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Exam Code: A00-240 Practice exam 2022 by Killexams.com team
A00-240 SAS Statistical Business Analysis SAS9: Regression and Model

This exam is administered by SAS and Pearson VUE.
60 scored multiple-choice and short-answer questions.
(Must achieve score of 68 percent correct to pass)
In addition to the 60 scored items, there may be up to five unscored items.
Two hours to complete exam.
Use exam ID A00-240; required when registering with Pearson VUE.

ANOVA - 10%
Verify the assumptions of ANOVA
Analyze differences between population means using the GLM and TTEST procedures
Perform ANOVA post hoc test to evaluate treatment effect
Detect and analyze interactions between factors

Linear Regression - 20%
Fit a multiple linear regression model using the REG and GLM procedures
Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
Use the REG or GLMSELECT procedure to perform model selection
Assess the validity of a given regression model through the use of diagnostic and residual analysis

Logistic Regression - 25%
Perform logistic regression with the LOGISTIC procedure
Optimize model performance through input selection
Interpret the output of the LOGISTIC procedure
Score new data sets using the LOGISTIC and PLM procedures

Prepare Inputs for Predictive Model Performance - 20%
Identify the potential challenges when preparing input data for a model
Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
Improve the predictive power of categorical inputs
Screen variables for irrelevance and non-linear association using the CORR procedure
Screen variables for non-linearity using empirical logit plots

Measure Model Performance - 25%
Apply the principles of honest assessment to model performance measurement
Assess classifier performance using the confusion matrix
Model selection and validation using training and validation data
Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
Establish effective decision cut-off values for scoring

Verify the assumptions of ANOVA
 Explain the central limit theorem and when it must be applied
 Examine the distribution of continuous variables (histogram, box -whisker, Q-Q plots)
 Describe the effect of skewness on the normal distribution
 Define H0, H1, Type I/II error, statistical power, p-value
 Describe the effect of trial size on p-value and power
 Interpret the results of hypothesis testing
 Interpret histograms and normal probability charts
 Draw conclusions about your data from histogram, box-whisker, and Q-Q plots
 Identify the kinds of problems may be present in the data: (biased sample, outliers, extreme values)
 For a given experiment, verify that the observations are independent
 For a given experiment, verify the errors are normally distributed
 Use the UNIVARIATE procedure to examine residuals
 For a given experiment, verify all groups have equal response variance
 Use the HOVTEST option of MEANS statement in PROC GLM to asses response variance

Analyze differences between population means using the GLM and TTEST procedures
 Use the GLM Procedure to perform ANOVA
o CLASS statement
o MODEL statement
o MEANS statement
o OUTPUT statement
 Evaluate the null hypothesis using the output of the GLM procedure
 Interpret the statistical output of the GLM procedure (variance derived from MSE, Fvalue, p-value R**2, Levene's test)
 Interpret the graphical output of the GLM procedure
 Use the TTEST Procedure to compare means Perform ANOVA post hoc test to evaluate treatment effect

Use the LSMEANS statement in the GLM or PLM procedure to perform pairwise comparisons
 Use PDIFF option of LSMEANS statement
 Use ADJUST option of the LSMEANS statement (TUKEY and DUNNETT)
 Interpret diffograms to evaluate pairwise comparisons
 Interpret control plots to evaluate pairwise comparisons
 Compare/Contrast use of pairwise T-Tests, Tukey and Dunnett comparison methods Detect and analyze interactions between factors
 Use the GLM procedure to produce reports that will help determine the significance of the interaction between factors. MODEL statement
 LSMEANS with SLICE=option (Also using PROC PLM)
 ODS SELECT
 Interpret the output of the GLM procedure to identify interaction between factors:
 p-value
 F Value
 R Squared
 TYPE I SS
 TYPE III SS

Linear Regression - 20%

Fit a multiple linear regression model using the REG and GLM procedures
 Use the REG procedure to fit a multiple linear regression model
 Use the GLM procedure to fit a multiple linear regression model

Analyze the output of the REG, PLM, and GLM procedures for multiple linear regression models
 Interpret REG or GLM procedure output for a multiple linear regression model:
 convert models to algebraic expressions
 Convert models to algebraic expressions
 Identify missing degrees of freedom
 Identify variance due to model/error, and total variance
 Calculate a missing F value
 Identify variable with largest impact to model
 For output from two models, identify which model is better
 Identify how much of the variation in the dependent variable is explained by the model
 Conclusions that can be drawn from REG, GLM, or PLM output: (about H0, model quality, graphics)
Use the REG or GLMSELECT procedure to perform model selection

Use the SELECTION option of the model statement in the GLMSELECT procedure
 Compare the differentmodel selection methods (STEPWISE, FORWARD, BACKWARD)
 Enable ODS graphics to display graphs from the REG or GLMSELECT procedure
 Identify best models by examining the graphical output (fit criterion from the REG or GLMSELECT procedure)
 Assign names to models in the REG procedure (multiple model statements)
Assess the validity of a given regression model through the use of diagnostic and residual analysis
 Explain the assumptions for linear regression
 From a set of residuals plots, asses which assumption about the error terms has been violated
 Use REG procedure MODEL statement options to identify influential observations (Student Residuals, Cook's D, DFFITS, DFBETAS)
 Explain options for handling influential observations
 Identify collinearity problems by examining REG procedure output
 Use MODEL statement options to diagnose collinearity problems (VIF, COLLIN, COLLINOINT)

Logistic Regression - 25%
Perform logistic regression with the LOGISTIC procedure
 Identify experiments that require analysis via logistic regression
 Identify logistic regression assumptions
 logistic regression concepts (log odds, logit transformation, sigmoidal relationship between p and X)
 Use the LOGISTIC procedure to fit a binary logistic regression model (MODEL and CLASS statements)

Optimize model performance through input selection
 Use the LOGISTIC procedure to fit a multiple logistic regression model
 LOGISTIC procedure SELECTION=SCORE option
 Perform Model Selection (STEPWISE, FORWARD, BACKWARD) within the LOGISTIC procedure

Interpret the output of the LOGISTIC procedure
 Interpret the output from the LOGISTIC procedure for binary logistic regression models: Model Convergence section
 Testing Global Null Hypothesis table
 Type 3 Analysis of Effects table
 Analysis of Maximum Likelihood Estimates table

Association of Predicted Probabilities and Observed Responses
Score new data sets using the LOGISTIC and PLM procedures
 Use the SCORE statement in the PLM procedure to score new cases
 Use the CODE statement in PROC LOGISTIC to score new data
 Describe when you would use the SCORE statement vs the CODE statement in PROC LOGISTIC
 Use the INMODEL/OUTMODEL options in PROC LOGISTIC
 Explain how to score new data when you have developed a model from a biased sample
Prepare Inputs for Predictive Model

Performance - 20%
Identify the potential challenges when preparing input data for a model
 Identify problems that missing values can cause in creating predictive models and scoring new data sets
 Identify limitations of Complete Case Analysis
 Explain problems caused by categorical variables with numerous levels
 Discuss the problem of redundant variables
 Discuss the problem of irrelevant and redundant variables
 Discuss the non-linearities and the problems they create in predictive models
 Discuss outliers and the problems they create in predictive models
 Describe quasi-complete separation
 Discuss the effect of interactions
 Determine when it is necessary to oversample data

Use the DATA step to manipulate data with loops, arrays, conditional statements and functions
 Use ARRAYs to create missing indicators
 Use ARRAYS, LOOP, IF, and explicit OUTPUT statements

Improve the predictive power of categorical inputs
 Reduce the number of levels of a categorical variable
 Explain thresholding
 Explain Greenacre's method
 Cluster the levels of a categorical variable via Greenacre's method using the CLUSTER procedure
o METHOD=WARD option
o FREQ, VAR, ID statement

Use of ODS output to create an output data set
 Convert categorical variables to continuous using smooth weight of evidence

Screen variables for irrelevance and non-linear association using the CORR procedure
 Explain how Hoeffding's D and Spearman statistics can be used to find irrelevant variables and non-linear associations
 Produce Spearman and Hoeffding's D statistic using the CORR procedure (VAR, WITH statement)
 Interpret a scatter plot of Hoeffding's D and Spearman statistic to identify irrelevant variables and non-linear associations Screen variables for non-linearity using empirical logit plots
 Use the RANK procedure to bin continuous input variables (GROUPS=, OUT= option; VAR, RANK statements)
 Interpret RANK procedure output
 Use the MEANS procedure to calculate the sum and means for the target cases and total events (NWAY option; CLASS, VAR, OUTPUT statements)
 Create empirical logit plots with the SGPLOT procedure
 Interpret empirical logit plots

Measure Model Performance - 25%
Apply the principles of honest assessment to model performance measurement
 Explain techniques to honestly assess classifier performance
 Explain overfitting
 Explain differences between validation and test data
 Identify the impact of performing data preparation before data is split Assess classifier performance using the confusion matrix
 Explain the confusion matrix
 Define: Accuracy, Error Rate, Sensitivity, Specificity, PV+, PV-
 Explain the effect of oversampling on the confusion matrix
 Adjust the confusion matrix for oversampling

Model selection and validation using training and validation data
 Divide data into training and validation data sets using the SURVEYSELECT procedure
 Discuss the subset selection methods available in PROC LOGISTIC
 Discuss methods to determine interactions (forward selection, with bar and @ notation)

Create interaction plot with the results from PROC LOGISTIC
 Select the model with fit statistics (BIC, AIC, KS, Brier score)
Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
 Explain and interpret charts (ROC, Lift, Gains)
 Create a ROC curve (OUTROC option of the SCORE statement in the LOGISTIC procedure)
 Use the ROC and ROCCONTRAST statements to create an overlay plot of ROC curves for two or more models
 Explain the concept of depth as it relates to the gains chart

Establish effective decision cut-off values for scoring
 Illustrate a decision rule that maximizes the expected profit
 Explain the profit matrix and how to use it to estimate the profit per scored customer
 Calculate decision cutoffs using Bayes rule, given a profit matrix
 Determine optimum cutoff values from profit plots
 Given a profit matrix, and model results, determine the model with the highest average profit

SAS Statistical Business Analysis SAS9: Regression and Model
SASInstitute Statistical course outline
Killexams : SASInstitute Statistical course outline - BingNews https://killexams.com/pass4sure/exam-detail/A00-240 Search results Killexams : SASInstitute Statistical course outline - BingNews https://killexams.com/pass4sure/exam-detail/A00-240 https://killexams.com/exam_list/SASInstitute Killexams : Statistical & Data Sciences

The program is designed to produce highly skilled, versatile statisticians and data scientists who possess powerful abilities for analyzing data. As such, SDS students learn not only how to build statistical models that generate predictions, but how to validate these models and interpret their parameters. Students learn to use their ingenuity to “wrangle” with complex data streams and construct informative data visualizations.

The major in statistical & data sciences consists of 10 courses, including depth in both statistics and computer science, an integrating course in data science, a course that emphasizes communication and an application domain of expertise. All but the application domain course must be graded; the application course can be taken S/U.

Advisers
Benjamin Baumer, Randi Garcia, Albert Y. Kim, Katherine Kinnaird, Scott LaCombe, Lindsay Poirier. If you wish to declare an SDS major and need an advisor, please fill out this form at https://bit.ly/sds_advisor.

Study Abroad Adviser
Scott LaCombe

Requirements

See the major diagram below for prerequisites, and see the Note on course substitutions following the description of the major.

Foundations and Core (5 courses): The following required courses build foundational skills in mathematics, statistics and computer science that are necessary for learning from modern data.
- SDS 201 or SDS 220: Introductory Statistics
- SDS 291: Multiple Regression
- CSC 110: Introduction to Computer Science or CSC 111: Intro to Programming
- SDS 192: Intro to Data Science
- MTH 211: Linear Algebra
Statistical Depth (1 course): One additional course that provides exposure to additional statistical models.
- SDS 290: Research Design and Analysis
- SDS 293: Modeling for Machine Learning
- MTH/SDS 320: Mathematical Statistics
- SDS 390: courses in SDS. Offerings may vary; previous versions of this course include:
  - Bayesian Statistics
  - Ecological Forecasting
  - Structural Equation Modeling
  - Statistical Analysis of Social Networks
Programming Depth (1 course): One additional course that deepens exposure to programming.
- CSC 120: Object Oriented Programming
- CSC 151: Programming Languages
- CSC 210: Data Structures and Basic Algorithms
- CSC 212: Data Structures
- CSC 220: Advanced Programming Techniques
- CSC/SDS 235: Visual Analytics (must take programming intensive track)
- CSC 240: Computer Graphics
- SDS 270: Advanced Programming for Data Science
- CSC 294: Computational Machine Learning
- CSC/SDS 352: Parallel & Distributed Computing
Communication (1 course): One course that focuses on the ability to communicate in written, graphical and/or oral forms in the context of data.
- CSC/SDS 109: Communicating with Data
- FYS 105: Ethics of Big Data
- FYS 189: Data and Social Justice
- CSC/SDS 235: Visual Analytics
- SDS 236: Data Journalism
- SDS 237: Data Ethnography

Application Domain (1 course): Every student is required to take a course that allows them to conduct a substantial data analysis project evaluated by an expert in a specific domain of application.
Please consult our continuously-updated, nonexhaustive list of previously approved application domain courses, which includes:
- SDS 300: Applications of Statistical & Data Sciences
- Dual-prefixed research seminars offered by SDS:
  - GOV/SDS 338: Research Seminar in Political Networks
  - CSC/SDS 354: Seminar: Music Information Retrieval
  - PSY/SDS 364: Research Seminar on Intergroup Relationships
- Research seminars (normally 300-level) or special studies of at least two credits. Normally, the domain would be outside of mathematics, statistics and computer science.
- Departmental honors theses in another major (normally not MTH or CSC)

A student and their adviser should identify potential application domains of interest as early as possible, since many suitable courses will have prerequisites. Normally, this should happen during the fourth semester or at the time of major declaration, whichever comes first. The determination of whether a course satisfies the requirement will be made by the student’s major adviser.

Capstone (1 course): Every student is required to complete a capstone experience, which exposes them to real-world data analysis challenges.
Electives: (as needed to complete to 10 courses): Provided that the requirements listed above are met, any of the courses listed above may be counted as electives to reach the 10 course requirement. Five College courses in statistics and computer science may be taken as electives. Additionally, the following courses may be counted toward completion of the major:
- MTH 246: Probability
- CSC 230: Introduction to Database Systems
- CSC 252: Algorithms
- CSC 256: Intelligent User Interfaces
- CSC 290: Artificial Intelligence
- CSC 330: Database Systems
- CSC 390: Seminar on Artificial Intelligence

Notes on course substitutions:

CSC 110 or 111 may be replaced by a 4 or 5 on the AP computer science exam.
SDS 220 or SDS 201 may be replaced by a 4 or 5 on the AP statistics exam. Replacement by AP courses does not diminish the total number of courses required for either the major or the minor (see Electives above). Any one of ECO 220, GOV 203, PSY 201, or SOC 204 may directly substitute for SDS 220 or SDS 201 without the need to take another course, in both the major and minor. Note that SDS 220 and ECO 220 require Calculus. Students should be aware that substituting for SDS 220 or SDS 201 could leave them without R programming experience, which is needed in subsequent courses, such as SDS 290 & 291.
MTH 211 may be replaced by petition in exceptional circumstances.
Five-College equivalents may substitute with permission of the program.
SDS 107 and EDC 206 are important courses but do not count for the major or the minor.
An Honors Thesis (SDS 430D) generally cannot substitute for the capstone SDS 410.

The Major in Mathematical Statistics

Students interested in doctoral programs in Statistics should consider the Major in Mathematical Statistics jointly operated by SDS and MTH.

Sun, 10 Jul 2022 15:34:00 -0500 en text/html https://www.smith.edu/academics/statistics Killexams : Fundamentals of Statistical Analysis

Prerequisites: No prerequisites, prior empirical or software background is not required for this course. This class can be taken as part of the HLS empirical track as a prerequisite for the Applied Quantitative Analysis and the Advanced Quantitative Analysis.

Exam Type: No Exam

Intended for law students with little or no background in mathematics and statistics, Fundamentals of Statistical Analysis will provide basic tools needed for designing, conducting and critically assessing empirical legal research. courses include research design, introduction to probability, descriptive statistics, hypothesis testing, statistical inference, univariate and bivariate analysis using one and two-sample t-tests, z-tests, Chi2 and ANOVA. We will learn and practice the math behind the models, to understand how distributions, differences, choice and size of samples impact our results mathematically as well as theoretically. The course is hands-on and applied in nature, it provides students an opportunity to become proficient in the use of the Stata statistical software. Applying mathematical concepts on real data enables students to acquire analytic skills in a realistic research context, which helps understanding not only how data are analyzed, but also why they are analyzed. Students will produce a 10-12 page empirical paper at the end of the semester.

Fri, 15 Apr 2022 01:16:00 -0500 en-us text/html https://hls.harvard.edu/courses/fundamentals-of-statistical-analysis/ Killexams : Introduction to Statistics

This course aims to introduce the basic statistical concepts and methods commonly used in medical and public health research.

Prerequisites

Please ensure you meet the following prerequisites before booking:

Software

Please will students ensure they have access to statistics software before the start of the course. Full support will be given for the use of Stata* in the practical sessions. Students are welcome to use a different package (e.g. R, SAS, SPSS) if they are confident in its use, but we cannot guarantee we will be able to help if difficulties are encountered.

*Internal University of Bristol participants are given access to Stata. Go to Stata Installation Instructions (internal only) for help setting it up before the start of the course.

Introduction

The field of statistics is a fundamental cornerstone of medical and epidemiological research, playing a key role in improving scientific understanding and developing successful health policy. This short course will provide students with a thorough grounding in the understanding and application of statistics.

Course format

All teaching for this 5-day course will be conducted online using Blackboard. It will consist of synchronous and asynchronous lectures, small group work, discussions, individual tasks, and computing practicals. Participants will be able to complete the computing practicals in breakout rooms while working on their own computer.

Course objectives

By the end of the course participants should be able to:

appreciate the role of statistical methods in epidemiology and public health;
present quantitative data using appropriate displays, tabulations and summaries;
appreciate the nature of sampling variation and the role of statistical methods in quantifying variation, setting confidence limits, and testing hypotheses;
select and use appropriate statistical methods in the analysis of simple datasets;
understand and interpret output from statistical analyses; and
present findings based on statistical analysis in a clear, concise and understandable manner.

Who the course is intended for

This course is intended for those who require a grounding in the common statistical methods used in medical research, which will provide a firm foundation for further learning. Guidance will be provided on the use of Stata for the practical exercises, but students are free to use their preferred package.

Course outline

Students will learn a range of statistical techniques for analysing quantitative data. Students will learn how to plan their own statistical analyses including formulating the study hypotheses; defining, summarising and displaying data; using confidence intervals and p-values for statistical inference; describing the association between two variables; and exploring the implications of different study designs for the analysis of the resulting data. The course combines a mix of theory and application, ensuring that students have both a thorough understanding of the application of statistics and the skills necessary to compute them. Students will have the opportunity to interpret and discuss with course tutors quantitative results in previously published papers, and their own statistical analysis plans. Examples will be drawn from epidemiology and public health, but the core techniques covered on the course have application across a diverse range of disciplines and fields.

Sat, 14 Aug 2021 06:03:00 -0500 en text/html https://www.bristol.ac.uk/medical-school/study/short-courses/2021-22-courses/introduction-to-statistics/ Killexams : A First Course in Statistical Programming with R

Crossref Citations

This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

Tindale, E. and Chapman, S. C. 2017. Solar Wind Plasma Parameter Variability Across Solar Cycles 23 and 24: From Turbulence to Extremes. Journal of Geophysical Research: Space Physics, Vol. 122, Issue. 10, p. 9824.

Tindale, E. Chapman, S.C. Moloney, N.R. and Watkins, N.W. 2018. The Dependence of Solar Wind Burst Size on Burst Duration and Its Invariance Across Solar Cycles 23 and 24. Journal of Geophysical Research: Space Physics, Vol. 123, Issue. 9, p. 7196.

L. Jockers, Matthew and Thalken, Rosamond 2020. Text Analysis with R. p. 3.

Bergin, A. Chapman, S. C. and Gjerloev, J. W. 2020. A E , D S T , and Their SuperMAG Counterparts: The Effect of Improved Spatial Resolution in Geomagnetic Indices . Journal of Geophysical Research: Space Physics, Vol. 125, Issue. 5,

Ranjha, Ali and Kaddoum, Georges 2022. URLLC-Enabled by Laser Powered UAV Relay: A Quasi-Optimal Design of Resource Allocation, Trajectory Planning and Energy Harvesting. IEEE Transactions on Vehicular Technology, Vol. 71, Issue. 1, p. 753.

Fri, 12 Feb 2021 12:16:00 -0600 en text/html https://www.cambridge.org/core/books/first-course-in-statistical-programming-with-r/2160702CBAAC2D3CE9204EC5D46260BA Killexams : Statistical Modelling

161304

This course covers the ideas underlying statistical modelling, its implementation through computational methods, and links to practical applications. courses include probability and random variables, models for discrete and continuous data, model selection, model fitting and goodness of fit, model inference, and introduction to stochastic processes.

Mon, 21 Mar 2022 08:43:00 -0500 en-NZ text/html https://www.massey.ac.nz/study/courses/statistical-modelling-161304/ Killexams : Statistical Consulting

Course planning information

Course notes

Students must contact course coordinator prior to enrolment. Access to an approved statistics package is required for analysis of data.

Expected prior learning

Students should be familiar with a broad range of statistical techniques.

General progression requirements

You may enrol in a postgraduate course (that is a 700-, 800- or 900-level course) if you meet the prerequisites for that course and have been admitted to a qualification which lists the course in its schedule.

1 Undertake statistical consulting on their own.
2 Critique the statistical content of published papers.
3 Demonstrate familiarity with the literature on statistical consulting.

Learning outcomes can change before the start of the semester you are studying the course in.

Assessments

Assessment weightings can change up to the start of the semester the course is delivered in.

You may need to take more assessments depending on where, how, and when you choose to take this course.

Explanation of assessment types

Computer programmes: Computer animation and screening, design, programming, models and other computer work.

Creative compositions: Animations, films, models, textiles, websites, and other compositions.

Exam College or GRS-based (not centrally scheduled): An exam scheduled by a college or the Graduate Research School (GRS). The exam could be online, oral, field, practical skills, written exams or another format.

Exam (centrally scheduled): An exam scheduled by Assessment Services (centrally) – you’ll usually be told when and where the exam is through the student portal.

Oral or performance or presentation: Debates, demonstrations, exhibitions, interviews, oral proposals, role play, speech and other performances or presentations.

Participation: You may be assessed on your participation in activities such as online fora, laboratories, debates, tutorials, exercises, seminars, and so on.

Portfolio: Creative, learning, online, narrative, photographic, written, and other portfolios.

Practical or placement: Field trips, field work, placements, seminars, workshops, voluntary work, and other activities.

Simulation: Technology-based or experience-based simulations.

Test: Laboratory, online, multi-choice, short answer, spoken, and other tests – arranged by the school.

Written assignment: Essays, group or individual projects, proposals, reports, reviews, writing exercises, and other written assignments.

Tue, 29 Mar 2022 01:02:00 -0500 en-NZ text/html https://www.massey.ac.nz/study/courses/statistical-consulting-161770/ Killexams : Mathematical Sciences

Consult the Smith College Course Catalog for information on the current courses available in mathematics and statistics.

There are also several courses that are available for credit from other departments, including art, psychology and more. Consult the catalog.

What classes you should take depends a great deal on what you find most interesting and on what your goals are. Discuss your options with your adviser and also talk to the instructors of particular courses that interest you.

If you are interested in the sciences

The department offers a variety of courses to provide you a solid mathematical experience. Calculus III and Linear Algebra are fundamental courses. You may also want to consider taking one or more of the following: Intro to Probability and Statistics, Differential Equations, Differential Equations and Numerical Methods, Discrete Mathematics, Advanced courses in Continuous Applied Mathematics.

If you are interested in computer science

Consider taking some of these: Calculus III, Linear Algebra, Modern Algebra, Discrete Mathematics. Many of our students are double–majoring in mathematics and computer science.

If you are interested in economics

Calculus will provide you a good, basic experience. You may consider other courses as well, so be sure to discuss your options with your adviser. If you are contemplating graduate school in economics, the economics department recommends you to take MTH 211, 212, 280 and 281. Taking a solid course in statistics is also a good idea (any of MTH 220, 246, 290, 291 and 320 would do). Many economics majors want to take MTH 264 as well. Double–majoring in mathematics and economics is a good choice.

If you are interested in applied mathematics

The following courses work specifically with applications: MTH 205, 264, 353 and 364. Other courses that contain many applications and are important for anyone considering graduate school in applied mathematics are: MTH 220, 246, 254, 255, 280, 290, 291, and 320.

If you are interested in theoretical mathematics

The following courses work with abstract structures: MTH 233, 238, 246, 254, 255, 280, 281, 333, 370, 381, and 382.

If you liked calculus

There are many reasons for liking calculus. If you delighted in the geometry, for example, you should consider MTH 270, 280, 370 and 382. If you enjoyed the power of calculus to describe and understand the world, you will want to take MTH 264. If you are fascinated with the ideas of limit and infinity and want to get to the bottom of them, you should take MTH 281.

If you liked linear algebra

You will like MTH 233 very much, and you will also like MTH 238 and 333.

If you liked discrete mathematics

The natural sequel to Discrete Mathematics is MTH 254 or 255 and then 353. In addition, you may be interested in MTH 246 and in CSC 252 (counts 2 credits toward the mathematics major).

If you are interested in graduate school in mathematics

Take a lot of courses, but be sure to take MTH 233, 254, and 281 and as many of MTH 264, 333, 370, 381, and 382 as possible. You should also consider taking a graduate course at the University of Massachusetts.

If you are interested in graduate school in statistics

The MST Mathematical Statistics joint Major between MTH and SDS is explicitly designed as a preparation for graduate school in Statistics.

If you are interested in graduate school in operations research

Operations research is a relatively new subarea of mathematics, bringing together mathematical ideas and techniques that are applied to large organizations such as businesses, computers, and governments. You should take MTH 211 and at least some of the courses listed for statistics above, some combinatorics (MTH 254) and some computer science. Consider also courses in Applied Mathematics and Numerical Analysis.

If you want to be a teacher

Certification requirements vary widely from state–to–state. If you are interested in teaching in secondary school, a mathematics major plus practice teaching may be enough to get started. In Massachusetts, the major should include either MTH 233 or 238 and one of MTH 220 or 246. A course involving geometry, such as MTH 270 or MTH 370 is also helpful. You should also have some introduction to computers. For guidelines, look at the list of courses listed in the MAT program. Finally, while MTH 307 courses in Mathematics Education is rarely offered, something equivalent is taught as a special studies whenever there are MAT students.

If you are interested in teaching elementary school, most of your required courses will be in the education department. In the mathematics department, our concern would be that you are comfortable with mathematics, have seen its variety, and most important, that you enjoy it. For all that, you should take the mathematics courses which appeal to you most. For education courses, the latest information is that you should take EDC 235, 238, 346, 347, 404 (practice teaching), and one elective to be certified. Note that during the semester when you take practice teaching EDC 404, you will likely be unable to take a math course. Plan ahead and consult the education department.

If you want to be a doctor

You are doing fine by majoring in mathematics. A course in statistics would be a very good idea. Other areas of mathematics that would be useful are differential equations and combinatorics.

If you want to be an actuary

Take MTH 246, 290, 291 and 320 and the actuarial exams that are offered periodically. Advancement as an actuary is achieved by passing of a series of examinations. Informal student study groups often form (ask around!).

If you want to get a good job when you graduate

A major in mathematics prepares you well, regardless of which courses you choose. Math majors learn to think on their feet; they aren't frightened of numbers and they're at home with abstract ideas. Often, this alone is what employers are looking for. That said, we should add that knowledge of computer programming is very useful, as is some familiarity with statistics.

If you want something Smith does not offer

If you are interested in a subject we do not offer, you should talk to professors whose fields of interest are closest to the subject, as a special studies. The arrangement must be approved by the department, but reasonable requests are not refused. If your interest is particularly strong, you might consider an honors project, or summer research work. You should also consider taking a course (or courses) at one of the consortium schools.

Sun, 04 Dec 2022 13:53:00 -0600 en text/html https://www.smith.edu/academics/mathematical-sciences Killexams : Elementary Statistical Theory I

This information is for the 2022/23 session.

This course is compulsory on the BSc in Econometrics and Mathematical Economics and BSc in Economics. This course is available as an outside option to students on other programmes where regulations permit and to General Course students.

This course cannot be taken with ST102 Elementary Statistical Theory or ST107 Quantitative Methods (Statistics).

A-level Mathematics.

No previous knowledge of statistics is assumed.

The course provides a precise and accurate treatment of introductory probability and distribution theory. courses covered are data visualisation and descriptive statistics, probability theory, random variables, common distributions of random variables and multivariate random variables.

22 hours of lectures, 15 hours of classes and 10 hours of workshops in the MT.

This course will be delivered through a combination of classes, lectures and workshops totalling a minimum of 45 hours in Michaelmas Term. This course does not include a practicing week.

Weekly exercises will be set and students are expected to submit solutions to their class teacher each week for feedback.

All course materials are made available via Moodle, including notes to accompany the lectures, but this can be supplemented with additional background reading. The recommended supplementary text is:

Larsen R.J. and M.L. Marx (2017) Introduction to Mathematical Statistics and Its Applications (sixth edition), Pearson (earlier editions are also fine).

Exam (100%, duration: 2 hours, practicing time: 10 minutes) in the January exam period.

Fri, 17 Dec 2021 08:08:00 -0600 text/html https://www.lse.ac.uk/resources/calendar2022-2023/courseGuides/ST/2022_ST109.htm Killexams : Data Analytics and Social Statistics

This is a flexible, online programme designed to fit around your existing commitments. There are 20 hours of study per week to take when it suits you. We have an extensive array of tools in our virtual learning environment (VLE) including videos, interactive workbooks, self-tests, online tutorials and online assessment.

You will also get to participate in events such as seminars with experts from leading organisations and engagement sessions with your course colleagues. In these sessions, you will have the chance to collaborate and build your network.

Our course academics are world-leading specialists in social science and research, with professional backgrounds analysing data across different disciplines.

Library services

As a student with The University of Manchester, you will be able to use our extensive library services. This will grant you access to books, e-books and journals about social statistics, quantitative data analysis and research, and data science, from introductory to advanced levels.

You will be assigned a dedicated Study Support Advisor who will be your first point of contact for study-related questions and help with the VLE.

Sun, 02 Oct 2022 02:17:00 -0500 en text/html https://www.manchester.ac.uk/study/online-blended-learning/courses/data-analytics-and-social-statistics/ Killexams : MSc Statistics

Overview

Degree awarded

MSc

Duration

12 months full time

Entry requirements

The normal entry requirements are a good upper second or first class honours degree from a UK university, or the equivalent from an overseas university, in Mathematics or in a subject with some significant mathematical content. Students should also have some knowledge of Probability and Statistics in their degree, at least up to what might be expected by the end of the second year of an undergraduate Mathematics degree.

Expected Background

You are expected to have a first degree with a substantial amount of mathematics including Probability and Statistics. As a minimum you should have done Calculus or Mathematical Analysis, Linear Algebra, two courses in Probability and two courses in Statistics. A Mathematical Statistics course may count as one Probability and one Statistics course depending on the syllabus. If your course is called Advanced Mathematics, then we need to know how much calculus/linear algebra it contains. You can have a look at what Manchester students do in the first two years, or refer to the following list for a quick summary.

Calculus or Mathematical Analysis (functions of a single and several variables, continuity, derivatives, integrals, Mean Value Theorem, Taylor series expansion, minimisation and maximisation, Lagrange multipliers)
Linear Algebra (linear independence, determinant, inverse, eigenvalues and eigenvectors)
Probability I (probabilities and conditional probabilities, Bayes Theorem, moments)
Probability II (multivariate and conditional distributions, generating functions, Law of Large Numbers and Central Limit Theorem)
Statistics I (descriptive statistics, normal, t, chi-square and F distributions, significance tests)
Statistics II (Maximum likelihood estimation, Likelihood ratio tests, simple regression and analysis of variance).

You are expected to have done well in the above courses and your university should have a high national standing.

Full entry requirements

How to apply

Apply online .

As there is high demand for this course we operate a staged admissions process with selection deadlines throughout the year. Due to the competition for places, we provide preference to students with grades above our minimum entry requirements. If we make you an offer, you will have 3 weeks in which to accept. Any offers not accepted within 3 weeks will be withdrawn so that an offer can be made to another candidate.

Please find more information in our Application and Selection section .

Course options

	Full-time	Part-time	Full-time distance learning	Part-time distance learning
MSc	Y	Y	N	N

Course description

The Probability and Statistics group in the Department of Mathematics have a long-standing reputation and experience of offering one year, high quality taught courses in areas of Statistics leading to the degree of MSc. These courses have aimed to offer a thorough professional training which prepare students to embark on statistical careers in a variety of areas. (There is a shortage of statisticians trained to postgraduate level in the UK and the employment prospects for such people remain good.) They have also provided a very good foundation for further study at PhD level.

Our current MSc programme in Statistics allows students to take a common core of five modules and an additional set of three specialist/additional modules depending on their interests and career aspirations.

Open days

For details of the next University Postgraduate open day, visit open days and visits

Fees

For entry in the academic year beginning September 2023, the tuition fees are as follows:

MSc (full-time)
UK students (per annum): £14,500
International, including EU, students (per annum): £30,500
MSc (part-time)
UK students (per annum): £7,250
International, including EU, students (per annum): £15,500

Further information for EU students can be found on our dedicated EU page.

The fees quoted above will be fully inclusive for the course tuition, administration and computational costs during your studies.

All fees for entry will be subject to yearly review and incremental rises per annum are also likely over the duration of courses lasting more than a year for UK/EU students (fees are typically fixed for International students, for the course duration at the year of entry). For general fees information please visit: postgraduate fees . Always contact the department if you are unsure which fee applies to your qualification award and method of attendance.

Self-funded international applicants for this course will be required to pay a deposit of £1000 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. This deposit will only be refunded if immigration permission is refused. We will notify you about how and when to make this payment.

Policy on additional costs

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

For more information, see the Department of Mathematics Fees and funding page or visit the University of Manchester funding for master's courses website for more information.

Courses in related subject areas

Use the links below to view lists of courses in related subject areas.

Entry requirements

Academic entry qualification overview

Expected Background

Calculus or Mathematical Analysis (functions of a single and several variables, continuity, derivatives, integrals, Mean Value Theorem, Taylor series expansion, minimisation and maximisation, Lagrange multipliers)
Linear Algebra (linear independence, determinant, inverse, eigenvalues and eigenvectors)
Probability I (probabilities and conditional probabilities, Bayes Theorem, moments)
Probability II (multivariate and conditional distributions, generating functions, Law of Large Numbers and Central Limit Theorem)
Statistics I (descriptive statistics, normal, t, chi-square and F distributions, significance tests)
Statistics II (Maximum likelihood estimation, Likelihood ratio tests, simple regression and analysis of variance).

You are expected to have done well in the above courses and your university should have a high national standing.

English language

All applicants will need to demonstrate competency in English language. Applicants who do not already possess an acceptable English Language qualification will need to take a recognised test and attain the required English Language score:

IELTS: at least 6.5 overall with 6.5 in writing and no other sub-test less than 6.0.
TOEFL iBT: at least 90 overall with 22 or above in writing and no other sub-test less than 20. We do not accept 'MyBestScore'.
Pearson PTE: at least 70 overall with 70 in writing and no other sub-test below 65.

Further information on language requirements can be found on our website .

Pre-sessional English

We will consider applicants who do not meet these scores but you may be required to complete a pre-sessional English language course at the University of Manchester prior to the start of the course.

To be considered for a pre-sessional English language course for this programme we require the following minimum IELTS (Academic) scores:

6 Week Pre-Sessional Course: Minimum 6.0 overall with 6.0 in three sub-tests including writing and at least 5.5 in the remaining sub-test.

If you have not yet completed your current academic study and are interested in studying a pre-sessional course, you must hold an IELTS for UKVI (Academic) test certificate.

English language test validity

Some English Language test results are only valid for two years. Your English Language test report must be valid on the start date of the course.

Other international entry requirements

We accept a range of qualifications from different countries. For these and general requirements including English language see entry requirements from your country .

Application and selection

How to apply

Apply online .

Please find more information in our Application and Selection section .

Staged admissions

Stage 1 : Application received by 11 November 2022; Decision by 13 January 2023 .

Stage 2 : Application received by 6 January 2023; Decision by 10 March 2023 .

Stage 3 : Application received by 3 March 2023; Decision by 21 April 2023 .

Stage 4 : Application received by 26 May 2023; Decision by 23 June 2023 .

You need to ensure that you submit your supporting documents with your application as it may delay us processing your application before the decision deadline.

Whilst we aim to provide you a decision on your application by the deadline date, in some instances due to the competition for places and the volume of applications received, it may be necessary to roll your application forward to the next deadline date. If this is the case we will let you know after the deadline date.

Applications received after our final selection deadline will be considered at our discretion if places are still available.

Please note: All places are subject to availability and if you apply at one of the later stages, some courses may already be closed. We therefore recommend that you apply early in the cycle to secure your place with us.

Applicants who are made a conditional offer of a place must demonstrate that they have met all the conditions of their offer by 31st July 2023.

Advice to applicants

We require the following documents before we can consider your application.

Latest transcripts of study showing all modules completed including credit weighting and grades achieved (and English translation if applicable).
If you have not yet graduated, a list of the modules that you will be completing in the final year of your degree together with their credit weighting.
An official document from your university verifying your current weighted average mark if this information is not included in your transcript of study. Please note : this must be recorded as a percentage, not as a GPA grade, and must provide the weighted average mark, not an arithmetic average mark.
We require references from two people who have knowledge of your academic ability in support of your application, although we can process your application with one academic reference. In most cases, these should be academic references, from a lecturer or professor at your last university. In some cases (for example, if your academic studies occurred some time ago), it might be more appropriate to submit recommendations from those familiar with your professional experience. If you have difficulty in identifying suitable referees you should ask for advice from the admissions team for your course. We will contact your referees directly after you submit your application and direct them to complete our online reference form.
Degree certificate if you have already graduated (and English translation if applicable).
Curriculum vitae (CV) if you graduated more than three years ago.
Personal Statement.
If English is not your first language, we also require proof of your English language ability. If you have already taken an English language qualification, please include your certificate with your application. We may be willing to consider your application without this document, but if we choose to make you an offer, the conditions will include IELTS (or equivalent qualification).

You must submit all these supporting documents with your application. If any of the above information is missing, we will not be able to consider your application and it may be rolled forward to the next stage or withdrawn.

How your application is considered

We consider your full academic history including which course units you have taken and the marks obtained. Even if you have met our minimum entry requirements, we will take into account your marks in relevant course units in our final decision making.

If you graduated more than three years ago, we will also consider the information contained on your CV and any relevant work experience you have to assess if you are still able to fulfil the entry criteria.

Interview requirements

No interview is required for this course.

Overseas (non-UK) applicants

CAS Information

Self-funded international applicants for this course will be required to pay a deposit of £1,000 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. This deposit will only be refunded if immigration permission is refused. We will notify you about how and when to make this payment.

Please upload a copy of your current valid passport with your application showing the photograph page with your application. For CAS purposes, this must show your full name, date of birth, nationality, passport number and the date the passport is valid until, which must be later than the date of your planned arrival in the UK, and the start date of your course.

If you have previously studied in the UK on a Tier 4 visa as an undergraduate or postgraduate student, please send a copy of your previous CAS statement to us as it will assist with the issue of your new CAS statement. This includes study in the UK on study abroad programmes and any study that you did not complete.

You cannot use your CAS to apply for a visa more than three months before the start date of your course. The Admissions Team will contact you at the appropriate time.

Your CAS number is only valid for one Tier 4 application.

Deferrals

Applications for deferred entry are not accepted for this course. If you receive an offer for 2023 entry and decide not to accept it, should you subsequently wish to be considered for 2024 entry you would be required to reapply.

Re-applications

If you applied in the previous year and your application was not successful you may apply again. Your application will be considered against the standard course entry criteria for that year of entry. In your new application you should demonstrate how your application has improved. We may draw upon all information from your previous applications or any previous registrations at the University as a student when assessing your suitability for your chosen course.

Course details

Course description

Coursework and assessment

There are two teaching semesters of 12 weeks each and approximately 15 weeks of dissertation work. Assessment for the taught part is by exams and coursework. Following the successful completion of the taught part (worth a total of 120 credits) students are then expected to work on a dissertation from June to September which is worth a further 60 credits, making 180 credits in total. Information on the various courses and projects which will be available for dissertation are provided to the students in May from which they are invited to state their preferences.

Course unit details

The taught part of the programme is divided into two 12-week semesters, each followed by a two- or three-week period of examinations. This in turn is followed by a period of approximately 12 weeks of research work over the summer which is supervised by a member of academic staff and ends with submission of the MSc dissertation in September. Full-time students attend weekly lectures and support classes for four modules (4 x 15 credits) in each semester. Students can also enrol on a part-time basis. In this case they study over a two year period and only take two modules per semester, with the dissertation being completed at the end of the second year. Details of the programme structure are given below, which are subject to review and continual improvement.

Main Programme

Semester One:

Linear Models with Nonparametric Regression
Statistical Computing
Statistical Inference
Multivariate Statistics

Semester Two:

Generalized Linear Models and Survival Analysis
Longitudinal Data Analysis
Markov Chain Monte Carlo (MCMC)
Design and Analysis of Experiments

This degree programme is accredited by the Royal Statistical Society.

Accreditation by the Royal Statistical Society (RSS) provides reassurance that our MSc programme produces graduates with the technical skills and subject knowledge required of a statistician. This provides our graduates with a competitive edge in the job market and provides employers with an assurance of quality of our degree.

Dissertation: Following the successful completion of the taught part of the programme (worth a total of 120 credits) students are then expected to work on a dissertation from June to September which is worth a further 60 credits, making 180 credits in total. Information on the various courses and projects which will be available for dissertation are provided to the students in May from which they are invited to state their preferences.

Course unit list

The course unit details given below are subject to change, and are the latest example of the curriculum available on this course of study.

Facilities

The Department of Mathematics is the largest in the UK with an outstanding research reputation and facilities .

Disability support

Practical support and advice for current students and applicants is available from the Disability Advisory and Support Service. Email: dass@manchester.ac.uk

Careers

Career opportunities

This programme will prepare students for a broad range of statistical careers, particularly in the financial, medical, pharmaceutical and industrial sectors of the economy, but also with local and national government agencies, as well as in other areas. They will also provide an excellent foundation for students wishing to pursue advanced postgraduate research in statistics.

Accrediting organisations

This degree programme is accredited by the Royal Statistical Society.

Thu, 10 Nov 2022 10:00:00 -0600 en text/html https://www.manchester.ac.uk/study/masters/courses/list/02362/msc-statistics/all-content/ A00-240 exam dump and training guide direct download
Training Exams List