A00-240 availability - SAS Statistical Business Analysis SAS9: Regression and Model Updated: 2023 | ||||||||
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Exam Code: A00-240 SAS Statistical Business Analysis SAS9: Regression and Model availability November 2023 by Killexams.com team | ||||||||
A00-240 SAS Statistical Business Analysis SAS9: Regression and Model This test 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 test 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 demo 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 availability | ||||||||
Other SASInstitute examsA00-240 SAS Statistical Business Analysis SAS9: Regression and ModelA00-250 SAS Platform Administration for SAS9 A00-280 Clinical Trials Programming Using SAS 9 | ||||||||
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| A00-240 Dumps A00-240 Braindumps A00-240 Real Questions A00-240 Practice Test A00-240 dumps free SASInstitute A00-240 SAS Statistical Business Analysis SAS9: Regression and Model http://killexams.com/pass4sure/exam-detail/A00-240 Question #87 What is a benefit to performing data cleansing (imputation, transformations, etc.) on data after partitioning the data for honest assessment as opposed to performing the data cleansing prior to partitioning the data? A. It makes inference on the model possible. B. It is computationally easier and requires less time. C. It omits the training (and test) data sets from the benefits of the cleansing methods. D. It allows for the determination of the effectiveness of the cleansing method. Answer: D Question #88 A researcher has several variables that could be possible predictors for the final model. There is interest in checking all 2-way interactions for possible entry to the model. The researcher has decided to use forward selection within PROC LOGISTIC. Fill in the missing code option that will ensure that all 2-way interactions will be considered for entry. A. start = 5 B. include = 4 C. include = 5 D. start = 4 Answer: C Question #89 FILL BLANK - Refer to the confusion matrix: An analyst determines that loan defaults occur at the rate of 3% in the overall population. The above confusion matrix is from an oversampled test set (1 = default). What is the sensitivity adjusted for the population event probability? Enter your answer in the space below. Round to three decimals (example: n.nnn). Answer: 0.617 Question #90 Refer to the exhibit: On the Gains Chart, what is the correct interpretation of the horizontal reference line? A. the proportion of cases that cannot be classified B. the probability of a false negative C. the probability of a false positive D. the prior event rate Answer: B Question #91 Refer to the confusion matrix: Calculate the accuracy and error rate (0 - negative outcome, 1 - positive outcome) A. Accuracy = 58/102, Error Rate = 23/48 B. Accuracy = 83/102, Error Rate = 67/102 C. Accuracy = 25/150, Error Rate = 44/150 D. Accuracy = 83/150, Error Rate = 67/150 Answer: A Question #92 Which statistic is based on the maximum vertical distance between the primary event EDF and the secondary event EDF? A. KS B. SBC C. Max EDF D. Brier Score Answer: A Reference: https://support.sas.com/documentation/onlinedoc/ets/132/severity.pdf Question #93 DRAG DROP - Drag the adjustment formulas for oversamping from the left and place them into the correct location in the confusion matrix shown on the right. Select and Place: Answer: Question #94 An analyst knows that the categorical predictor, zip_code, is an important predictor of a binary target. However, zip_code has too many levels to be a feasible predictor in a model. The analyst uses PROC CLUSTER to implement Greenacre's method to reduce the number of categorical levels. What is the correct application of Greenacre's method in this situation? A. Clustering the levels using the target proportion for each zip_code as input. B. Clustering the levels using the zip_code values as input. C. Clustering the levels using the number of cases in each zip_code as input. D. Clustering the levels using dummy coded zip_code levels as inputs. Answer: A Reference: https://support.sas.com/resources/papers/proceedings/proceedings/sugi31/079-31.pdf Question #95 What does the Pearson product moment correlation coefficient measure? A. nonlinear and nonmonotonic association between two variables B. linear and monotonic association between two variables C. linear and nonmonotonic association between two variables D. nonlinear and monotonic association between two variables Answer: B Reference: http://d-scholarship.pitt.edu/8056/1/Chokns_etd2010.pdf Question #96 This question will ask you to provide a segment of missing code. The following code is used to create missing value indicator variables for input variables, fred1 to fred7. Which segment of code would complete the task? A. B. C. D. Answer: C Question #97 This question will ask you to provide a missing option. Given the following SAS program: What option must be added to the program to obtain a data set containing Spearman statistics? A. OUTCORR=estimates B. OUTS=estimates C. OUT=estimates D. OUTPUT=estimates Answer: D Question #98 This question will ask you to provide a missing option. A business analyst is investigating the differences in sales figures across 8 sales regions. The analyst is interested in viewing the regression equation parameter estimates for each of the design variables. Which option completes the program to produce the regression equation parameter estimates? A. Solve B. Estimate C. Solution D. Est Answer: C Reference: https://documentation.sas.com/?docsetId=statug&docsetTarget=statug_ods_examples06.htm&docsetVersion=14.3&locale=en Question #99 After performing an ANOVA test, an analyst has determined that a significant effect exists due to income. The analyst wants to compare each Income to all others and wants to control for experimentwise error. Which GLM procedure statement would provide the most appropriate output? A. lsmeans Income / pdiff=control adjust=dunnett; B. lsmeans Income / pdiff=control adjust=t; C. lsmeans Income / pdiff=all adjust=tukey; D. lsmeans Income / pdiff=all adjust=t; Answer: A Reference: https://rpubs.com/JsoLab/Stat01_L02 Question #100 SIMULATION - A linear model has the following characteristics: *A dependent variable (y) *One continuous variable (xl), including a quadratic term (x12) *One categorical (d with 3 levels) predictor variable and an interaction term (d by x1) How many parameters, including the intercept, are associated with this model? Enter your numeric answer in the space below. Do not add leading or trailing spaces to your answer. Answer: 7 For More exams visit https://killexams.com/vendors-exam-list Kill your test at First Attempt....Guaranteed! | ||||||||
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I have done my internship program in Silicon India Private limited in JAVA.The Internship was very good, gave real life examples and gave good insight into each aspect of JAVA; This Internship, I understood JAVA technologies much better in spite of being a fresher.After this internship i got many improvement in my java skills, and made a real life project. Thanks for Siliconindia and also My trainer Arun kumar Das to give us a very good environment to learn and express my talent. At least 21 million Americans experience a major depressive episode. Risk factors include family history, history of trauma, substance use disorders, and other mental health issues. Sadness, grief, and even passing feelings of hopelessness or despair are part of the human experience. Most people have these feelings from time to time, but they will generally Boost within a few days or as the situation changes. Major depressive disorder (MDD) — also called major depression or clinical depression — involves a low or sad mood that persists for 2 weeks or longer. You might also notice declining energy, loss of appetite, feelings of emptiness, and a loss of interest in the things you used to enjoy. Untreated depression can affect physical and emotional well-being, as well as your personal life and relationships. But support from a mental health professional can make a big difference in your symptoms, and you have plenty of options for treatment — including therapy, medication, and complementary treatments. Depression is common. According to the World Health Organization (WHO), about 280 million people worldwide have depression, including 5% of the world’s adults and 5.7% of adults above age 60. The National Insitute of Mental Health (NIMH) estimates that 21 million U.S. adults had at least one major depressive episode in 2021. This represents 8.3% of the U.S. adult population. Still, many people with depression don’t get help for their symptoms, so the real number of people living with depression may be higher:
How much have depression rates increased?According to a 2022 study looking at 2015 through 2020, the rates of depression in the United States increased as follows: There was no increase in older age groups. In general, the researchers found that in 2020, having depression for longer than a year occurred in 1 in 10 Americans and almost 1 in 5 adolescents and young adults. Several types of depression exist. Symptoms may show up in slightly different ways. Major depressive disorderWhen people refer to “depression,” they’re often talking about major or clinical depression. The NIMH estimates that 21 million adults, or 8.3% of all adults in the United States, had at least one episode of major depression in 2021. Episodes of major depression were more common among:
Persistent depressive disorderPersistent depressive disorder, or dysthymia, is a type of chronic depression that lasts 2 years or longer. It generally involves milder depression symptoms that are more long lasting. The mood changes associated with persistent depression may be less severe, but they can still cause plenty of distress. Estimates suggest that 3% of U.S. people may have this type of depression. Bipolar disorderNot everyone who lives with bipolar disorder will experience an episode of depression, but many do. People with bipolar I disorder have episodes of mania, sometimes followed by episodes of depression. People with bipolar II have alternating episodes of depression and hypomania. Research from 2018 suggests the lifetime prevalence of people who develop bipolar I is around 1%, though estimates vary. According to a mix of data from older epidemiological studies, as reported in research from 2018:
Learn more about the types of bipolar disorder. Seasonal depressionMajor depressive disorder with seasonal patterns — sometimes called seasonal affective disorder or seasonal depression — involves changes in mood that happen alongside changes in the seasons. For many people, this type of depression begins in autumn and lasts through winter, but some experience seasonal depression symptoms in spring and summer. The American Psychiatric Association notes that around 5% of American adults experience seasonal depression. For these people, symptoms generally linger for around 40% of the year. Seasonal depression more commonly affects:
Symptoms of seasonal depression also return each year for almost 70% of people living with the condition. Postpartum depressionTemporary feelings of sadness and fatigue, along with shifts in mood, are very common after childbirth. These “baby blues” can have a range of causes, including:
But if these mood changes intensify or persist for more than a week or two, you could be experiencing major depressive disorder with peripartum onset. This is commonly called postpartum depression. According to estimates, 6.5–20% of people develop postpartum depression after giving birth. The condition more commonly affects new parents who:
Postpartum depression can lead to social withdrawal, loss of appetite, and unwanted emotions. It can also affect your relationship with your baby. It’s important to note that this condition doesn’t develop because of anything you did or did not do as a parent. Learn more about getting support. Psychotic depressionMajor depressive disorder with psychotic features (aka psychotic depression) describes depression that involves psychosis, hallucinations, delusions, or paranoia. Research on depression with psychotic features remains fairly limited in comparison to research on other types of depression. However, some experts believe this subtype is more common than previously believed. According to a 2021 research review, depression with psychotic features affects:
The same researchers note that symptoms of psychosis develop during an episode of depression for:
The symptoms of depression can range from mild to severe. In the 2019 National Health Interview Survey, adults were asked about their symptoms over the past 2 weeks and found that:
You may have depression if feelings of sadness or emptiness linger through most of each day for longer than 2 weeks. Other key mental and emotional symptoms include:
Experts believe depression develops in response to a combination of factors, including brain chemistry, hormones, and genetics. In other words, there’s no single cause of depression. Some risk factors of depression include:
Learn more about potential causes and risk factors for depression. There are various treatments for depression. According to the American Psychiatric Association, a combination of antidepressants and therapy is, on average, more effective. But both treatments have about the same effectiveness when used alone. Between 40% and 60% of people taking antidepressants for moderate or severe depression notice improved symptoms after 6–8 weeks. In contrast, between 20% and 40% of people taking a placebo noticed similar improvements. However, about 23% of people taking antidepressants have a relapse of depression symptoms within a year or two. In contrast, nearly half of those taking placebos have a relapse within the same timeframe. Evidence-based therapy for depression, such as cognitive behavioral therapy, also led to remission in one-third of subjects, according to a 2021 study. While you can take steps to lower your risk of developing depression, experts have yet to find a way to prevent it completely. That said, learning to recognize the signs of depression and knowing how to get help when needed can help you address symptoms early before they get worse. Statistics can absolutely have value, but they may not always seem relevant to your specific circumstances. Maybe you have no family history of depression, for example, or any other key risk factors. Perhaps you experience symptoms in an uncommon pattern, like depression that worsens in summer. No matter what symptoms you experience or how they show up, a trained therapist can help you begin to explore possible causes and offer guidance on effective treatments. A student with three or four years of high school mathematics (the final year may be called precalculus, trigonometry, functions, or analysis), but no calculus, normally enrolls in MTH 111. A student with a year of AB calculus, A levels or IB math SL normally enrolls in MTH 153 and/or MTH 112 during the first year. Placement in MTH 112 is determined not only by the amount of previous calculus but also by the strength of the student’s preparation. If a student has a year of BC calculus or IB math HL, they may omit MTH 112. A student with two years of high school mathematics, but no calculus or precalculus, should enroll in MTH 102. Topics offered in MTH 105 are intended for students not expecting to major in mathematics or the sciences. A student who receives credit for taking MTH 111 may not have AP calculus credits applied toward their degree. A student with 8 AP Calculus credits (available to students with a 4 or 5 on the AP test for BC Calculus) may apply only 4 of them if they also receive credit for MTH 112. A student who has a score of 4 or 5 on the AP Statistics examination may receive 4 AP credits. They may not however, use them toward their degree requirements if they also receive credit for SDS 201, SDS 220, PSY 201 or ECO 220. (AP credits can be used to meet degree requirements only under circumstances specified by the college.) MTH 101/ IDP 101 Math Skills Studio (4 Credits) Offered as MTH 101 and IDP 101. This course is for students who need additional preparation to succeed in courses containing quantitative material. It provides a supportive environment for learning or reviewing, as well as applying, arithmetic, algebra and mathematical skills. Students develop their numerical and algebraic skills by working with numbers drawn from a variety of sources. This course does not carry a Latin Honors designation. Enrollment limited to 20. Instructor permission required. Fall, Interterm MTH 102 Elementary Functions (4 Credits) Linear, polynomial, exponential, logarithmic and trigonometric functions graphs, verbal descriptions, tables and mathematical formulae. For students who intend to take calculus or quantitative courses in scientific fields, economics, government and sociology. Also recommended for prospective teachers preparing for certification. {M} Fall MTH 103/ IDP 103 Precalculus and Calculus Bootcamp (2 Credits) Offered as IDP 103 and MTH 103. This course provides a fast-paced review of and intense practice of computational skills, graphing skills, algebra, trigonometry, elementary functions (pre-calculus) and computations used in calculus. Featuring a daily review followed by problem-solving drills and exercises stressing technique and application, this course provides concentrated practice in the skills needed to succeed in courses that apply elementary functions and calculus. Students gain credit by completing all course assignments. This course does not count towards the Mathematics or Mathematical Statistics majors. S/U only. Enrollment limited to 20. Fall, Interterm, Spring, Variable MTH 105ar courses in Discovering Mathematics-MathStudio: Making, Art + Math (4 Credits) The course has geometrical, mathematical and studio art components. Students draw and build 3D objects with simple tools and study their geometric and mathematical properties. Introduction to elements of geometry, algebra and symmetry in connection to what is built. {M} Spring, Variable MTH 105we courses in Discovering Mathematics-The Mathematics of Wealth (4 Credits) This course looks at the intersection of mathematics and social justice thru the lens of wealth in America. Social justice courses include wealth distribution, taxes, the Gini index and the poverty cycle. Mathematical courses include mathematical modeling, logic, set theory, statistics and probability. (E) Fall, Spring, Variable MTH 111 Calculus I (4 Credits) Rates of change, differentiation, applications of derivatives including differential equations and the fundamental theorem of the calculus. Written communication and applications to other sciences and social sciences motivate course content. Enrollment limited to 25. {M} Fall, Spring MTH 112 Calculus II (4 Credits) Techniques of integration, geometric applications of the integral, differential equations and modeling, infinite series, and approximation of functions. Written communication and applications to other sciences and social sciences motivate course content. Prerequisite: MTH 111 or equivalent. Enrollment limited to 25. {M} Fall, Spring MTH 153 Introduction to Discrete Mathematics (4 Credits) An introduction to discrete (finite) mathematics with emphasis on the study of algorithms and on applications to mathematical modeling and computer science. courses include sets, logic, graph theory, induction, recursion, counting and combinatorics. Enrollment limited to 25. {M} Fall, Spring MTH 205/ CSC 205 Modeling in the Sciences (4 Credits) Offered as CSC 205 and MTH 205. This course integrates the use of mathematics and computers for modeling various phenomena drawn from the natural and social sciences. Scientific case studies span a wide range of systems at all scales, with special emphasis on the life sciences. Mathematical tools include data analysis, discrete and continuous dynamical systems, and discrete geometry. This is a project-based course and provides elementary training in programming using Mathematica. Designations: Theory, Programming. Prerequisites: MTH 112. CSC 110 recommended. Enrollment limited to 20. {M} Fall, Spring, Annually MTH 206/ EDC 206 Statistical Literacy in Educational Research and Policy (4 Credits) Offered as EDC 206 and MTH 206. Education is increasingly data driven--data is used to evaluate classroom pedagogy, student achievement, teacher efficacy and school failure. It is important for educators then, to be able to interpret complex data and make research-based decisions. This course fosters student’s ability to critically interpret education-related data by concentrating on the application of critical thinking skills to arguments involving statistics in education. The student emerges as a knowledgeable consumer of statistics rather than a producer of statistical calculations. Course activities focus on the interpretation, evaluation and communication of statistics in educational research literature, standardized tests, and real-world situations. {M} Fall, Spring, Variable MTH 211 Linear Algebra (4 Credits) Systems of linear equations, matrices, linear transformations and vector spaces. Applications to be selected from differential equations, foundations of physics, geometry and other topics. Not open to students who have taken MTH 210. Prerequisite: MTH 112 or equivalent, or MTH 111 and MTH 153; MTH 153 is suggested. Enrollment limited to 35. {M} Fall, Spring MTH 212 Multivariable Calculus (4 Credits) Theory and applications of limits, derivatives and integrals of functions of one, two and three variables. Curves in two-and three-dimensional space, vector functions, double and triple integrals, polar, cylindrical and spherical coordinates. Path integration and Green’s Theorem. Prerequisites: MTH 112. MTH 211 suggested (may be concurrent). Enrollment limited to 30. {M} Fall, Spring MTH 233 An Introduction to AbstractAlgebra (4 Credits) An introduction to the concepts of abstract algebra, including groups, quotient groups and, if time allows, rings and fields. Prerequisites: MTH 153 and MTH 211 or equivalent. {M} Spring MTH 238 Number Theory (4 Credits) Topics to be covered include properties of the integers, prime numbers, congruences, various Diophantine problems, arithmetical functions and cryptography. Prerequisite: MTH 153 and MTH 211, or equivalent. {M} Fall MTH 246 Probability (4 Credits) An introduction to probability, including combinatorial probability, random variables, discrete and continuous distributions. Prerequisites: MTH 153 and MTH 212 (may be taken concurrently), or equivalent. {M} Fall MTH 254 Combinatorics (4 Credits) Enumeration, including recurrence relations and generating functions. Special attention paid to binomial coefficients, Fibonacci numbers, Catalan numbers and Stirling numbers. Combinatorial designs, including Latin squares, finite projective planes, Hadamard matrices and block designs. Necessary conditions and constructions. Error correcting codes. Applications. Prerequisites: MTH 153 and MTH 211 or equivalent. {M} Fall, Spring, Alternate Years MTH 255 Graph Theory (4 Credits) The course begins with the basic structure of graphs including connectivity, paths, cycles and planarity and proceeds to independence, stability, matchings and colorings. Directed graphs and networks are considered. In particular, some optimization problems including maximum flow are covered. The material includes theory and mathematical proofs as well as algorithms and applications. Prerequisites: MTH 153 and MTH 211 or equivalent. {M} Fall, Spring, Alternate Years MTH 264de courses in Applied Math-Differential Equations (4 Credits) This course gives an introduction to the theory and applications of ordinary differential equations. We explore different applications in physics, chemistry, biology, engineering and social sciences. We learn to predict the behavior of a particular system described by differential equations by finding exact solutions, making numerical approximations, and performing qualitative and geometric analysis. Specific courses include solutions to first order equations and linear systems, existence and uniqueness of solutions, nonlinear systems and linear stability analysis, forcing and resonance, Laplace transforms. Prerequisites: MTH 112, MTH 212 and MTH 211 (recommended) or PHY 210, or equivalent. {M} Fall, Spring, Variable MTH 270ss courses in Geometry-The Shape of Space (4 Credits) This is a course in intuitive geometry and topology, with an emphasis on hands-on exploration and developing the visual imagination. Discussions may include knots, geometry and topology of surfaces and the Gauss-Bonnet Theorem, symmetries, wallpaper patterns in Euclidean, spherical and hyperbolic geometries, and an introduction to 3-dimensional manifolds. Prerequisites: MTH 211 and MTH 212 or equivalent. {M} Fall, Spring, Variable MTH 280 Advanced Calculus (4 Credits) Functions of several variables, vector fields, divergence and curl, critical point theory, transformations and their Jacobians, implicit functions, manifolds, theory and applications of multiple integration, and the theorems of Green, Gauss and Stokes. Prerequisites: MTH 211 and MTH 212, or equivalent. MTH 153 is encouraged. {M} Spring MTH 281 Introduction to Analysis (4 Credits) The topological structure of the real line, compactness, connectedness, functions, continuity, uniform continuity, differentiability, sequences and series of functions, uniform convergence, introduction to Lebesgue measure and integration. Prerequisites: MTH 211 and MTH 212, or equivalent. MTH 153 is strongly encouraged. {M} Fall MTH 300 Dialogues in Mathematics and Statistics (1 Credit) In this class students don’t do math as much as they talk about doing math and the culture of mathematics. The class includes lectures by students, faculty and visitors on a wide variety of topics, and opportunities to talk with mathematicians about their lives. This course is especially helpful for those considering graduate school in the mathematical sciences. Prerequisites: MTH 211, MTH 212 and two additional mathematics courses at the 200-level, or equivalent. May be repeated once for credit. S/U only. {M} Fall, Spring MTH 301rs courses in Advanced Mathematics-Research (3 Credits) In this course students work in small groups on original research projects. Students are expected to attend a brief presentation of projects at the start of the semester. latest courses include interactions between algebra and graph theory, plant patterns, knot theory and mathematical modeling. This course is open to all students interested in gaining research experience in mathematics. Prerequisites vary depending on the project, but normally MTH 153 and MTH 211 are required. {M} Fall, Spring, Variable MTH 320/ SDS 320 Seminar: Mathematical Statistics (4 Credits) Offered as MTH 320 and SDS 320. An introduction to the mathematical theory of statistics and to the application of that theory to the real world. Discussions include functions of random variables, estimation, likelihood and Bayesian methods, hypothesis testing and linear models. Prerequisites: a course in introductory statistics, MTH 212 and MTH 246, or equivalent. Enrollment limited to 12. Juniors and seniors only. Instructor permission required. {M} Spring, Alternate Years MTH 333ca courses in Abstract Algebra-Category Theory (4 Credits) This course provides an introduction to category theory through the language of universal algebra and module theory. courses include: semigroups, monoids, quasigroups, modules, hom sets, categories, functors, representable functors. Additional courses may be covered if time permits: varieties, Birkhoff's Theorem, congruences, adjunctions. Course consists of lectures, weekly student presentations, one midterm test and a final presentation. Prerequisites: MTH 233 or equivalent. (E) Fall, Spring, Variable MTH 333ct courses in Abstract Algebra-Coding Theory (4 Credits) An overview of noiseless and noisy coding. Covers both theory and applications of coding theory. courses include linear codes, Hamming codes, Reed-Muller codes, cyclic redundancy checks, entropy, and other courses as time permits. Prerequisites: MTH 153 and MTH 211. One of MTH 233 or MTH 238 is highly recommended. {M} Fall, Spring, Variable MTH 333la courses in Abstract Algebra-Advanced Linear Algebra (4 Credits) This is a second course in linear algebra that explores the structure of matrices. courses may include characteristic and minimal polynomials, diagonalization and canonical forms of matrices, the spectral theorem, the singular value decomposition theorem, an introduction to modules, and applications to problems in optimization, Markov chains, and others. {M} Fall, Spring, Variable MTH 333rt courses in Abstract Algebra-Representation Theory (4 Credits) Representation theory is used everywhere, from number theory, combinatorics, and topology, to chemistry, physics, coding theory, and computer graphics. The core question of representation theory is: what are the fundamentally different ways to describe symmetries as groups of matrices acting on an underlying vector space? This course will explain each part of that question and key approaches to answering it. courses may include irreducible representations, Schur’s Lemma, Maschke’s Theorem, character tables, orthogonality of characters, and representations of specific finite groups. MTH 233 is helpful but not required. Prerequisite: MTH 211. {M} Fall, Spring, Variable MTH 353ac Seminar: Advanced courses in Discrete Applied Mathematics-Calderwood Seminar on Applied Algebraic Combinatorics and Mathematical Biology (4 Credits) Calderwood Seminar. Combinatorial ideas permeate biology at all scales, from the combinatorial properties of the sequences of letters (nucleotides) representing DNA and RNA, to the symmetries often observed in cell divisions, to the graphs that can be used to represent evolutionary trees. This course focuses on key combinatorial ideas that arise on multiple scales in biology, including molecular, cellular and organism, especially: counting and classification, symmetries and combinatorial graphs. The class interviews mathematicians and biologists about their current research and prepares multiple reports and presentations for different kinds of popular audiences (for example: kids, biologists and newspapers). No particular biological background is expected. MTH 153 and an additional proof-based course are required, or equivalent. MTH 233 and MTH 254 or their equivalents are useful but not required. Enrollment limited to 12. Juniors and seniors only. Instructor permission required. {M} Fall, Spring, Variable MTH 353dl Seminar: Advanced courses in Discrete Applied Mathematics-Mathematics of Deep Learning (4 Credits) The course covers courses from different parts of mathematics that play some role in the design of neural networks. The course also looks at some neural networks’ applications and at how mathematics is integrated. Topics will include: What is a neural network, examples and applications; Universal approximation theorems (Cybenko and others); Examples of loss functions; Gradient Descent and Stochastic Gradient descent; Generalization gap, training vs testing data; Quick review of game theory, Nash equilibrium; Generative Adversarial Networks (GAN); Unrolled GANs. Enrollment limited to 12. Juniors and seniors only. Instructor permission required. {M} Fall, Spring, Variable MTH 364ds Advanced courses in Continuous Applied Mathematics-Dynamical Systems, Chaos and Applications (4 Credits) An introduction to the theory of Dynamical Systems with applications. A dynamical system is a system that evolves with time under certain rules. The class looks at both continuous and discrete dynamical systems when the rules are given by differential equations or iteration of transformations. Students study the stability of equilibria or periodic orbits, bifurcations, chaos and strange attractors. Applications are often biological, but the final project is on a scientific application of the student's choice. Prerequisites: MTH 211 and MTH 212 or equivalent. {M} Fall, Spring, Variable MTH 364pd Advanced courses in Continuous Applied Mathematics-Partial Differential Equations (4 Credits) Partial differential equations allow the ability to track how quantities change when they depend on multiple variables, e.g. space and time. This course provides an introduction to techniques for analyzing and solving partial differential equations and surveys applications from the sciences and engineering. Specific courses include Fourier series; separation of variables; heat, wave and Laplace’s equations; finite difference numerical methods; and introduction to pattern formations. Prerequisite: MTH 211 and MTH 212, or MTH 280/MTH 281, or equivalent. MTH 264 is strongly recommended. Prior exposure to computing (using Matlab, Mathematica, Python, etc.) is helpful. {M} Fall, Spring, Variable MTH 370tp courses in Topology & Geometry-Topology (4 Credits) Topology is a kind of geometry in which important properties of a shape are preserved under continuous motions (homeomorphisms)—for instance, properties like whether one object can be transformed into another by stretching and squishing but not tearing. This course gives students an introduction to some of the classical courses in the area: the basic notions of point set topology (including connectedness and compactness) and the definition and use of the fundamental group. Prerequisites: MTH 280 or 281 or permission of the instructor. {M} Fall, Spring, Variable MTH 381fw courses in Mathematical Analysis: Fourier Analysis and Wavelets (4 Credits) The mathematics of how it is possible to simultaneously stream videos while using the same cable to call on the phone. Hilbert spaces, Fourier series, Fourier transform, discrete Fourier transforms, wavelets, multiresolution analysis, applications. Prerequisite: MTH 280 or MTH 281. {M} Fall, Spring, Variable MTH 382 Complex Analysis (4 Credits) Complex numbers, functions of a complex variable, algebra and geometry of the complex plane. Differentiation, integration, Cauchy integral formula, calculus of residues, applications. Prerequisite: MTH 211 and MTH 212, or equivalent. Fall, Spring, Variable MTH 400 Special Studies (1-4 Credits) By permission of the department, normally for majors who have had at least four semester courses at the intermediate level. Fall, Spring MTH 430D Honors Project (4 Credits) Fall, Spring, Annually MTH 431 Honors Project (8 Credits) Fall, Spring, Annually MTH 432D Honors Project (6-12 Credits) Fall, Spring MTH 580 Graduate Special Studies (4 Credits) Fall, Spring CSC 109/ SDS 109 Communicating with Data (4 Credits) Offered as SDS 109 and CSC 109. The world is growing increasingly reliant on collecting and analyzing information to help people make decisions. Because of this, the ability to communicate effectively about data is an important component of future job prospects across nearly all disciplines. In this course, students learn the foundations of information visualization and sharpen their skills in communicating using data. Throughout the semester, we explore concepts in decision-making, human perception, color theory and storytelling as they apply to data-driven communication. Whether you’re an aspiring data scientist or you just want to learn new ways of presenting information, this course helps you build a strong foundation in how to talk to people about data. {M} Fall, Spring, Alternate Years CSC 205/ MTH 205 Modeling in the Sciences (4 Credits) Offered as CSC 205 and MTH 205. This course integrates the use of mathematics and computers for modeling various phenomena drawn from the natural and social sciences. Scientific case studies span a wide range of systems at all scales, with special emphasis on the life sciences. Mathematical tools include data analysis, discrete and continuous dynamical systems, and discrete geometry. This is a project-based course and provides elementary training in programming using Mathematica. Designations: Theory, Programming. Prerequisites: MTH 112. CSC 110 recommended. Enrollment limited to 20. {M} Fall, Spring, Annually CSC 270 Digital Circuits and Computer Systems (5 Credits) This class introduces the operation of logic and sequential circuits. Students explore basic logic gates (AND, OR, NAND, NOR), counters, flip-flops, decoders, microprocessor systems. Students have the opportunity to design and implement digital circuits during a weekly lab. Designation: Systems. Prerequisite: CSC 231. Enrollment limited to 12. Fall, Spring, Variable CSC 290 Introduction to Artificial Intelligence (4 Credits) An introduction to artificial intelligence including an introduction to artificial intelligence programming. Discussions include: game playing and search strategies, machine learning, natural language understanding, neural networks, genetic algorithms, evolutionary programming and philosophical issues. Designations: Theory, Programming. Prerequisite: CSC 120 or equivalent. Prerequisites for CSC Majors: CSC 210 and MTH 111 or equivalent. Fall, Spring, Variable EDC 206/ MTH 206 Statistical Literacy in Educational Research and Policy (4 Credits) Offered as EDC 206 and MTH 206. Education is increasingly data driven--data is used to evaluate classroom pedagogy, student achievement, teacher efficacy and school failure. It is important for educators then, to be able to interpret complex data and make research-based decisions. This course fosters student’s ability to critically interpret education-related data by concentrating on the application of critical thinking skills to arguments involving statistics in education. The student emerges as a knowledgeable consumer of statistics rather than a producer of statistical calculations. Course activities focus on the interpretation, evaluation and communication of statistics in educational research literature, standardized tests, and real-world situations. {M} Fall, Spring, Variable IDP 101/ MTH 101 Math Skills Studio (4 Credits) Offered as MTH 101 and IDP 101. This course is for students who need additional preparation to succeed in courses containing quantitative material. It provides a supportive environment for learning or reviewing, as well as applying, arithmetic, algebra and mathematical skills. Students develop their numerical and algebraic skills by working with numbers drawn from a variety of sources. This course does not carry a Latin Honors designation. Enrollment limited to 20. Instructor permission required. Fall, Interterm IDP 105 Quantitative Skills in Practice (4 Credits) A course continuing the development of quantitative skills and quantitative literacy begun in MTH 104/ IDP 104. Students continue to exercise and review basic mathematical skills, to reason with quantitative information, to explore the use and power of quantitative reasoning in rhetorical argument, and to cultivate the habit of mind to use quantitative skills as part of critical thinking. Attention is given to visual literacy in reading graphs, tables and other displays of quantitative information and to cultural attitudes surrounding mathematics. Prerequisites: MTH 104/ IDP 104. Enrollment limited to 18. {M} Spring IDP 325 Art/Math Studio (4 Credits) This course is a combination of two distinct but related areas of study: studio art and mathematics. Students are actively engaged in the design and fabrication of three-dimensional models that deal directly with aspects of mathematics. The class includes an introduction to basic building techniques with a variety of tools and media. At the same time each student pursues an intensive examination of a particular-individual-theme within studio art practice. The mathematical projects are pursued in small groups. The studio artwork is done individually. Group discussions of reading, oral presentations and critiques, as well as several small written assignments, are a major aspect of the class. Limited to juniors and seniors. Instructor permisison required. Enrollment is limited to 15. {A}{M} Spring MTH 320/ SDS 320 Seminar: Mathematical Statistics (4 Credits) Offered as MTH 320 and SDS 320. An introduction to the mathematical theory of statistics and to the application of that theory to the real world. Discussions include functions of random variables, estimation, likelihood and Bayesian methods, hypothesis testing and linear models. Prerequisites: a course in introductory statistics, MTH 212 and MTH 246, or equivalent. Enrollment limited to 12. Juniors and seniors only. Instructor permission required. {M} Spring, Alternate Years PSY 201 Statistical Methods for Undergraduate Research (5 Credits) An overview of the statistical methods needed for undergraduate research emphasizing methods for data collection, data description and statistical inference including an introduction to study design, confidence intervals, testing hypotheses, analysis of variance and regression analysis. Techniques for analyzing both quantitative and categorical data are discussed. Applications are emphasized, and students use R and other statistical software for data analysis. Classes meet for lecture/discussion and a required laboratory that emphasizes the analysis of real data. This course satisfies the basis requirement for the psychology major. Students who have taken MTH 111 or the equivalent or who have taken AP STAT should take SDS 220, which also satisfies the major requirement. Enrollment is restricted to psychology majors or permission of instructor. Normally students receive credit for only one of the following introductory statistics courses: PSY 201, ECO 220, GOV 190, SDS 220, SDS 201, SOC 201, EDC 206. {M} Fall, Spring SDS 220 Introduction to Probability and Statistics (4 Credits) (Formerly MTH 220/SDS 220). An application-oriented introduction to modern statistical inference: study design, descriptive statistics, random variables, probability and sampling distributions, point and interval estimates, hypothesis tests, resampling procedures and multiple regression. A wide variety of applications from the natural and social sciences are used. This course satisfies the basic requirement for biological science, engineering, environmental science, neuroscience and psychology. Normally students receive credit for only one of the following introductory statistics courses: SDS 201, PSY 201, GOV 203, ECO 220, SDS 220 or SOC 204. Exceptions may be allowed in special circumstances with adviser and instructor permission. Corequisite: SDS 100 required for students who have not completed SDS 192, SDS 201, SDS 290 or SDS 291. Prerequisite: MTH 111 or equivalent. Enrollment limited to 40. {M} Fall, Spring SDS 290 Research Design and Analysis (4 Credits) (Formerly MTH/SDS 290). A survey of statistical methods needed for scientific research, including planning data collection and data analyses that provide evidence about a research hypothesis. The course can include coverage of analyses of variance, interactions, contrasts, multiple comparisons, multiple regression, factor analysis, causal inference for observational and randomized studies and graphical methods for displaying data. Special attention is given to analysis of data from student projects such as theses and special studies. Statistical software is used for data analysis. Prerequisites: One of the following: PSY 201, SDS 201, GOV 203, ECO 220, SDS 220 or a score of 4 or 5 on the AP Statistics examination or the equivalent. Corequisite: SDS 100 required for students who have not completed SDS 192, SDS 201, SDS 220 or SDS 291. Enrollment limited to 40. {M} Fall, Spring, Annually SDS 291 Multiple Regression (4 Credits) (Formerly MTH 291/ SDS 291). Theory and applications of regression techniques: linear and nonlinear multiple regression models, residual and influence analysis, correlation, covariance analysis, indicator variables and time series analysis. This course includes methods for choosing, fitting, evaluating and comparing statistical models and analyzes data sets taken from the natural, physical and social sciences. Prerequisite: one of the following: SDS 201, PSY 201, GOV 203, SDS 220, ECO 220 or equivalent or a score of 4 or 5 on the AP Statistics examination. Corequisite: SDS 100 required for students who have not completed SDS 192, 201, 220 or 290. Enrollment limited to 40. {M}{N} Fall, Spring 2023 ANNUAL SECURITY AND FIRE SAFETY COMPLIANCE REPORTThe Annual Security and Fire Safety Compliance Report includes Pratt Institute’s security policies, crime statistics, and on-campus student residential facilities’ fire safety policies and data. This report is published annually by the Pratt Department of Campus Safety. Our responsibility and intent are to provide comprehensive information regarding campus safety and security, including the incidence of crime. This report summarizes campus safety and security policies in effect at Pratt Institute. It highlights crime reporting procedures, crime prevention programs, and other services available to the campus community. This report also includes crime statistics for the 2020, 2021, and 2022 calendar years and information regarding the number of arrests made for certain designated criminal offenses during these periods. Crime statistics included in this publication are from locations identified as owned or leased property belonging to Pratt Institute. The crime statistics also include incidents reported by non-Pratt affiliated individuals and on-campus, residential facilities, non-campus buildings, property, and public properties. Additionally, this document is the Campus Fire Safety and Right-To-Know Act Report, which provides Pratt Institute’s policies and procedures regarding fire safety, fire drills, evacuation, and fire systems for each on-campus student residential building. This report includes statistics on the number and type of fires in residential facilities. You may get this report from the attached PDF file below or pick up a copy from the Pratt Department of Campus Safety administrative office, located in the Chapel Hall, room 003 or 005, Monday through Friday, between 9 a.m. and 5 p.m. Wirtschaft und Statistik journalIn 2022, the new earnings survey replaced the three previous earnings surveys, thereby modernising the range of data and publications available on the subject. In addition to discussing the background and the situation at the start of the reorganisation, one of the articles in the current issue of our scientific WISTA journal describes the modern way of collecting data, the processing of the data and the statistical methods used for this purpose. The School of Mathematics and Statistics is recognized for its contributions to research and applications of mathematical and statistical science, and it’s also known for expertise in mathematical and computational modeling, data science, and scientific inference. Since mathematics is at the root of many social, technical, medical, and environmental issues faced by society today, we equip our graduates with a deep understanding of mathematical and statistical principles, tools to apply those skills to real-world problems, and the ability to express complex ideas in everyday language. We provide our students with research and experiential learning opportunities and nurture curiosity and creativity. The registration fee is $600 for individuals with an academic appointment, and $1000 for individuals with non-academic positions. This fee is payable at the time of registration using a credit card at the Eventbrite website: https://2024-isgworkshop.eventbrite.com/ A $30 refund processing fee will be charged on any refunds made; and the registration fee is not refundable after February 14th, 2024. The registration fees will cover workshop attendance, breakfast and lunches throughout the week (Monday-Friday), hosted receptions, and a gift bag with information on the workshop and Boulder. Please contact the Workshop Coordinator by emailing ibgworkshop@colorado.edu or phone at +1-303-735-8490 if you need additional event information. Our systems have detected unusual traffic activity from your network. Please complete this reCAPTCHA to demonstrate that it's you making the requests and not a robot. If you are having trouble seeing or completing this challenge, this page may help. If you continue to experience issues, you can contact JSTOR support. 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