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

Associate Professor

Degree

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

B.S., Occupational Therapy, School of Medicine and Health Sciences, University of North Dakota

Office

OSS 424

Phone

(651) 962-5397

Email

Mail

University of St. Thomas

Mail Number OSS 402

2115 Summit Avenue

St. Paul, MN 55105

Mail Number OSS 402

2115 Summit Avenue

St. Paul, MN 55105

** Research: **Transformational Learning in Higher Education, Applied Statistics Education, Psychometric Analysis, Assessment and Evaluation in Education.

Course - Section | Title | Days | Time | Location | |
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STAT 220 - 02 | Statistics I | M T W R - | 1300 - 1500 | OSS 313 | |

Description of course Genetics B/ Lab: | CRN: 30052 4 Credit Hours 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, chi-square, 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 |

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 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, chi-square, 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 | ||||

STAT 220 - 03 | Statistics I | M - W - F | 0935 - 1040 | OSS 313 | |

Description of course Genetics B/ Lab: | CRN: 41141 4 Credit Hours 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, chi-square, 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 | ||||

STAT 320 - 01 | Statistics II | M - W - F | 1215 - 1320 | OSS 333 | |

Description of course Genetics B/ Lab: | CRN: 43197 4 Credit Hours 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 multi-factor 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 |

Course - Section | Title | Days | Time | Location |
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