# Statistics

### Statistics (STAT) Course Offerings

Visit ClassFinder to vew the full course schedule.

## Fall 2017 Courses

Course - Section Title Days Time Location
STAT 220 - 01 Statistics I M - W - F - - 0815 - 0920 OSS 432

M - W - F - -

0815 - 0920

OSS 432

### Course Registration Number:

40842 (View in ClassFinder)

4

### Instructor:

David L. Ehren

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

## Schedule Details

Location Time Day(s)
STAT 220 - 02 Statistics I M - W - F - - 0815 - 0920 OSS 431

M - W - F - -

0815 - 0920

OSS 431

### Course Registration Number:

42491 (View in ClassFinder)

4

### Instructor:

Marc D. Isaacson

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

## Schedule Details

Location Time Day(s)
STAT 220 - 03 Statistics I M - W - F - - 0935 - 1040 OSS 328

M - W - F - -

0935 - 1040

OSS 328

### Course Registration Number:

40843 (View in ClassFinder)

4

### Instructor:

David L. Ehren

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

## Schedule Details

Location Time Day(s)
STAT 220 - 04 Statistics I M - W - - - - 1525 - 1700 OSS 329

M - W - - - -

1525 - 1700

OSS 329

### Course Registration Number:

40844 (View in ClassFinder)

4

### Instructor:

Daniel G. Brick

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

## Schedule Details

Location Time Day(s)
STAT 220 - 05 Statistics I M - W - F - - 1055 - 1200 OSS 328

M - W - F - -

1055 - 1200

OSS 328

### Course Registration Number:

40845 (View in ClassFinder)

4

### Instructor:

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

## Schedule Details

Location Time Day(s)
STAT 220 - 06 Statistics I M - W - F - - 1055 - 1200 OSS 431

M - W - F - -

1055 - 1200

OSS 431

### Course Registration Number:

40846 (View in ClassFinder)

4

### Instructor:

Mark E. Werness

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

## Schedule Details

Location Time Day(s)
STAT 220 - 07 Statistics I M - W - F - - 1215 - 1320 OSS 432

M - W - F - -

1215 - 1320

OSS 432

### Course Registration Number:

40847 (View in ClassFinder)

4

### Instructor:

Mark E. Werness

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

## Schedule Details

Location Time Day(s)
STAT 220 - 08 Statistics I M - W - - - - 1730 - 1915 OSS 432

M - W - - - -

1730 - 1915

OSS 432

### Course Registration Number:

40848 (View in ClassFinder)

4

### Instructor:

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

## Schedule Details

Location Time Day(s)
STAT 220 - 09 Statistics I - T - R - - - 0800 - 0940 OSS 432

- T - R - - -

0800 - 0940

OSS 432

### Course Registration Number:

40849 (View in ClassFinder)

### Instructor:

David L. Ehren

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

## Schedule Details

Location Time Day(s)
STAT 220 - 10 Statistics I - T - R - - - 0800 - 0940 OSS 431

- T - R - - -

0800 - 0940

OSS 431

### Course Registration Number:

40850 (View in ClassFinder)

4

### Instructor:

Marc D. Isaacson

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

## Schedule Details

Location Time Day(s)
STAT 220 - 11 Statistics I - T - R - - - 0955 - 1135 OSS 431

- T - R - - -

0955 - 1135

OSS 431

### Course Registration Number:

40851 (View in ClassFinder)

4

### Instructor:

Jonathan D. Vikesland

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

## Schedule Details

Location Time Day(s)
STAT 220 - 12 Statistics I - T - R - - - 0955 - 1135 OSS 328

- T - R - - -

0955 - 1135

OSS 328

### Course Registration Number:

40852 (View in ClassFinder)

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

## Schedule Details

Location Time Day(s)
STAT 220 - 13 Statistics I - T - R - - - 1330 - 1510 OSS 328

- T - R - - -

1330 - 1510

OSS 328

### Course Registration Number:

40853 (View in ClassFinder)

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

## Schedule Details

Location Time Day(s)
STAT 220 - 14 Statistics I - T - R - - - 1330 - 1510 OSS 432

- T - R - - -

1330 - 1510

OSS 432

### Course Registration Number:

41041 (View in ClassFinder)

4

### Instructor:

Sergey S. Berg

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

## Schedule Details

Location Time Day(s)
STAT 220 - 15 Statistics I - T - R - - - 1525 - 1700 OSS 329

- T - R - - -

1525 - 1700

OSS 329

### Course Registration Number:

40854 (View in ClassFinder)

4

### Instructor:

Daniel G. Brick

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

## Schedule Details

Location Time Day(s)
STAT 314 - 01 Mathematical Statistics M - W - F - - 1055 - 1200 BEC LL03

M - W - F - -

1055 - 1200

BEC LL03

### Course Registration Number:

40893 (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 recieve credit for MATH 314 may not receive credit for MATH 303.

## Schedule Details

Location Time Day(s)
STAT 314 - 02 Mathematical Statistics M - W - F - - 1215 - 1320 BEC 101

M - W - F - -

1215 - 1320

BEC 101

### Course Registration Number:

41177 (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 recieve 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:

42065 (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 333

## Schedule Details

Location Time Day(s)
STAT 400 - 01 Data Mining & Machine Learning - T - R - - - 1330 - 1510 OSS 431

- T - R - - -

1330 - 1510

OSS 431

### Course Registration Number:

42577 (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 2017 Courses

Course - Section Title Days Time Location

## J-Term 2018 Courses

Course - Section Title Days Time Location
STAT 201 - 01 Introductory Statistics II See Details * *

See Details

*

*

### Course Registration Number:

10071 (View in ClassFinder)

2

### Instructor:

Erin M. Curran

This will be a hybrid section of STAT 201. A majority of course topics will be addressed through online and independent learning activities assigned by the instructor. Two exams will be taken face-to-face on campus. Mandatory face to face class sessions are scheduled for January 2nd, 4th, 11th, 18th and 25th. While this class format will offer students a great deal of flexibility in learning the assigned coursework, strong self-directed learning and time management skills will be essential for success.

## Schedule Details

Location Time Day(s)
OSS 3331030-1230- T - R - - -
-- - - - - - -
OSS 3331030-123011 Jan '18
OSS 3331030-123018 Jan '18
OSS 3331030-123025 Jan '18

## J-Term 2018 Courses

Course - Section Title Days Time Location

## Spring 2018 Courses

Course - Section Title Days Time Location
STAT 220 - 01 Statistics I M - W - F - - 0815 - 0920 OSS 432

M - W - F - -

0815 - 0920

OSS 432

### Course Registration Number:

20703 (View in ClassFinder)

4

### Instructor:

Staff

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

## Schedule Details

Location Time Day(s)
STAT 220 - 02 Statistics I M - W - F - - 0935 - 1040 OSS 431

M - W - F - -

0935 - 1040

OSS 431

### Course Registration Number:

20704 (View in ClassFinder)

4

### Instructor:

Staff

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

## Schedule Details

Location Time Day(s)
STAT 220 - 03 Statistics I M - W - F - - 0935 - 1040 OSS 329

M - W - F - -

0935 - 1040

OSS 329

### Course Registration Number:

20705 (View in ClassFinder)

4

### Instructor:

Staff

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

## Schedule Details

Location Time Day(s)
STAT 220 - 04 Statistics I M - W - F - - 1055 - 1200 OSS 432

M - W - F - -

1055 - 1200

OSS 432

### Course Registration Number:

20706 (View in ClassFinder)

4

### Instructor:

Mark E. Werness

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

## Schedule Details

Location Time Day(s)
STAT 220 - 05 Statistics I M - W - F - - 1335 - 1440 OSS 333

M - W - F - -

1335 - 1440

OSS 333

### Course Registration Number:

20707 (View in ClassFinder)

4

### Instructor:

Mark E. Werness

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

## Schedule Details

Location Time Day(s)
STAT 220 - 06 Statistics I M - W - - - - 1335 - 1510 OSS 432

M - W - - - -

1335 - 1510

OSS 432

### Course Registration Number:

20708 (View in ClassFinder)

4

### Instructor:

Sergey S. Berg

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

## Schedule Details

Location Time Day(s)
STAT 220 - 07 Statistics I M - W - - - - 1525 - 1700 OSS 432

M - W - - - -

1525 - 1700

OSS 432

### Course Registration Number:

20709 (View in ClassFinder)

4

### Instructor:

Staff

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

## Schedule Details

Location Time Day(s)
STAT 220 - 08 Statistics I M - W - - - - 1730 - 1915 OSS 432

M - W - - - -

1730 - 1915

OSS 432

### Course Registration Number:

20710 (View in ClassFinder)

4

### Instructor:

Staff

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

## Schedule Details

Location Time Day(s)
STAT 220 - 09 Statistics I - T - R - - - 0800 - 0940 OSS 431

- T - R - - -

0800 - 0940

OSS 431

### Course Registration Number:

20711 (View in ClassFinder)

4

### Instructor:

Staff

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

## Schedule Details

Location Time Day(s)
STAT 220 - 10 Statistics I - T - R - - - 0800 - 0940 OSS 432

- T - R - - -

0800 - 0940

OSS 432

### Course Registration Number:

20712 (View in ClassFinder)

4

### Instructor:

Staff

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

## Schedule Details

Location Time Day(s)
STAT 220 - 11 Statistics I - T - R - - - 0955 - 1135 OSS 431

- T - R - - -

0955 - 1135

OSS 431

### Course Registration Number:

20713 (View in ClassFinder)

4

### Instructor:

Staff

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

## Schedule Details

Location Time Day(s)
STAT 220 - 12 Statistics I - T - R - - - 0955 - 1135 OSS 329

- T - R - - -

0955 - 1135

OSS 329

### Course Registration Number:

20714 (View in ClassFinder)

4

### Instructor:

Staff

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

## Schedule Details

Location Time Day(s)
STAT 220 - 13 Statistics I - T - R - - - 1330 - 1510 OSS 329

- T - R - - -

1330 - 1510

OSS 329

### Course Registration Number:

20715 (View in ClassFinder)

4

### Instructor:

Sergey S. Berg

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

## Schedule Details

Location Time Day(s)
STAT 220 - 14 Statistics I - T - R - - - 1525 - 1700 OSS 329

- T - R - - -

1525 - 1700

OSS 329

### Course Registration Number:

20716 (View in ClassFinder)

4

### Instructor:

Staff

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

## 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:

20957 (View in ClassFinder)

4

### Instructor:

Erin M. Curran

This course provides students with the knowledge and skils 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.

## Schedule Details

Location Time Day(s)
STAT 314 - 01 Mathematical Statistics M - W - - - - 1525 - 1700 OSS 226

M - W - - - -

1525 - 1700

OSS 226

### Course Registration Number:

21077 (View in ClassFinder)

4

### Instructor:

Staff

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 recieve credit for MATH 314 may not receive credit for MATH 303.

## Schedule Details

Location Time Day(s)
STAT 333 - D01 Applied Statistical Methods - T - R - - - 0955 - 1135 OSS 227

- T - R - - -

0955 - 1135

OSS 227

### Course Registration Number:

20702 (View in ClassFinder)

4

### Instructor:

Staff

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 226

- T - R - - -

1330 - 1510

OSS 226

### Course Registration Number:

21092 (View in ClassFinder)

4

### Instructor:

Staff

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 - - - 1525 - 1700 OSS 429

- T - R - - -

1525 - 1700

OSS 429

### Course Registration Number:

22277 (View in ClassFinder)

4

### Instructor:

Sergey S. Berg

(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)

## Spring 2018 Courses

Course - Section Title Days Time Location