I. General Information
1. Course Title:
Honors Introduction to Statistics
2. Course Prefix & Number:
MATH 1461
3. Course Credits and Contact Hours:
Credits: 4
Lecture Hours: 4
4. Course Description:
This course covers descriptive statistics, sampling, probability, probability distributions, normal probability distributions, estimates and sample sizes, hypothesis testing, correlation and regression, inferences of two samples, and process control. Much of the content of this course will involve independent learning with classroom lecture involving more in-depth involvement with statistical data. Students enrolled in this course will be required to do additional reading of statistical writings, participate in group projects, present projects to the class, and develop an original survey. Daily assignments will involve use of online homework to accompany the readings from the course. A student must be accepted into the honors program prior to registration.
5. Placement Tests Required:
| Accuplacer (specify test): |
College Level Math |
Score: |
50 |
| Other (specify test): |
ACT |
Score: |
18
|
6. Prerequisite Courses:
MATH 1461 - Honors Introduction to Statistics
Applies to all requirements
Accuplacer college level math score of 50 or higher, or MATh 1505 or MATH 1506, and Admission to the Honors Program
7. Other Prerequisites
Admission to the Honors Program
8. Prerequisite (Entry) Skills:
Arithmetic skills with whole numbers, integers, fractions/ratios, percentages and decimals.
Algebraic skills with linear & quadratic functions and equation manipulation.
9. Co-requisite Courses:
MATH 1461 - Honors Introduction to Statistics
There are no corequisites for this course.
II. Transfer and Articulation
1. Course Equivalency - similar course from other regional institutions:
|
Name of Institution
|
Course Number and Title
|
Credits
|
|
Bemidji State University
|
MATH2610 Applied Statistics
|
4
|
|
Normandale Community College
|
MATH1080 Introduction to Statistics
|
4
|
III. Course Purpose
MN Transfer Curriculum (General Education) Courses - This course fulfills the following goal area(s) of the MN Transfer Curriculum:
- Goal 2 – Critical Thinking
- Goal 4 – Mathematical/Logical Reasoning
IV. Learning Outcomes
1. College-Wide Outcomes
| College-Wide Outcomes/Competencies |
Students will be able to: |
| Demonstrate oral communication skills |
Presentation of projects, effectively demonstrating the use of statistical concepts. |
| Demonstrate written communication skills |
Written documentation to accompany studies that clearly outline all processes used in the project. |
| Demonstrate reading and listening skills |
Online homework and quizzes that will be completed prior to class. |
| Demonstrate interpersonal communication skills |
Group work involving project development. |
| Assess alternative solutions to a problem |
Be able to demonstrate multiple ways to analyze data. |
| Analyze and follow a sequence of operations |
Give detailed demonstration of how each stage of a study is developed and the data analyzed. |
| Apply abstract ideas to concrete situations |
Students will take raw data and develop descriptive and inferential deductions based on that data. |
| Utilize appropriate technology |
Students will use a graphing calculator to input statistical functions. |
2. Course Specific Outcomes - Students will be able to achieve the following measurable goals upon completion of
the course:
|
Expected Outcome
|
MnTC
Goal Area
|
|
Compute measures of center, z-scores, quartiles, and percentile ranks from data and give interpretations of these numerical measures
|
4b
|
|
Construct graphical representations of data and estimate common numerical measures from them
|
2a, 4b
|
|
Calculate basic probabilities
|
4d
|
|
Use binomial distributions to determine characteristics of data
|
4d
|
|
Apply normal approximation to estimate projected outcomes and percentiles for data that is normally distributed
|
4b,d
|
|
Compute and interpret confidence intervals and sample sizes for means, proportions, and variances
|
2a, c; 4a,b,c,d
|
|
Perform hypothesis testing for claims about proportions, means, and variations and interpret the results of these tests
|
4b,d
|
|
Develop inferences about two proportions, two means and dependent samples.
|
4d
|
|
Compute and interpret the correlation coefficient as a measure of the strength of the linear association between two numeric values
|
4a,b,d
|
|
Apply regression methods to estimate dependent variable values/interpret slope and constant in regression equations
|
4a,b,d
|
|
Conduct goodness-of-fit test to determine correlations
|
4b,c,d
|
|
Perform analysis of variance
|
2a,c; 4a,b,c,d
|
V. Topical Outline
Listed below are major areas of content typically covered in this course.
1. Lecture Sessions
|
Lecture Content Outline
|
|
Introduction to Statistics
|
|
Summarizing and Graphing Data
|
|
Describe and compare data in statistical terms
|
|
Probability
|
|
Discrete Probability Distributions
|
|
Normal Probability Distributions
|
|
Estimates and Sample Sizes
|
|
Hypothesis Testing
|
|
Inferences from two samples
|
|
Correlation
|
|
Linear Regression
|
|
Goodness-of-fit and Contingency Tables
|
|
Analysis of Variance
|
|
|
I. General Information
1. Course Title:
Honors Introduction to Statistics
2. Course Prefix & Number:
MATH 1461
3. Course Credits and Contact Hours:
Credits: 4
Lecture Hours: 4
4. Course Description:
This course covers descriptive statistics, sampling, probability, probability distributions, normal probability distributions, estimates and sample sizes, hypothesis testing, correlation and regression, inferences of two samples, and process control. Much of the content of this course will involve independent learning with classroom lecture involving more in-depth involvement with statistical data. Students enrolled in this course will be required to do additional reading of statistical writings, participate in group projects, present projects to the class, and develop an original survey. Daily assignments will involve use of online homework to accompany the readings from the course. A student must be accepted into the honors program prior to registration.
5. Placement Tests Required:
| Accuplacer (specify test): |
College Level Math |
Score: |
50 |
| Other (specify test): |
ACT |
Score: |
18
|
6. Prerequisite Courses:
MATH 1461 - Honors Introduction to Statistics
Applies to all requirements
Accuplacer college level math score of 50 or higher, or MATh 1505 or MATH 1506, and Admission to the Honors Program
7. Other Prerequisites
Admission to the Honors Program
8. Prerequisite (Entry) Skills:
Arithmetic skills with whole numbers, integers, fractions/ratios, percentages and decimals.
Algebraic skills with linear & quadratic functions and equation manipulation.
9. Co-requisite Courses:
MATH 1461 - Honors Introduction to Statistics
There are no corequisites for this course.
II. Transfer and Articulation
1. Course Equivalency - similar course from other regional institutions:
|
Name of Institution
|
Course Number and Title
|
Credits
|
|
Bemidji State University
|
MATH2610 Applied Statistics
|
4
|
|
Normandale Community College
|
MATH1080 Introduction to Statistics
|
4
|
III. Course Purpose
2. MN Transfer Curriculum (General Education) Courses - This course fulfills the following goal area(s) of the MN Transfer Curriculum:
- Goal 2 – Critical Thinking
- Goal 4 – Mathematical/Logical Reasoning
IV. Learning Outcomes
1. College-Wide Outcomes
| College-Wide Outcomes/Competencies |
Students will be able to: |
| Demonstrate oral communication skills |
Presentation of projects, effectively demonstrating the use of statistical concepts. |
| Demonstrate written communication skills |
Written documentation to accompany studies that clearly outline all processes used in the project. |
| Demonstrate reading and listening skills |
Online homework and quizzes that will be completed prior to class. |
| Demonstrate interpersonal communication skills |
Group work involving project development. |
| Analyze and follow a sequence of operations |
Give detailed demonstration of how each stage of a study is developed and the data analyzed. |
| Apply abstract ideas to concrete situations |
Students will take raw data and develop descriptive and inferential deductions based on that data. |
| Utilize appropriate technology |
Students will use a graphing calculator to input statistical functions. |
2. Course Specific Outcomes - Students will be able to achieve the following measurable goals upon completion of
the course:
|
Expected Outcome
|
MnTC
Goal Area
|
|
Compute measures of center, z-scores, quartiles, and percentile ranks from data and give interpretations of these numerical measures
|
4b
|
|
Construct graphical representations of data and estimate common numerical measures from them
|
2a, 4b
|
|
Calculate basic probabilities
|
4d
|
|
Use binomial distributions to determine characteristics of data
|
4d
|
|
Apply normal approximation to estimate projected outcomes and percentiles for data that is normally distributed
|
4b,d
|
|
Compute and interpret confidence intervals and sample sizes for means, proportions, and variances
|
2a, c; 4a,b,c,d
|
|
Perform hypothesis testing for claims about proportions, means, and variations and interpret the results of these tests
|
4b,d
|
|
Develop inferences about two proportions, two means and dependent samples.
|
4d
|
|
Compute and interpret the correlation coefficient as a measure of the strength of the linear association between two numeric values
|
4a,b,d
|
|
Apply regression methods to estimate dependent variable values/interpret slope and constant in regression equations
|
4a,b,d
|
|
Conduct goodness-of-fit test to determine correlations
|
4b,c,d
|
|
Perform analysis of variance
|
2a,c; 4a,b,c,d
|
V. Topical Outline
Listed below are major areas of content typically covered in this course.
1. Lecture Sessions
|
Lecture Content Outline
|
|
Introduction to Statistics
|
|
Summarizing and Graphing Data
|
|
Describe and compare data in statistical terms
|
|
Probability
|
|
Discrete Probability Distributions
|
|
Normal Probability Distributions
|
|
Estimates and Sample Sizes
|
|
Hypothesis Testing
|
|
Inferences from two samples
|
|
Correlation
|
|
Linear Regression
|
|
Goodness-of-fit and Contingency Tables
|
|
Analysis of Variance
|
|
|