Ph.D., Research Methodologies: Quantitative Methods and Measurement, University of North Dakota
B.S., Occupational Therapy, School of Medicine and Health Sciences, University of North Dakota
Research: Transformational Learning in Higher Education, Applied Statistics Education, Psychometric Analysis, Assessment and Evaluation in Education.
Teaching: Applied Statistics, Psychometrics, Biometrics, Quantitative Research Methods, Assessment and Evaluation in Education.
Course  Section  Title  Days  Time  Location  

STAT 220  01  Statistics I  M  W  F    0815  0920  OSS 313  
Description of course Genetics B/ Lab: 
CRN: 41139
4 Credit Hours
Instructor: Erin M. Curran
Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chisquare, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
Schedule Details


STAT 220  03  Statistics I  M  W  F    0935  1040  OSS 313  
Description of course Genetics B/ Lab: 
CRN: 41141
4 Credit Hours
Instructor: Erin M. Curran
Formerly IDTH 220 or QMCS 220 Introductory applied statistics. Work environment; population, sampling frame, random sample, type of variables and studies. Descriptive statistics: collecting, displaying, summarizing, and interpreting data to extract information. Probability; relative frequency definition of probability, conditional probability, independence, discrete and continuous random variables, probability distribution and probability density, binomial, normal, standard normal, t, chisquare, and F distributions. Inferential statistics; sampling distribution of the sample mean and sample proportion, central limit theorem, confidence intervals and hypothesis tests for one and two means and one and two proportions. Basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at level of MATH 111 or above; or MATH 100, 101, 105, 108, 109, 111 or 113 NOTE: Students who receive credit for STAT 220 may not receive credit for IDTH 201
Schedule Details


STAT 320  01  Statistics II  M  W  F    1215  1320  OSS 333  
Description of course Genetics B/ Lab: 
CRN: 43197
4 Credit Hours
Instructor: Erin M. Curran
Formerly IDTH 320 or QMCS 320 Applie linear regression models. Simple linear regression; introduction, inferences, diagonstics, remedial measures, simultaneous inference. Matrix approach in linear regression. Multiple regression; inference, remedial measures, extra sums of squares, partial determinations, standardized models, use of indicator and mixed variables, polynomial regression, model selection and validation, diagnostics, remedial measures, multicollinearity and effects, autocorrelation. Single and multifactor analysis of variance: analysis of factor level means, interactions, inferences, diagnostics and remedial measures. A statistical package must be used as tool. Optional topics may include: logistic regression, design of experiments, and forecasting. Prerequisite: STAT 202 or 333 or IDTH 201 or 220 or MATH 333
Schedule Details

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