Statistics (STAT) Course Offerings

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Summer 2018 Courses

Course - Section Title Days Time Location
STAT 220 - 01 Statistics I See Details * *

Days of Week:

See Details

Time of Day:

*

Location:

*

Course Registration Number:

30031 (View in ClassFinder)

Credit Hours:

4

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, 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 NOTE: This is a hybrid section of STAT 220 where approximately 50% of course topics will be addressed through face-to-face instruction on scheduled class days. This means that roughly 50% of STAT 220 topics will be addressed through online and independent learning activities assigned by the instructor. These activities will be completed on students’ own time and in accordance with the class schedule. Homework will be assigned in addition to online and independent learning activities. All exams will be completed on campus during a scheduled face-to-face class period. While this class format will offer students a great deal of flexibility in learning assigned coursework, strong self-directed learning and time management skills will be essential for success.

Schedule Details

Location Time Day(s)
OSS 4291230-1430M - W - - - -
-- - - - - - -
STAT 220 - 02 Statistics I M T W R - - - 1715 - 1915 OSS 432

Days of Week:

M T W R - - -

Time of Day:

1715 - 1915

Location:

OSS 432

Course Registration Number:

30423 (View in ClassFinder)

Credit Hours:

4

Instructor:

James P. Normington

Statistics I is composed of an in-depth study of the processes through which statistics are applied in order to learn about environments and events. In this course, there is an intensive focus on the application, analysis, interpretation, and presentation of both descriptive and inferential statistics in myriad contexts. Topics covered include analytical and graphical tools for summarizing categorical and quantitative variables; correlation and simple linear regression; sampling strategies and research design; probability, probability models, and random variables; sampling distribution models; inference for one and two proportions (z); inference for one and two or more independent means (z, t and F); inference for paired means (t); inference for comparing counts (Goodness of Fit, Test of Independence, Test of Homogeneity); and inference for simple linear regression (t and F). Students must select both a lecture and software-specific laboratory section (see academic advisor for a recommendation) for enrollment in STAT 220. Prerequsistes: Math placement at level of MATH 108 or above; or successful completion of MATH 100, 101, 105, 108, 109, 111 or 113.

Schedule Details

Location Time Day(s)

Summer 2018 Courses

Course - Section Title Days Time Location

Fall 2018 Courses

Course - Section Title Days Time Location
STAT 220 - 01 Statistics I M - W - F - - 0935 - 1040 JRC 126

Days of Week:

M - W - F - -

Time of Day:

0935 - 1040

Location:

JRC 126

Course Registration Number:

40825 (View in ClassFinder)

Credit Hours:

4

Instructor:

Mark E. Werness

NOTE: Students registering for lecture STAT 220 01 must also register for lab 51, 52, or 53. Formerly IDTH 220. Statistics I is composed of an in-depth study of the processes through which statistics are applied in order to learn about environments and events. In this course, there is an intensive focus on the application, analysis, interpretation, and presentation of both descriptive and inferential statistics in myriad contexts. Topics covered include analytical and graphical tools for summarizing categorical and quantitative variables; correlation and simple linear regression; sampling strategies and research design; probability, probability models, and random variables; sampling distribution models; inference for one and two proportions; inference for one and two or more independent means; inference for paired means; inference for comparing counts; and inference for simple linear regression. Students must enroll in both a lecture section and software-specific laboratory section, in the same academic semester, to successfully complete STAT 220. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at MATH 108 or above; or MATH 100, 101, or 105. NOTE: Students who receive credit for STAT 220 may not receive credit for STAT 201.

Schedule Details

Location Time Day(s)
STAT 220 - 02 Statistics I M - W - F - - 1055 - 1200 JRC 126

Days of Week:

M - W - F - -

Time of Day:

1055 - 1200

Location:

JRC 126

Course Registration Number:

41939 (View in ClassFinder)

Credit Hours:

4

Instructor:

Mark E. Werness

NOTE: Students registering for lecture STAT 220 02 must also register for lab 54, 55, or 56. Formerly IDTH 220. Statistics I is composed of an in-depth study of the processes through which statistics are applied in order to learn about environments and events. In this course, there is an intensive focus on the application, analysis, interpretation, and presentation of both descriptive and inferential statistics in myriad contexts. Topics covered include analytical and graphical tools for summarizing categorical and quantitative variables; correlation and simple linear regression; sampling strategies and research design; probability, probability models, and random variables; sampling distribution models; inference for one and two proportions; inference for one and two or more independent means; inference for paired means; inference for comparing counts; and inference for simple linear regression. Students must enroll in both a lecture section and software-specific laboratory section, in the same academic semester, to successfully complete STAT 220. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at MATH 108 or above; or MATH 100, 101, or 105. NOTE: Students who receive credit for STAT 220 may not receive credit for STAT 201.

Schedule Details

Location Time Day(s)
STAT 220 - 03 Statistics I - T - R - - - 0955 - 1135 OWS 150

Days of Week:

- T - R - - -

Time of Day:

0955 - 1135

Location:

OWS 150

Course Registration Number:

40826 (View in ClassFinder)

Credit Hours:

4

Instructor:

Sergey S. Berg

NOTE: Students registering for lecture STAT 220 03 must also register for lab 57, 58, or 59. Formerly IDTH 220. Statistics I is composed of an in-depth study of the processes through which statistics are applied in order to learn about environments and events. In this course, there is an intensive focus on the application, analysis, interpretation, and presentation of both descriptive and inferential statistics in myriad contexts. Topics covered include analytical and graphical tools for summarizing categorical and quantitative variables; correlation and simple linear regression; sampling strategies and research design; probability, probability models, and random variables; sampling distribution models; inference for one and two proportions; inference for one and two or more independent means; inference for paired means; inference for comparing counts; and inference for simple linear regression. Students must enroll in both a lecture section and software-specific laboratory section, in the same academic semester, to successfully complete STAT 220. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at MATH 108 or above; or MATH 100, 101, or 105. NOTE: Students who receive credit for STAT 220 may not receive credit for STAT 201.

Schedule Details

Location Time Day(s)
STAT 220 - 04 Statistics I - T - R - - - 1330 - 1510 OWS 150

Days of Week:

- T - R - - -

Time of Day:

1330 - 1510

Location:

OWS 150

Course Registration Number:

40827 (View in ClassFinder)

Credit Hours:

4

Instructor:

Erin M. Curran

NOTE: Students registering for lecture STAT 220 04 must also register for lab 60, 61, or 62. Formerly IDTH 220. Statistics I is composed of an in-depth study of the processes through which statistics are applied in order to learn about environments and events. In this course, there is an intensive focus on the application, analysis, interpretation, and presentation of both descriptive and inferential statistics in myriad contexts. Topics covered include analytical and graphical tools for summarizing categorical and quantitative variables; correlation and simple linear regression; sampling strategies and research design; probability, probability models, and random variables; sampling distribution models; inference for one and two proportions; inference for one and two or more independent means; inference for paired means; inference for comparing counts; and inference for simple linear regression. Students must enroll in both a lecture section and software-specific laboratory section, in the same academic semester, to successfully complete STAT 220. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at MATH 108 or above; or MATH 100, 101, or 105. NOTE: Students who receive credit for STAT 220 may not receive credit for STAT 201.

Schedule Details

Location Time Day(s)
STAT 220 - 05 Statistics I - T - R - - - 1525 - 1700 OWS 150

Days of Week:

- T - R - - -

Time of Day:

1525 - 1700

Location:

OWS 150

Course Registration Number:

40828 (View in ClassFinder)

Credit Hours:

4

Instructor:

Amelia A. McNamara

NOTE: Students registering for lecture STAT 220 05 must also register for lab 63, 64, or 65. Formerly IDTH 220. Statistics I is composed of an in-depth study of the processes through which statistics are applied in order to learn about environments and events. In this course, there is an intensive focus on the application, analysis, interpretation, and presentation of both descriptive and inferential statistics in myriad contexts. Topics covered include analytical and graphical tools for summarizing categorical and quantitative variables; correlation and simple linear regression; sampling strategies and research design; probability, probability models, and random variables; sampling distribution models; inference for one and two proportions; inference for one and two or more independent means; inference for paired means; inference for comparing counts; and inference for simple linear regression. Students must enroll in both a lecture section and software-specific laboratory section, in the same academic semester, to successfully complete STAT 220. This course fulfills the third course in natural Science and Mathematics and Quantitative Reasoning requirement in the core curriculum. Prerequisites: Math placement at MATH 108 or above; or MATH 100, 101, or 105. NOTE: Students who receive credit for STAT 220 may not receive credit for STAT 201.

Schedule Details

Location Time Day(s)
STAT 220 - 51 Statistics I (Lab) - T - - - - - 0800 - 0940 OSS 431

Days of Week:

- T - - - - -

Time of Day:

0800 - 0940

Location:

OSS 431

Course Registration Number:

40829 (View in ClassFinder)

Credit Hours:

0

Instructor:

David L. Ehren

This lab section will use JMP for data analysis. NOTE: Students registering for this lab must also register for STAT 220 01 lecture.

Schedule Details

Location Time Day(s)
STAT 220 - 52 Statistics I (Lab) - T - - - - - 1730 - 1915 OSS 431

Days of Week:

- T - - - - -

Time of Day:

1730 - 1915

Location:

OSS 431

Course Registration Number:

40830 (View in ClassFinder)

Credit Hours:

0

Instructor:

David L. Ehren

This lab section will use SPSS for data analysis. NOTE: Students registering for this lab must also register for STAT 220 01 lecture.

Schedule Details

Location Time Day(s)
STAT 220 - 53 Statistics I (Lab) - - - R - - - 0800 - 0940 OSS 431

Days of Week:

- - - R - - -

Time of Day:

0800 - 0940

Location:

OSS 431

Course Registration Number:

40831 (View in ClassFinder)

Credit Hours:

0

Instructor:

David L. Ehren

This lab section will use JMP for data analysis. NOTE: Students registering for this lab must also register for STAT 220 01 lecture.

Schedule Details

Location Time Day(s)
STAT 220 - 54 Statistics I (Lab) - T - - - - - 0800 - 0940 OSS 432

Days of Week:

- T - - - - -

Time of Day:

0800 - 0940

Location:

OSS 432

Course Registration Number:

40832 (View in ClassFinder)

Credit Hours:

0

Instructor:

Marc D. Isaacson

This lab section will use EXCEL for data analysis. NOTE: Students registering for this lab must also register for STAT 220 02 lecture.

Schedule Details

Location Time Day(s)
STAT 220 - 55 Statistics I (Lab) - - - R - - - 0800 - 0940 OSS 432

Days of Week:

- - - R - - -

Time of Day:

0800 - 0940

Location:

OSS 432

Course Registration Number:

40833 (View in ClassFinder)

Credit Hours:

0

Instructor:

Marc D. Isaacson

This lab section will use EXCEL for data analysis. NOTE: Students registering for this lab must also register for STAT 220 02 lecture.

Schedule Details

Location Time Day(s)
STAT 220 - 56 Statistics I (Lab) - - - R - - - 1730 - 1915 OSS 431

Days of Week:

- - - R - - -

Time of Day:

1730 - 1915

Location:

OSS 431

Course Registration Number:

40834 (View in ClassFinder)

Credit Hours:

0

Instructor:

David L. Ehren

This lab section will use SPSS for data analysis. NOTE: Students registering for this lab must also register for STAT 220 02 lecture.

Schedule Details

Location Time Day(s)
STAT 220 - 57 Statistics I (Lab) M - - - - - - 1525 - 1700 OSS 431

Days of Week:

M - - - - - -

Time of Day:

1525 - 1700

Location:

OSS 431

Course Registration Number:

40835 (View in ClassFinder)

Credit Hours:

0

Instructor:

Daniel G. Brick

This lab section will use MINITAB for data analysis. NOTE: Students registering for this lab must also register for STAT 220 03 lecture.

Schedule Details

Location Time Day(s)
STAT 220 - 58 Statistics I (Lab) - - W - - - - 1525 - 1700 OSS 431

Days of Week:

- - W - - - -

Time of Day:

1525 - 1700

Location:

OSS 431

Course Registration Number:

40836 (View in ClassFinder)

Credit Hours:

0

Instructor:

Daniel G. Brick

This lab section will use EXCEL for data analysis. NOTE: Students registering for this lab must also register for STAT 220 03 lecture.

Schedule Details

Location Time Day(s)
STAT 220 - 59 Statistics I (Lab) - - - - F - - 1525 - 1700 OSS 432

Days of Week:

- - - - F - -

Time of Day:

1525 - 1700

Location:

OSS 432

Course Registration Number:

41013 (View in ClassFinder)

Credit Hours:

0

Instructor:

David L. Ehren

This lab section will use EXCEL for data analysis. NOTE: Students registering for this lab must also register for STAT 220 03 lecture.

Schedule Details

Location Time Day(s)
STAT 220 - 60 Statistics I (Lab) - T - - - - - 1525 - 1700 OSS 429

Days of Week:

- T - - - - -

Time of Day:

1525 - 1700

Location:

OSS 429

Course Registration Number:

40837 (View in ClassFinder)

Credit Hours:

0

Instructor:

James P. Normington

This lab section will use R for data analysis. NOTE: Students registering for this lab must also register for STAT 220 04 lecture.

Schedule Details

Location Time Day(s)
STAT 220 - 61 Statistics I (Lab) - - W - - - - 1730 - 1915 OSS 432

Days of Week:

- - W - - - -

Time of Day:

1730 - 1915

Location:

OSS 432

Course Registration Number:

42747 (View in ClassFinder)

Credit Hours:

0

Instructor:

Daniel G. Brick

This lab section will use EXCEL for data analysis. NOTE: Students registering for this lab must also register for STAT 220 04 lecture.

Schedule Details

Location Time Day(s)
STAT 220 - 62 Statistics I (Lab) - - - R - - - 1525 - 1700 OSS 429

Days of Week:

- - - R - - -

Time of Day:

1525 - 1700

Location:

OSS 429

Course Registration Number:

42748 (View in ClassFinder)

Credit Hours:

0

Instructor:

James P. Normington

This lab section will use R for data analysis. NOTE: Students registering for this lab must also register for STAT 220 04 lecture.

Schedule Details

Location Time Day(s)
STAT 220 - 63 Statistics I (Lab) M - - - - - - 1730 - 1915 OSS 432

Days of Week:

M - - - - - -

Time of Day:

1730 - 1915

Location:

OSS 432

Course Registration Number:

42749 (View in ClassFinder)

Credit Hours:

0

Instructor:

Daniel G. Brick

This lab section will use MINITAB for data analysis. NOTE: Students registering for this lab must also register for STAT 220 05 lecture.

Schedule Details

Location Time Day(s)
STAT 220 - 64 Statistics I (Lab) - T - - - - - 0955 - 1135 OSS 432

Days of Week:

- T - - - - -

Time of Day:

0955 - 1135

Location:

OSS 432

Course Registration Number:

42750 (View in ClassFinder)

Credit Hours:

0

Instructor:

Marc D. Isaacson

This lab section will use MINITAB for data analysis. NOTE: Students registering for this lab must also register for STAT 220 05 lecture.

Schedule Details

Location Time Day(s)
STAT 220 - 65 Statistics I (Lab) - - - R - - - 0955 - 1135 OSS 432

Days of Week:

- - - R - - -

Time of Day:

0955 - 1135

Location:

OSS 432

Course Registration Number:

42751 (View in ClassFinder)

Credit Hours:

0

Instructor:

Marc D. Isaacson

This lab section will use MINITAB for data analysis. NOTE: Students registering for this lab must also register for STAT 220 05 lecture.

Schedule Details

Location Time Day(s)
STAT 314 - 01 Mathematical Statistics - T - R - - - 1330 - 1510 OSS 226

Days of Week:

- T - R - - -

Time of Day:

1330 - 1510

Location:

OSS 226

Course Registration Number:

40874 (View in ClassFinder)

Credit Hours:

4

Instructor:

Christina P. Knudson

Populations and random sampling; sampling distributions. Theory of statistical estimation; criteria and methods of point and interval estimation. Theory of testing statistical hypotheses; non-parametric methods. Offered in fall semester. Prerequisite: MATH 240 and 313 NOTE: Students who receive credit for MATH 314 may not receive credit for MATH 303.

Schedule Details

Location Time Day(s)
STAT 314 - 02 Mathematical Statistics - T - R - - - 1525 - 1700 OSS 226

Days of Week:

- T - R - - -

Time of Day:

1525 - 1700

Location:

OSS 226

Course Registration Number:

41121 (View in ClassFinder)

Credit Hours:

4

Instructor:

Christina P. Knudson

Populations and random sampling; sampling distributions. Theory of statistical estimation; criteria and methods of point and interval estimation. Theory of testing statistical hypotheses; non-parametric methods. Offered in fall semester. Prerequisite: MATH 240 and 313 NOTE: Students who receive credit for MATH 314 may not receive credit for MATH 303.

Schedule Details

Location Time Day(s)
STAT 320 - D01 Statistics II - T - R - - - 1330 - 1510 OSS 432

Days of Week:

- T - R - - -

Time of Day:

1330 - 1510

Location:

OSS 432

Course Registration Number:

41725 (View in ClassFinder)

Credit Hours:

4

Instructor:

Amelia A. McNamara

Applied linear regression models. Simple linear regression; introduction, inferences, diagnostics, 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: One of the following, STAT 201, STAT 220, STAT 333, MATH 303

Schedule Details

Location Time Day(s)
STAT 400 - 01 Data Mining & Machine Learning M - W - - - - 1335 - 1510 OSS 429

Days of Week:

M - W - - - -

Time of Day:

1335 - 1510

Location:

OSS 429

Course Registration Number:

42006 (View in ClassFinder)

Credit Hours:

4

Instructor:

Mark E. Werness

(Formerly IDTH 400) Introduction to statistical learning methods, from a statistical and computational perspective, to deal with massive and complex data. Topics include: Introduction; creating a project and diagram. Data preparation; defining and exploring data sources. Pattern discovery; cluster analysis, market basket analysis. Decision trees; cultivating and pruning decision trees, autonomous tree growth. Regression; transforming inputs, categorical inputs, polynomial regression. Neural Networks; input selection, stopped training. Model assessment; fit statistics, graphs, separate sampling. Model implementation; scored data sets, score code models. Applications. This course will give the basic ideas and intuition behind these methods, and special emphasis will be placed on their application through statistical software. Prerequisites: CISC 130 or 131, and MATH 113, and STAT 320 or 333.

Schedule Details

Location Time Day(s)

Fall 2018 Courses

Course - Section Title Days Time Location

J-Term 2019 Courses

Course - Section Title Days Time Location

J-Term 2019 Courses

Course - Section Title Days Time Location