# Statistics

### Statistics (STAT) Course Offerings

Visit ClassFinder to vew the full course schedule.

## Fall 2018 Courses

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

M - W - F - -

0935 - 1040

JRC 126

### Course Registration Number:

40825 (View in ClassFinder)

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

M - W - F - -

1055 - 1200

JRC 126

### Course Registration Number:

41939 (View in ClassFinder)

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

- T - R - - -

0955 - 1135

OWS 150

### Course Registration Number:

40826 (View in ClassFinder)

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

- T - R - - -

1330 - 1510

OWS 150

### Course Registration Number:

40827 (View in ClassFinder)

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

- T - R - - -

1525 - 1700

OWS 150

### Course Registration Number:

40828 (View in ClassFinder)

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

- T - - - - -

0800 - 0940

OSS 431

### Course Registration Number:

40829 (View in ClassFinder)

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

- T - - - - -

1730 - 1915

OSS 431

### Course Registration Number:

40830 (View in ClassFinder)

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

- - - R - - -

0800 - 0940

OSS 431

### Course Registration Number:

40831 (View in ClassFinder)

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

- T - - - - -

0800 - 0940

OSS 432

### Course Registration Number:

40832 (View in ClassFinder)

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

- - - R - - -

0800 - 0940

OSS 432

### Course Registration Number:

40833 (View in ClassFinder)

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

- - - R - - -

1730 - 1915

OSS 431

### Course Registration Number:

40834 (View in ClassFinder)

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

M - - - - - -

1525 - 1700

OSS 431

### Course Registration Number:

40835 (View in ClassFinder)

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

- - W - - - -

1525 - 1700

OSS 431

### Course Registration Number:

40836 (View in ClassFinder)

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

- - - - F - -

1525 - 1700

OSS 432

### Course Registration Number:

41013 (View in ClassFinder)

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

- T - - - - -

1525 - 1700

OSS 429

### Course Registration Number:

40837 (View in ClassFinder)

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

- - W - - - -

1730 - 1915

OSS 432

### Course Registration Number:

42747 (View in ClassFinder)

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

- - - R - - -

1525 - 1700

OSS 429

### Course Registration Number:

42748 (View in ClassFinder)

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

M - - - - - -

1730 - 1915

OSS 432

### Course Registration Number:

42749 (View in ClassFinder)

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

- T - - - - -

0955 - 1135

OSS 432

### Course Registration Number:

42750 (View in ClassFinder)

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

- T - R - - -

1330 - 1510

OSS 226

### Course Registration Number:

40874 (View in ClassFinder)

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

- T - R - - -

1525 - 1700

OSS 226

### Course Registration Number:

41121 (View in ClassFinder)

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

- T - R - - -

1330 - 1510

OSS 432

### Course Registration Number:

41725 (View in ClassFinder)

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

M - W - - - -

1335 - 1510

OSS 429

### Course Registration Number:

42006 (View in ClassFinder)

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
STAT 201 - 01 Introductory Statistics II - T W R F - - 0800 - 0940 OSS 431

- T W R F - -

0800 - 0940

OSS 431

### Course Registration Number:

10060 (View in ClassFinder)

2

### Instructor:

Marc D. Isaacson

(Formerly IDTH 201) This course is for students desiring to satisfy the coverage of STAT 220 ( a full semester of statistics), when less than one full semester of statistics has been taken. Review of 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. Introduction to basic applications: tests of independence, analysis of variance and linear regression. A statistical package must be used as tool. Prerequisite: STAT 206 (IDTH 206) or at least .35 semester, but less than one semester of statistics. Note: Students who receive credit for STAT 201 may not receive credit for STAT 220.

## Schedule Details

Location Time Day(s)
STAT 220 - 01 Statistics I - T W R F - - 1000 - 1300 OSS LL18

- T W R F - -

1000 - 1300

OSS LL18

### Course Registration Number:

10035 (View in ClassFinder)

4

### Instructor:

Erin M. Curran

NOTE: Students registering for lecture STAT 220 01 must also register for lab 51. 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 - T W R F - - 1330 - 1510 OSS 429

- T W R F - -

1330 - 1510

OSS 429

### Course Registration Number:

10334 (View in ClassFinder)

0

### Instructor:

Erin M. Curran

This lab section will use MINITAB for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. Note: Students registering for this lab must also register for STAT 220 01 lecture.

## Schedule Details

Location Time Day(s)

## J-Term 2019 Courses

Course - Section Title Days Time Location

## Spring 2019 Courses

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

M - W - F - -

0935 - 1040

JRC 126

### Course Registration Number:

20638 (View in ClassFinder)

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

M - W - F - -

1055 - 1200

JRC 126

### Course Registration Number:

20639 (View in ClassFinder)

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

- T - R - - -

0955 - 1135

OWS 150

### Course Registration Number:

20640 (View in ClassFinder)

4

### Instructor:

Amelia A. McNamara

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

- T - R - - -

1330 - 1510

OWS 150

### Course Registration Number:

20641 (View in ClassFinder)

4

### Instructor:

Sergey S. Berg

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

- T - R - - -

1525 - 1700

OWS 150

### Course Registration Number:

20642 (View in ClassFinder)

4

### Instructor:

Sergey S. Berg

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 - - - - - 1330 - 1510 OSS 432

- T - - - - -

1330 - 1510

OSS 432

### Course Registration Number:

20643 (View in ClassFinder)

0

### Instructor:

Erin M. Curran

This lab section will use SPSS for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. 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 428

- T - - - - -

1730 - 1915

OSS 428

### Course Registration Number:

20644 (View in ClassFinder)

0

### Instructor:

Daniel G. Brick

This lab section will use MINITAB for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. 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 - - - 1330 - 1510 OSS 432

- - - R - - -

1330 - 1510

OSS 432

### Course Registration Number:

20645 (View in ClassFinder)

0

### Instructor:

Erin M. Curran

This lab section will use SPSS for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. 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

- T - - - - -

0800 - 0940

OSS 432

### Course Registration Number:

20646 (View in ClassFinder)

0

### Instructor:

Marc D. Isaacson

This lab section will use EXCEL for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. 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

- - - R - - -

0800 - 0940

OSS 432

### Course Registration Number:

20647 (View in ClassFinder)

0

### Instructor:

Marc D. Isaacson

This lab section will use EXCEL for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. 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 428

- - - R - - -

1730 - 1915

OSS 428

### Course Registration Number:

20648 (View in ClassFinder)

0

### Instructor:

Daniel G. Brick

This lab section will use MINITAB for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. 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) - T - - - - - 1525 - 1700 OSS 431

- T - - - - -

1525 - 1700

OSS 431

### Course Registration Number:

20649 (View in ClassFinder)

0

### Instructor:

Daniel G. Brick

This lab section will use MINITAB for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. 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) - T - - - - - 0800 - 0940 OSS 431

- T - - - - -

0800 - 0940

OSS 431

### Course Registration Number:

20650 (View in ClassFinder)

0

### Instructor:

David L. Ehren

This lab section will use EXCEL for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. 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) - - - R - - - 0800 - 0940 OSS 431

- - - R - - -

0800 - 0940

OSS 431

### Course Registration Number:

20651 (View in ClassFinder)

0

### Instructor:

David L. Ehren

This lab section will use EXCEL for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. 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

- T - - - - -

1525 - 1700

OSS 429

### Course Registration Number:

21869 (View in ClassFinder)

0

### Instructor:

James P. Normington

This lab section will use R for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. 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) - - - R - - - 1730 - 1915 OSS 432

- - - R - - -

1730 - 1915

OSS 432

### Course Registration Number:

22298 (View in ClassFinder)

0

### Instructor:

This lab section will use JMP for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. 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

- - - R - - -

1525 - 1700

OSS 429

### Course Registration Number:

22299 (View in ClassFinder)

0

### Instructor:

James P. Normington

This lab section will use R for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. 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 - T - - - - - 1730 - 1915 OSS 432

- T - - - - -

1730 - 1915

OSS 432

### Course Registration Number:

22300 (View in ClassFinder)

0

### Instructor:

This lab section will use JMP for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. 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

- T - - - - -

0955 - 1135

OSS 432

### Course Registration Number:

22301 (View in ClassFinder)

0

### Instructor:

David L. Ehren

This lab section will use EXCEL for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. 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

- - - R - - -

0955 - 1135

OSS 432

### Course Registration Number:

22302 (View in ClassFinder)

0

### Instructor:

Marc D. Isaacson

This lab section will use EXCEL for data analysis. Please check with your academic advisor to determine whether this is the recommended lab for your intended major. Note: Students registering for this lab must also register for STAT 220 05 lecture.

## Schedule Details

Location Time Day(s)
STAT 310 - 01 Biostatistics M - W - - - - 1525 - 1700 OSS 429

M - W - - - -

1525 - 1700

OSS 429

### Course Registration Number:

20870 (View in ClassFinder)

4

### Instructor:

Erin M. Curran

This course provides students with the knowledge and skills needed to effectively apply basic statistical methods in health related fields, such as Biology, Medicine, and Public Health. Students learn inferential statistical techniques involving topics in estimation, hypothesis testing, nonparametric methods, clinical trials, contingency tables, review of analysis of variance and linear regression, and a brief introduction to experimental design. Prerequisites: STAT 220.

## Schedule Details

Location Time Day(s)
STAT 314 - 01 Mathematical Statistics - T - R - - - 0800 - 0940 OWS 250

- T - R - - -

0800 - 0940

OWS 250

### Course Registration Number:

20987 (View in ClassFinder)

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 - - - 0955 - 1135 OSS 429

- T - R - - -

0955 - 1135

OSS 429

### Course Registration Number:

22362 (View in ClassFinder)

4

### Instructor:

Sergey S. Berg

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 333 - D01 Applied Statistical Methods M - W - - - - 1525 - 1700 OSS 214

M - W - - - -

1525 - 1700

OSS 214

### Course Registration Number:

20637 (View in ClassFinder)

4

### Instructor:

Regression and exponential smoothing methods; Stochastic Time Series: auto- and cross-correlation, autoregressive moving average models; application to forecasting. Prerequisites: MATH 303 or 314 or STAT 314 or permission of instructor

## Schedule Details

Location Time Day(s)
STAT 333 - D02 Applied Statistical Methods - T - R - - - 1330 - 1510 OSS 214

- T - R - - -

1330 - 1510

OSS 214

### Course Registration Number:

20999 (View in ClassFinder)

4

### Instructor:

Regression and exponential smoothing methods; Stochastic Time Series: auto- and cross-correlation, autoregressive moving average models; application to forecasting. Prerequisites: MATH 303 or 314 or STAT 314 or permission of instructor

## Schedule Details

Location Time Day(s)
STAT 360 - 01 Advanced Statistical Software - T - R - - - 1330 - 1510 OSS 415

- T - R - - -

1330 - 1510

OSS 415

### Course Registration Number:

21565 (View in ClassFinder)

4

### Instructor:

Amelia A. McNamara

(Formerly IDTH 360) This course introduces students to an advanced statistical software package to effectively apply statistical methods, in general. Students create data sets from raw data files, create variables within a data set, append and/or modify data sets, create subsets, then apply a whole host of statistical procedures, create graphs and produce reports. The course will be based on several leading advanced statistical software packages, which will be chosen from semester to semester to match the needs of the community. Prerequisites: STAT 220 or STAT 314

## Schedule Details

Location Time Day(s)
STAT 360 - 02 Advanced Statistical Software - T - R - - - 1525 - 1700 OSS 415

- T - R - - -

1525 - 1700

OSS 415

### Course Registration Number:

22363 (View in ClassFinder)

4

### Instructor:

Amelia A. McNamara

(Formerly IDTH 360) This course introduces students to an advanced statistical software package to effectively apply statistical methods, in general. Students create data sets from raw data files, create variables within a data set, append and/or modify data sets, create subsets, then apply a whole host of statistical procedures, create graphs and produce reports. The course will be based on several leading advanced statistical software packages, which will be chosen from semester to semester to match the needs of the community. Prerequisites: STAT 220 or STAT 314

## Schedule Details

Location Time Day(s)
STAT 370 - 01 Bayesian Models - T - R - - - 1525 - 1700 OSS 227

- T - R - - -

1525 - 1700

OSS 227

### Course Registration Number:

22368 (View in ClassFinder)

4