**STAT 280: ELEMENTARY APPLIED STATISTICS**

**The University of Adelaide**

**ECON 1008: Data Analytics**

In today's world, good decision making relies on data and data analysis. This course helps students develop the understanding that they will need to make informed decisions using data, and to communicate the results effectively. The course is an introduction to the essential concepts, tools and methods of statistics for students in business, economics and similar disciplines, although it may have wider interest. The focus is on concepts, reasoning, interpretation and thinking rather than computation, formulae and theory. Much of the work will require students to write effectively and communicate their ideas with clarity. The course covers two main branches of statistics: descriptive statistics and inferential statistics. Descriptive statistics includes collecting data and summarising and interpreting them through numerical and graphical techniques. Inferential statistics includes selecting and applying the correct statistical technique in order to make estimates or test claims about a population based on a sample. Topics covered may include descriptive statistics, correlation and simple regression, probability, point and interval estimation, hypothesis testing, multiple regression, time series analysis and index numbers. By the end of this course, students should understand and know how to use statistics. Students will also develop some understanding of the limitations of statistical inference and of the ethics of data analysis and statistics. Students will work in small groups in this course; this will develop the skills required to work effectively and inclusively in groups, as in a real work environment. Typically, one component of the assessment requires students to work in teams and collect and analyse data in order to answer a real-world problem of their own choosing.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**College of the Canyons**

**MATH 140: Introductory Statistics**

This course examines statistical methods including empirical and theoretical frequency distributions, sampling, estimation, hypothesis testing, correlation, regression, probability, counting techniques and computer-based statistical software.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**Del Mar College**

**MATH 1342: Elementary Statistical Methods**

Statistical description - frequency distributions, measures of location, variation; probability - basic rules, concepts of random variables and their distributions (including binomial and normal); statistical inference - confidence intervals, tests of hypotheses p-values, introduction to linear regression.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**East China Normal University**

**MAT 22: Intro to Statistics**

The ability to predict the future is a rare gift. Future events are never certain. The study of Statistics is an important, practical methodology for predicting future outcomes. Statistics begins with data from past events. By incorporating the element of randomness in a data set, Statistics is able to harness the power of probability theory to assess future scenarios. The challenge of “decision making under uncertainty” becomes a reasonable possibility.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**De Anza College**

**MATH 010: Introductory Statistics**

Introduction to data analysis making use of graphical and numerical techniques to study patterns and departures from patterns. The student studies randomness with an emphasis on understanding variation, collects information in the face of uncertainty, checks distributional assumptions, tests hypotheses, uses probability as a tool for anticipating what the distribution of data may look like under a set of assumptions, and uses appropriate statistical models to draw conclusions from data. The course introduces the student to applications in engineering, business, economics, medicine, education, social sciences, psychology, the sciences, and those pertaining to issues of contemporary interest. The use of technology (computers or graphing calculators) will be required in certain applications. Where appropriate, the contributions to the development of statistics by men and women from diverse cultures will be introduced.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**College of Marin: Kentfield**

**MATH 115: Probability and Statistics**

An in-depth introduction to probability and statistics appropriate for students in the math and life/earth science disciplines. Descriptive statistics, introduction to probability theory, probability distributions, data sampling, estimation, correlation, hypothesis testing.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**College of DuPage**

**MATH 1635: Statistics**

Students will be introduced to elements of descriptive and inferential statistics. Topics include communication with data descriptions and graphs; probability principles and their use in developing probability distributions; binomial, normal, student-t, chi-square, and F distributions; hypothesis testing; estimation; contingency tables; linear regression and correlation; and one-way ANOVA.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**Colgate University**

**MATH 105: Introduction to Statistics**

An introduction to the basic concepts of statistics. Topics include experimental design, descriptive statistics, correlation, regression, basic probability, mean tendencies, the central limit theorem, point estimation with errors, hypothesis testing for means, proportions, paired data, and the chi-squared test for independence. Emphasis is on statistical reasoning rather than computation, although computation is done via software.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**Colby College**

**MA 231 APPL STAT / REGRESS ANALY**

Elementary probability theory, special discrete and continuous distributions, descriptive statistics, sampling theory, confidence intervals, tests of hypotheses, correlation, linear regression, and multiple linear regression. Examples and applications slanted toward economics.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**Birmingham Southern College**

**MA 207: General Statistics**

An investigation of four fundamental topics in statistics: displaying data, producing data,

probability, and statistical inference. The course uses a statistical software package.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**El Camino College**

**MATH 150: Elementary Statistics with Probability**

The focus of this course is the basic practice of statistics, including descriptive statistics, inferential statistics, and the role probability plays in statistical analysis. Students calculate and the interpret various descriptive statistics using graphing calculators with statistical testing capabilities and statistical software, as well as by hand. Major topics include methods of data collection and simulation; distributions, including normal and binomial distributions; probability theory; and inferential statistical methods. Students choose, justify, use, and interpret the results of inferential techniques, such as confidence intervals, hypothesis tests, goodness of fit, analysis of variance, and nonparametric tests.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**Grinnell College**

**STA 209: Applied Statistics**

The course covers the application of basic statistical methods such as univariate graphics and summary statistics, basic statistical inference for one and two samples, linear regression (simple and multiple), one- and two-way ANOVA, and categorical data analysis. Students use statistical software to analyze data and conduct simulations.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**Indian River State College**

**STA 2023: Elementary Statistics I**

This course includes measures of central tendency and variability, probability, random variables, normal and binomial distributions, confidence intervals, tests of hypotheses, correlation and simple linear regression, descriptive and inferential techniques and concepts which apply to sample data which has been gathered from a population.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**INTI International University & Colleges Malaysia**

**STA 219: Statistical Methods Lab**

The course consists of topics from three basic areas: descriptive statistics, probability and statistical inference and forecasting techniques. Descriptive statistics covers organizing, presenting and summarizing data. Probability includes Bayes’ theorem and probability distribution. Statistical inferences emphasize on estimation and hypothesis testing of large samples. Concept of simple linear regression and correlation as well as time-series is covered under forecasting techniques.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**The American Business School of Paris**

**MATH210: Business Statistics**

Upon completion of this course, students should be able to:

- Use measures of position and dispersion as well as graphs, to describe a given set of data and interpret the result
- Understand basic probability concepts.
- Use a probabilistic model in simple decision-making situations.
- Assess estimates of proportions and averages measured on a sample

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**University of Sydney**

**BUSS1020: Quantitative Business Analysis**

All graduates from the BCom need to be able to use quantitative techniques to analyse business problems. This ability is important in all business disciplines since all disciplines deal with increasing amounts of data, and there are increasing expectations of quantitative skills. This unit shows how to interpret data involving uncertainty and variability; how to model and analyse the relationships within business data; and how to make correct inferences from the data (and recognise incorrect inferences). The unit will include instruction in the use of software tools (primarily spreadsheets) to analyse and present quantitative data.

**STAT 280: ELEMENTARY APPLIED STATISTICS**

**Western Connecticut State University: Danbury**

**MAT 120: Elementary Statistics**

An introduction to the practices of statistics, for non-science or math majors, which emphasizes elementary data analysis and inference. Topics include correlation, regression, probability models, estimation, and hypothesis testing. Examples will be selected from many fields, such as anthropology, business, medicine, psychology, sociology, and education. Students will be expected to use appropriate computer software.

**STAT 305: INTRO TO STAT FOR BIOSCIENCES**

**Oregon State University**

**ST 314: Introduction to Statistics for Engineers**

Probability, common probability distributions, sampling distributions, estimation, hypothesis testing, control charts, regression analysis, experimental design.

**STAT 305: INTRO TO STAT FOR BIOSCIENCES**

**Universidad Carlos III de Madrid**

**15535: Statistics**

Once successfully having studied this subject, the students should be able to:

- Analyze problems involving random phenomena
- Define populations for a statistical study
- Build Hypothesis about a distribution
- Estimate and test hypothesis about the parameters of the chosen model
- Evaluate how well does the model fit to reality
- Understand the limitations of the methods that have been studied and the conditions under which they lead to wrong conclusions

**STAT 305: INTRO TO STAT FOR BIOSCIENCES**

**University College London**

**MATH 0008: Applied Mathematics I**

The course provides an introduction to methods and tools used in applied mathematics to develop simple models of mechanical, biological and other systems of interest. The main tools introduced are qualitative and analytic approaches to differential equations, stability, waves and oscillations.

**STAT 305: INTRO TO STAT FOR BIOSCIENCES**

**University of Western Ontario**

**STATS 2244A: Statistics for Science**

An introductory course in the application of statistical methods, intended for honors students in departments other than Statistical and Actuarial Sciences, Applied Mathematics, Mathematics, or students in the Faculty of Engineering. Topics include sampling, confidence intervals, analysis of variance, regression and correlation. Cannot be taken for credit in any module in Statistics, Actuarial Science, or Financial Modeling.

**STAT 310: PROBABILITY & STATISTICS**

**Tecnológico de Monterrey**

**MA 1006: Probability & Statistics**

Upon completion of this course:

- Students will understand the basic concepts of probability and problem solving using counting techniques, conditional probability, and discrete and continuous random variables and their distributions.
- Students will analyze a set of experimental data and make statistical inferences from the data.

**STAT 310: PROBABILITY & STATISTICS**

**Boğaziçi University**

**ME 207: Probability and Statistics for Mechanical Engineers**

Elements of Probability and Statistics; Random Variables; Mathematical Expectation; Probability Distributions; Sampling Theory; Estimation Theory; Hypothesis Testing; Regression and Correlation Analysis; Analysis of Variance; Mechanical Engineering Applications.

**STAT 310: PROBABILITY & STATISTICS**

**London School of Economics & Political Science**

**ME117: Further Statistics for Economics and Econometrics**

The course provides a precise and accurate treatment of probability, distribution theory and statistical inference. As such there will be a strong emphasis on mathematical statistics as important discrete and continuous probability distributions are covered (such as the Binomial, Poisson, Uniform, Exponential and Normal distributions). Properties of these distributions will be investigated including use of the moment generating function. Point estimation techniques are discussed including method of moments, maximum likelihood and least squares estimation. Statistical hypothesis testing and confidence interval construction follow, along with non-parametric and goodness-of-fit tests and contingency tables. A treatment of linear regression models, featuring the interpretation of computer-generated regression output and implications for prediction, rounds off the course. Collectively, these topics provide a solid training in statistical analysis. As such, this course would be of value to those intending to pursue further study in statistics, econometrics and/or empirical economics. Indeed, the quantitative skills developed by the course are readily applicable to all fields involving real data analysis.

**STAT 310: PROBABILITY & STATISTICS**

**University of British Columbia**

**STAT 302: Introduction to Probability**

Basic notions of probability, random variables, expectation and conditional expectation, discrete and continuous probability distributions, limit theorems.

**STAT 310: PROBABILITY & STATISTICS**

**University College London**

**MATH0057: Probability and Statistics**

The aim of the course is to introduce students to the theory of probability and some of the statistical methods based upon it. Many physical processes involve random components which can only be modelled using probabilistic methods. Statistical theory is vital for analyzing scientific data where it is necessary to distinguish genuine patterns from random fluctuations.

**STAT 313: UNCERT & RISK IN URBAN INFRAST**

**University College London**

**STAT0011: DECISION AND RISK**

This course aims to give an introduction to the statistical treatment of risk, the calculation of losses, and the theory of how to make optimal decisions based on such considerations. We begin with a review of statistical methods for estimating parameters of physical processes, and then show how these can be used to find the expected loss associated with different decisions, allowing choices to be made. Much of this course focuses on how to estimate the probability of extreme events occurring, for example high magnitude earthquakes, or large terrorist attacks. Additionally, we cover methods for detecting whether something important about a physical process has changed, so that risk computations can be updated in the light of new information. On successful completion of this course, a student should be able to understand measures of risk, find appropriate probability models for risky events, and check the validity of the underlying assumptions, understand Bayesian risk together with its theoretical assumptions, understand basic extreme value statistics, and understand basic time series modelling with structural change detection.

**STAT 3XX: DEPT APPROVED TRANSFER CREDIT**

**National University of Singapore**

**ST 3236: Stochastic Processes I**

This module introduces the concept of modelling dependence and focuses on discrete-time Markov chains. Topics include discrete-time Markov chains, examples of discrete-time Markov chains, classification of states, irreducibility, periodicity, first passage times, recurrence and transience, convergence theorems and stationary distributions. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

**STAT 405: R FOR DATA SCIENCE**

**Claremont Graduate University**

**MATH 264: Scientific Computing**

Computational techniques applied to problems in the sciences and engineering. Modeling of physical problems, computer implementation, analysis of results; use of mathematical software; numerical methods chosen from: solutions of linear and nonlinear algebraic equations, solutions of ordinary and partial differential equations, finite elements, linear programming, optimization algorithms and fast-Fourier transforms.

**STAT 406: SAS STATISTICAL PROGRAMMING**

**Arcadia University**

**STATS 301 Statistical Programming and Modeling using SAS**

One of the key purposes of STATS 301 is to introduce you to the SAS software for the purposes of statistical inference, programming and modelling. SAS is a major commercial statistics package that is used at about 40,000 sites worldwide, and by four million users. We will use SAS as a programming language, and some more advanced features of SAS programming. STATS 301 is designed to be a practical course in the use of SAS in industry, such as, Market Research, Finance and Medicine. To date all the data you have seen has usually been given to you in a form ready for exploration and modelling. This is rarely the case in most day-to-day projects in industry. Here, the emphasis will be on getting data from a “raw and messy” form into a state ready for the data analysis techniques you have learnt, or will learn, at undergraduate level. *Topics studied include*: The general SAS programming environment, reading data into SAS, 'Slicing Dicing and Splicing data' and presenting the data in user friendly formats. Statistical Modeling techniques include linear modeling, multivariate ANOVA, tables of counts.

**STAT 406: SAS STATISTICAL PROGRAMMING**

**The University of Auckland**

**STATS 301: Statistical Programming and Modeling using SAS**

One of the key purposes of STATS 301 is to introduce you to the SAS software for the purposes of statistical inference, programming and modelling. SAS is a major commercial statistics package that is used at about 40,000 sites worldwide, and by four million users. We will use SAS as a programming language, and some more advanced features of SAS programming. STATS 301 is designed to be a practical course in the use of SAS in industry, such as, Market Research, Finance and Medicine. To date all the data you have seen has usually been given to you in a form ready for exploration and modelling. This is rarely the case in most day-to-day projects in industry. Here, the emphasis will be on getting data from a “raw and messy” form into a state ready for the data analysis techniques you have learnt, or will learn, at undergraduate level.

Topics studied include: The general SAS programming environment, reading data into SAS, 'Slicing Dicing and Splicing data' and presenting the data in user friendly formats. Statistical Modeling techniques include linear modeling, multivariate ANOVA, tables of counts.

**STAT 410: LINEAR REGRESSION **

**National University of Singapore**

**ST 3131: Regression Analysis**

This module focuses on data analysis using multiple regression models. Topics include simple linear regression, multiple regression, model building and regression diagnostics. One and two factor analysis of variance, analysis of covariance, linear model as special case of generalized linear model. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

**STAT 410: LINEAR REGRESSION**

**London School of Economics & Political Science**

**ST201: Statistical Models and Data Analysis**

A second course in statistics with an emphasis on problems of practical importance and statistical analysis using computers. Principles of modelling: data preparation, mathematical and statistical models, linear and non-linear models. Simple linear regression. Multiple regression: assumptions, transformations, diagnostics, model selection. Logistic regression: odds ratios and likelihood. The course will conclude with a brief introduction to time series.

**STAT 410: LINEAR REGRESSION**

**Jacobs University Bremen**

**990 201 STATS MTHDS II CLASS MOD PRED**

This course builds on discussion of quantitative methods in Statistical Methods I. It focuses on multivariate statistical methods, in particular regression analysis, factor analysis, principal component analysis, and cluster analysis. The general objective is to make students intelligent users of the various multivariate statistical methods and enable them to make sensible decisions about when to use which procedure. This course, like the previous one, is divided into lecture and lab sessions. The lectures discuss the theoretical aspects of the different methods. The lab classes teach students how to run the relevant procedures in SPSS, how to interpret the computer output and how to effectively communicate the results of statistical analyses.

**STAT 410: LINEAR REGRESSION**

**London School of Economics & Political Science**

**ST300: Regression and Generalised Linear Models**

A solid coverage of the most important parts of the theory and application of regression models, generalised linear models and the analysis of variance. Analysis of variance models; factors, interactions, confounding. Multiple regression and regression diagnostics. Generalised linear models; the exponential family, the linear predictor, link functions, analysis of deviance, parameter estimation, deviance residuals. Model choice, fitting and validation. The use of a statistics package will be an integral part of the course. The computer workshops revise the theory and show how it can be applied to real datasets.

**STAT 410: LINEAR REGRESSION**

**Universidade Estadual de Campinas: Instituto de Matemática, Estatística e Computação Científica**

**ME613: Análise de Regressão**

**STAT 410: LINEAR REGRESSION**

**Chinese University of Hong Kong**

**STAT5030: Linear Models**

This course introduces important and fundamental elements related to the area of linear statistical models. A brief review of linear algebra will be given to the students. The major substance of this course covers: 1) distribution theory: multivariate normal and related distributions, distribution of quadratic forms; 2) full-rank linear models: least squares estimation, maximum likelihood estimation, simultaneous confidence intervals, tests of linear hypotheses, generalized least squares; 3) non-full-rank linear models: estimability, parameter estimation, testable hypotheses, estimability conditions; and 4) applications of linear models: regression analysis, analysis of variance, analysis of covariance.

**STAT 421: APPLIED TIME SERIES/FORECASTNG**

**London School of Economics & Political Science**

**ST304: Time Series and Forecasting**

The course introduces the student to the statistical analysis of time series data and simple models. What time series analysis can be useful for; autocorrelation; stationarity, trend removal and seasonal adjustment, basic time series models; AR, MA, ARMA; invertibility; spectral analysis; estimation; forecasting; introduction to financial time series and the GARCH models; unit root processes.

**STAT 421: APPLIED TIME SERIES/FORECASTNG**

**University College London**

**STAT0010: Forecasting **

Forecasting as the discovery and extrapolation of patterns in time ordered data. Revision of descriptive measures for multivariate distributions. Descriptive techniques for time series. Models for stationary processes: derivation of properties. Box-Jenkins approach to forecasting: model identification, estimation, verification. Forecasting using ARIMA and structural models. Forecast assessment. State space models and Kalman Filter. Comparison of procedures. Practical aspects of forecasting. Case studies in forecasting.

**STAT 486: COMP FIN I: MARKET MODELS**

**London School of Economics & Political Science **

**ST226: Actuarial Investigations: Financial**

Introduction to actuarial modelling. The application of compound interest techniques to financial transactions. Describing how to use a generalised cash-model to describe financial transactions such as a zero-coupon bond, a fixed interest security, an index-linked security, cash on deposit, an equity, an interest only loan, a repayment loan, an annuity certain and others. The time value of money using the concepts of compound interest and discounting. Accumulation of payments and present value of future payments. Variable interest rates. The calculation of the present value and the accumulated value of a stream of equal or unequal payments using specified rates of interest and the net present value at a real (possibly variable) rate of interest, assuming a constant rate of inflation. Compound interest rate functions; definitions and use. Equations of value with certain and uncertain payments and receipts. Introduction to life insurance. Life, assurance and annuity functions. Calculating means and variances of contracts with contingent payments.

**STAT 499: QUAN FINANCIAL RISK MANAGEMENT**

**The University of Auckland**

**STATS 370: Financial Mathematics**

STATS 370 is suitable for Finance majors who want to learn more about the more mathematical aspects of the subject and for Statistics or Mathematics majors wanting to learn about Finance. *Topics studied include*: Models for financial returns; pricing of options using binomial models and the Black-Scholes formula; mean-variance portfolio theory; compound interest, annuities,capital redemption policies, valuation of securities, sinking funds; varying rates of interest, taxation; duration and immunisation; introduction to life annuities and life insurance mathematics.