Pdf Statistics Class 10
Totaly Pavlina
Statistics courses cover a range of topics essential for understanding and applying statistical methods. These include fundamental concepts such as probability, descriptive statistics, and inferential statistics. Learners will explore topics like hypothesis testing, regression analysis, analysis of variance (ANOVA), and statistical distributions. Advanced courses might explore multivariate statistics, time series analysis, and the use of statistical software like R, SAS, or SPSS. Practical exercises and real-world data analysis projects help learners apply these concepts effectively.
Choosing the right statistics course depends on your current knowledge level and career aspirations. Beginners should look for courses that cover the basics of probability, descriptive statistics, and introductory inferential statistics. Those with some experience might benefit from intermediate courses focusing on specific statistical methods like regression analysis, hypothesis testing, and ANOVA. Advanced learners or professionals seeking specialized knowledge might consider courses on multivariate analysis, time series forecasting, or advanced statistical modeling. Reviewing course content, instructor expertise, and learner feedback can help ensure the course aligns with your career goals.
A certificate in statistics can open up various career opportunities across multiple industries. Common roles include statistician, data analyst, biostatistician, and market researcher. These positions involve analyzing data, interpreting statistical results, and providing insights to support decision-making processes. With the increasing importance of data-driven approaches in fields such as healthcare, finance, marketing, and public policy, earning a statistics certificate can significantly enhance your career prospects and opportunities for advancement.
STAT 101 is an introductory course in statistics intended for students in a wide variety of areas of study. Topics discussed include displaying and describing data, the normal curve, regression, probability, statistical inference, confidence intervals, and hypothesis tests with applications in the real world. Students also have the opportunity to analyze data sets using technology.
The Basic Practice of Statistics by Moore, 8th edition, published by MacMillan. Custom edition (available only from the UIC Bookstore) includes only sections covered in this course. Note that an Achieve code is required for the course while the printed textbook is optional.
An Achieve code linked to your Blackboard account is required for this course. To ensure your Achieve code is properly linked to your blackboard account you should purchase your Achieve code through the link in Blackboard. An Achieve code purchased through the link in Blackboard will include an electronic version of the textbook, buying a print copy is optional.
The courses offered by the Department encompass three important areas: Applied Statistics, Statistical Theory, and Probability. Furthermore, courses offered by the department are kept up-to-date in accord with latest developments in statistical theory and practices.
This course provides students with a background in applied statistical reasoning. Fundamental topics are covered including graphical and numerical description of data, understanding randomness, central tendency, correlation versus causation, line of best fit, estimation of proportions, and statistical testing.
Statistical thinking, relevant ideas, themes, and concepts are emphasized over mathematical calculation. In this class students learn many of the elementary principles that underlie collecting data, organizing it, summarizing it, and drawing conclusions from it.
This course teaches the methods and concepts behind creating and conducting surveys and the statistical tools needed to analyze data gathered from them. Students participate in data collection from different sources for individual- and class-designed surveys. Requirement Designation LS Stats/Logic.
The course covers statistical applications in business, involving graphical and numerical descriptions of data, data collection, correlation and simple linear regression, elementary probability, random variables, Binomial and Normal distributions, sampling distributions, and confidence intervals and hypothesis tests for a single sample.
The purpose of this course is to prepare students for further study and job preparation in the field of Business. It will emphasize understanding of data and interpretation of statistical analyses. It will require students to think of data, and report the results of their analyses, in context.
Special Note: High school students who earn a "3" or better on the AP statistics exam will be given credit for STA 2023.
The course covers Normal distributions, sampling variation, confidence intervals, hypothesis testing, one-way and two-way analysis of variance, correlation, simple and multiple regression, contingency tables and chi-square tests, non-parametric statistics.
The purpose of this course is to prepare students for further study and job preparation in the field of Natural Sciences. It will emphasize understanding of data and interpretation of statistical analyses. It will require students to think of data, and report the results of their analyses, in context
Prerequisite: A grade of "C-" or better in MAC 1105 College Algebra (or equivalent).
Special Note: Subsequent credit for STA 5126 is not permitted. No credit is given for STA 2122 if a grade of "C-" or better is earned in STA 2171, STA 3032, or QMB 3200.
This course provides an introduction to statistics emphasizing applications in Biology. Topics include descriptive statistics, elementary probability, the binomial and normal distributions, confidence intervals and hypothesis tests for means and proportions, correlation and regression, contingency tables and goodness-of-fit tests, analysis of variance and non-parametric tests.
The purpose of this course is to prepare students for further study and job preparation in the field of Biological Sciences including Medicine, Dentistry, other healthcare professions, Veterinary Medicine, Zoology and Botany. It will emphasize understanding of data and interpretation of statistical analyses. It will require students to think of data, and report the results of their analyses, in context.
Prerequisite: MAC 2311 Calculus I and Biology major status, or departmental approval.
Special Note: No credit is given for STA 2171 if a "C-" or better has been previously earned in STA 2122 or STA 3032 or QMB 3200.
This course covers linear and multiple regression; one-and-two-way analysis of variance; chi-square and contingency tables; design, analysis, evaluation and interpretation of statistical models. Well-prepared students can skip STA 3024 and take either STA 4202 or 4203.
Prerequisite: Introductory statistics course at or above the 2000 level or instructor permission.
This course will cover calculus-based probability, discrete and continuous random variables, joint distributions, sampling distributions, and the central limit theorem. Topics include descriptive statistics, interval estimates and hypothesis tests, ANOVA, correlation, simple and multiple regression, analysis of categorical data, and statistical quality control.
Prerequisite: MAC2312
This course is a sequel to STA 3024, SAS for Data and Statistical Analyses. We will cover the following topics utilizing the SAS software: ANOVA, Linear Modeling, Logistic Regression, bootstrap sampling, simulation using the data step, and some additional topics in the data step.
Prerequisite: STA 3024 or instructor permission.
Matlab and a programming language (C/Fortran) will be used. Floating point arithmetic, numerical matrix analysis, multiple regression analysis, non-linear optimization, root finding, numerical integration, Monte Carlo sampling, survey of density estimation.
Prerequisite: At least one statistics above STA 1013, some programming experience, or instructor permission.
Matlab and a programming language (C/Fortran) will be used. A continuation of STA 4102 in computational techniques for linear and non-linear statistics. Statistical image understanding, elements of pattern theory, simulated annealing, Metropolis-Hastings algorithm, Gibbs sampling.
Prerequisite: STA 4102 or instructor permission.
This course is a hands-on introduction to statistical methods for supervised, unsupervised, and semi-supervised learning. It explores fundamental techniques including but not limited to Support Vector Machines, Decision Trees, Linear Discriminant Analysis, Random Forests, Neural Networks, and different flavors of Boosting.
Prerequisite: STA 3032 or instructor permission.
Autoregressive, moving average and mixed models, autocovariance and autocorrelation functions, model identification, forecasting techniques, seasonal model identification, estimation and forecasting, intervention and transfer function model identification, estimation and forecasting.
Prerequisite: STA 2122, STA 2171, QMB 3200 or equivalent. Knowledge of PCs or UNIX.
This is a capstone course intended for statistics majors. The goal will be to enhance students' competencies by applying advanced statistical methodology to the challenges imposed by real data and developing effective writing skills to effectively communicate project requirements and findings. Students will be exposed to several aspects of statistical practices including elements of statistical consulting, study design, setting project goals and deliverables, applying appropriate methodology, performing accurate analyses, and providing clear and concise explanations of results. Fulfills upper division writing requirement.
This is a course of webcasts (about 15-30 minutes apiece), designed to introduce you to the basics of statitiscs, primarily as practiced in finance and investing. As with my accounting class, I will start with the open disclosure that my knowledge in statistics is limited to what I use on a regular basis, and that I have no interest (or expertise) in delving into the depths of statistical theory. The class webcasts are right below, followed by links to the statistical tools that I find useful, and readings on each topic. With each session, I also have a post-class test and solution, some more involved than others, testing the grasp of the material in the session.
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