2 classes matched your search criteria.

Spring 2017  |  MBA 6120 Section 070: Data Analysis and Statistics for Managers (54437)

Instructor(s)
Class Component:
Lecture
Credits:
3 Credits
Grading Basis:
A-F only
Instructor Consent:
No Special Consent Required
Instruction Mode:
Completely Online
Class Attributes:
Online Course
Times and Locations:
wk Session
 
01/17/2017 - 04/10/2017
Off Campus
Virtual Rooms ONLINEONLY
Also Offered:
Course Catalog Description:
Concepts/ principles of business statistics, data analysis and presentation of results. Topics: exploratory data analysis, basic inferential procedures, statistical process control, time series/regression analysis, and analysis of variance. These methods are selected for their relevance to managerial decision making and problem solving. prereq: MBA student
Class Notes:
The course assessments are completed online with Proctorio software. Requirements include a computer/laptop that's equipped with a web camera and microphone and one that allows both Proctorio and web browser software to be installed. https://ay16.moodle.umn.edu/enrol/index.php?id=2670
Class Description:
The course begins with an overview of graphical and numerical methods for summarizing data. These tools are useful for exploratory data analysis. Much of the rest of the course is devoted to the use of inferential statistics. The basis for inferential statistics is the concept of the sampling distribution of a statistic. Some background is given first on statistical distributions, and then the sampling distribution of the sample mean is covered in detail. Once the concept of a sampling distribution is absorbed, basic statistical procedures such as the confidence interval estimation and hypothesis testing are easily understood. These are the basic statistical building blocks for a number of useful statistical procedures that follow, including simple linear regression, multiple linear regression, experimental design, and categorial data analysis. Throughout, we focus on basic concepts and the practical use of these methods in management environments. This course provides the background in statistical methods that is required for a number of other courses in the curriculum, such as marketing, operations, and finance.
Who Should Take This Class?:
This is a required course for Part-Time MBA students and is reserved for MBA students. If you are a non-MBA student seeking to take this course, fill out the petition form found at goo.gl/9Y9PR5. Additional information, including petition deadlines, can be found at http://carlsonschool.umn.edu/degrees/master-business-administration/part-time-mba/admissions/mba-course-petition-form
Learning Objectives:
During the course, the student should achieve the ability to: (1) formulate a management problem as a statistical problem if appropriate; (2) collect data and to execute basic statistical procedures in analysis; and (3) to interpret, critically evaluate, and implement the results of the statistical analysis. In particular, the student should be able to:
 generate and use basic graphical and numerical descriptive methods;
 apply basic estimation and testing procedures;
 estimate and interpret the parameters of simple and multiple regression models; to test the utility of the models and to use them for estimation and prediction;
 think statistically about issues facing her/his organization
 recognize when statistical methods are effective, and when they are not, translate,
communicate, and critically evaluate the results of statistical analyses
Textbooks:
https://bookstores.umn.edu/course-lookup/54437/1173
Instructor Supplied Information Last Updated:
8 February 2017

Spring 2017  |  MBA 6120 Section 090: Data Analysis and Statistics for Managers (54328)

Instructor(s)
Class Component:
Lecture
Credits:
3 Credits
Grading Basis:
A-F only
Instructor Consent:
No Special Consent Required
Instruction Mode:
In Person Term Based
Times and Locations:
wk Session
 
01/17/2017 - 04/10/2017
Sat 08:00AM - 11:20AM
UMTC, West Bank
Hanson Hall 1-106
Also Offered:
Course Catalog Description:
Concepts/ principles of business statistics, data analysis and presentation of results. Topics: exploratory data analysis, basic inferential procedures, statistical process control, time series/regression analysis, and analysis of variance. These methods are selected for their relevance to managerial decision making and problem solving. prereq: MBA student
Class Description:
The course begins with an overview of graphical and numerical methods for summarizing data. These tools are useful for exploratory data analysis. Much of the rest of the course is devoted to the use of inferential statistics. The basis for inferential statistics is the concept of the sampling distribution of a statistic. Some background is given first on statistical distributions, and then the sampling distribution of the sample mean is covered in detail. Once the concept of a sampling distribution is absorbed, basic statistical procedures such as the confidence interval estimation and hypothesis testing are easily understood. These are the basic statistical building blocks for a number of useful statistical procedures that follow, including simple linear regression, multiple linear regression, experimental design, and categorial data analysis. Throughout, we focus on basic concepts and the practical use of these methods in management environments. This course provides the background in statistical methods that is required for a number of other courses in the curriculum, such as marketing, operations, and finance.
Who Should Take This Class?:
This is a required course for Part-Time MBA students and is reserved for MBA students. If you are a non-MBA student seeking to take this course, fill out the petition form found at goo.gl/9Y9PR5. Additional information, including petition deadlines, can be found at http://carlsonschool.umn.edu/degrees/master-business-administration/part-time-mba/admissions/mba-course-petition-form
Learning Objectives:
During the course, the student should achieve the ability to: (1) formulate a management problem as a statistical problem if appropriate; (2) collect data and to execute basic statistical procedures in analysis; and (3) to interpret, critically evaluate, and implement the results of the statistical analysis. In particular, the student should be able to:
 generate and use basic graphical and numerical descriptive methods;
 apply basic estimation and testing procedures;
 estimate and interpret the parameters of simple and multiple regression models; to test the utility of the models and to use them for estimation and prediction;
 think statistically about issues facing her/his organization
 recognize when statistical methods are effective, and when they are not, translate,
communicate, and critically evaluate the results of statistical analyses
Textbooks:
https://bookstores.umn.edu/course-lookup/54328/1173
Instructor Supplied Information Last Updated:
8 February 2017

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