariate normal inference, and statistical decision theory. The first semester is required for BA and BS statistics majors of all concentrations.

Lower Division Requirements MAT Analytcl Geometry and Calculus I Basic concepts of analytical geometry, limits and derivatives, differentials and rates, integration, definite and indefinite integrals, differentiation of logarithmic and exponential functions.

MAT Anlytcl Geometry and Calculus II Continued study and applications of integration: volumes, lengths, surface of revolution; derivatives and integrals involving trigonometric functions, infinite series, expansion of functions, hyperbolic functions, law of the mean, partial fractions, polar coordinates, and conic sections.You may also be interested in exploring the offerings of the Dept. While the focus here is on the undergraduate course offerings at UofT, strong students can sometimes petition to take graduate courses.

Graduate course listings are available on individual Departments' websites. Data Scientists need a solid background in Statistics and Computer Science, but often domain expertise — specific knowledge about the domain from which the data to be analyzed came from — is invaluable.

During your time at UofT, it would be valuable to develop expertise in some application domain; examples include finance, life science, physical science, psychology, and marketing. Some ideas for course sequences in an application domain can be found in the description of the Applied Statistics Specialist.

You will also learn how to use R to perform data analysis. Data Scientists in industry routinely conduct studies and experiments, for example to determine an optimal business strategy. Statistical Theory STA Theory of Statistical Practice allows you to more deeply understand and explain the results of statistical analysis, and enables you to read research studies.

Machine Learning Machine Learning is used to obtain insights for large-scale datasets. Machine Learning courses complement the Data Analysis courses. Multiple machine learning courses are available. Their contents overlap to some extent.

CSC Introduction to Neural Networks and Machine Learning is a third-year level course which introduces machine learning, but focus on neural networks. Neural networks are a set of techniques whose use has been booming in recent years. They saw applications in artificial intelligence, but in recent years Data Scientists have been using them in a variety of application domains.

While it is possible to take more than one of those courses, if you did well in one of them, you might like to explore other options. Data Wrangling For many Data Scientists, facility with wrangling data is absolutely essential. You should take CSC and practice processing datasets as soon as possible.

For people working with large datasets, knowledge of Linux-like systems is essential to be able to process data. CSC is a good place to start. It is a good idea to learn to do Data Wrangling in R as well.Statistical methods have applications in almost all areas of science, engineering, business, government, and industry.

The practising statistician is involved in such diverse projects as designing clinical trials to test a new drug, economic model-building to evaluate the costs of a guaranteed-income scheme, predicting the outcome of a national election, planning a survey of television viewing habits, and estimating animal populations. Probability theory is used to analyse the changing balance among the age-groups in a population as the birth rate changes, the control force needed to keep an aircraft on course through gusts of wind, the chance that the demand for electricity by all the customers served by a substation will exceed its capacity.

These are just three of many phenomena that can be analysed in terms of randomness and probability. The course offerings are intended not only for specialists in the theory of the subject but also to serve the needs of the many other disciplines that use statistical methods, e.

Students following the Specialist Program are encouraged to include courses in major fields of application in their overall program. The Major Program can be profitably combined with specialization in another discipline. Students in these programs may also qualify for the A.

Both applied and theoretical courses are offered in Statistics and Probability. The probability course STAH1 will be of interest to those whose field of application includes stochastic models.

Enquiries: St. Neal; e-mail: ugchair. Broverman; e-mail: ugchair. Higher Years: 1. STAH1 2. On the other hand, a student with an interest in pure math might choose to focus on applications of that subject matter to theoretical probability and statistics, selecting STAH1STAH1 towards a major in statistics.

Recommended: introductory course in disciplinary focus. Second year 3. Upper years: 4. Students in the Applied Statistics Specialist program must complete at least one disciplinary focus. To enrol in one or more focuses, students must first be enrolled in the Applied Statistics Specialist program. Sociology: 2. The Y1 and H1 seminars are designed to provide the opportunity to work closely with an instructor in a class of no more than twenty-four students.

Details can be found at www. This course, intended for students considering a program in Statistical Sciences, discusses the crucial role played by statistical reasoning in solving challenging problems from natural science, social science, technology, health care, and public policy, using a combination of logical thinking, mathematics, computer simulation, and oral and written discussion and analysis.This course will provide an introduction to the fundamental concepts of the design of scientific studies including the design of experiments and observational studies.

Students will be become acquainted with statistical methods used to design and analyze experiments and observational studies. In particular, this course will cover: experiments versus observational studies, clinical trial design, comparing several groups using a completely randomized design, randomized blocks, Latin squares, incomplete block designs, factorial designs, causal inference in randomized and non-randomized studies, and adjusting for selection bias using propensity score methods.

Causal inference in randomized experiments versus observational studies. Introduction to the propensity score and three ways to use the propensity score to adjust for selection bias: matching; sub classification; direct regression adjustment.

Comparing several groups in an experimental and observational setting and deciding whether differences that are found are likely to be real or due to chance.

Power and sample size will be introduced for several designs. Applications will include the design and analysis of clinical trials with continuous or binary endpoints. Split plot designs will be discussed as an example of restricted randomization in the design of experiments. Taback, N. Design of Experiments and Observational Studies.

Statistics for Experimenters: Design, Innovation, and Discovery. Box, G. Wiley 2nd Ed. Design and Analysis of Experiments. Dean, A. Design of Observational Studies. Rosenbaum, P. Springer Causal inference for statistics, social, and biomedical sciences.


Imbens and Rubin. Cambridge University Press, Sign up. Student Buddy. Browse course packages Packages may be identical but requires different amount of Oxdia points. Course info. Midterm Final exam 0.

Stat 305: Linear Models (and more)

Practice test 1. Exam review 2. Default sort Uploader rating Package rating Points required: low to high Points required: high to low Downloads. Midterm number. Package rating. Uploader rating. Require points. Not rated Sunshine Solutions: None or not audited. More detail and download. Solutions: Full. Description: Winter Session Test Description: 2 term tests with full solution. And other past tests helpful. Description: midterm sample solution Description: Midterm exam with full solutions Not rated jevonsz.

STA 305H/1004: STA305-STA1004 Experimental Design

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