I. General Information
1. Course Title:
Introduction to Statistics
2. Course Prefix & Number:
MATH 1460
3. Course Credits and Contact Hours:
Credits: 4
Lecture Hours: 4
Lab Hours: 0
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.
5. Placement Tests Required:
Accuplacer (specify test): 
College Level Math 
Score: 
50 
6. Prerequisite Courses:
MATH 1460  Introduction to Statistics
The required Course(s) from 1 of the following groups...
7. Other Prerequisites
Grade of “C” or higher in prerequisite course.
9. Corequisite Courses:
MATH 1460  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

3. Prior Learning  the following prior learning methods are acceptable for this course:
Advanced Placement (AP)
III. Course Purpose
2. MN Transfer Curriculum (General Education) Courses  This course fulfills the following goal area(s) of the MN Transfer Curriculum:
Goal 4 – Mathematical/Logical Reasoning
IV. Learning Outcomes
1. CollegeWide Outcomes
CollegeWide Outcomes/Competencies 
Students will be able to: 
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 
Clearly express mathematical/logical ideas in writing 
4 
Explain what constitutes a valid mathematical argument 
4 
Apply higherorder problemsolving and/or modeling strategies 
4 
V. Topical Outline
Listed below are major areas of content typically covered in this course.
1. Lecture Sessions
 Introduction to Statistics
 Define types of data
 Use critical thinking skills
 Describe methods of collecting data
 Summarizing and Graphing Data
 Construct graphical representations of data and estimate common numerical measures from them
 Analyze misuse of graphs for data
 Describe and compare data in statistical terms
 Compute measures of center, zscores, variation, quartiles, and percentile ranks from data and give interpretations of these numerical measures
 Develop boxplot representations and interpret distribution characteristics
 Probability
 Calculate basic probabilities
 Addition rule
 Multiplication rule
 Counting
 Discrete Probability Distributions
 Use binomial distributions to determine characteristics of data
 Normal Probability Distributions
 Apply normal approximation to estimate projected outcomes and percentiles for data that is normally distributed
 Estimates and Sample Sizes
 Compute and interpret confidence intervals and sample sizes for means, proportions, and variances
 Hypothesis Testing
 Perform hypothesis testing for claims about proportions, means, and variations and interpret the results of these tests
 Inferences from two samples
 Develop inferences about two proportions, two means and dependent samples.
 Correlation
 Compute and interpret the correlation coefficient as a measure of the strength of the linear association between two numeric values
 Linear Regression
 Apply regression methods to estimate dependent variable values
 Interpret slope and constant in regression equations.
 Goodnessoffit and Contingency Tables
 Determine correlations conducting goodnessoffit test
 Analysis of Variance
I. General Information
1. Course Title:
Introduction to Statistics
2. Course Prefix & Number:
MATH 1460
3. Course Credits and Contact Hours:
Credits: 4
Lecture Hours: 4
Lab Hours: 0
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.
5. Placement Tests Required:
Accuplacer (specify test): 
College Level Math 
Score: 
50 
6. Prerequisite Courses:
MATH 1460  Introduction to Statistics
The required Course(s) from 1 of the following groups...
7. Other Prerequisites
Grade of “C” or higher in prerequisite course.
9. Corequisite Courses:
MATH 1460  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

3. Prior Learning  the following prior learning methods are acceptable for this course:
Advanced Placement (AP)
III. Course Purpose
2. MN Transfer Curriculum (General Education) Courses  This course fulfills the following goal area(s) of the MN Transfer Curriculum:
Goal 4 – Mathematical/Logical Reasoning
IV. Learning Outcomes
1. CollegeWide Outcomes
CollegeWide Outcomes/Competencies 
Students will be able to: 
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 
Clearly express mathematical/logical ideas in writing 
4 
Explain what constitutes a valid mathematical argument 
4 
Apply higherorder problemsolving and/or modeling strategies 
4 
V. Topical Outline
Listed below are major areas of content typically covered in this course.
1. Lecture Sessions
 Introduction to Statistics
 Define types of data
 Use critical thinking skills
 Describe methods of collecting data
 Summarizing and Graphing Data
 Construct graphical representations of data and estimate common numerical measures from them
 Analyze misuse of graphs for data
 Describe and compare data in statistical terms
 Compute measures of center, zscores, variation, quartiles, and percentile ranks from data and give interpretations of these numerical measures
 Develop boxplot representations and interpret distribution characteristics
 Probability
 Calculate basic probabilities
 Addition rule
 Multiplication rule
 Counting
 Discrete Probability Distributions
 Use binomial distributions to determine characteristics of data
 Normal Probability Distributions
 Apply normal approximation to estimate projected outcomes and percentiles for data that is normally distributed
 Estimates and Sample Sizes
 Compute and interpret confidence intervals and sample sizes for means, proportions, and variances
 Hypothesis Testing
 Perform hypothesis testing for claims about proportions, means, and variations and interpret the results of these tests
 Inferences from two samples
 Develop inferences about two proportions, two means and dependent samples.
 Correlation
 Compute and interpret the correlation coefficient as a measure of the strength of the linear association between two numeric values
 Linear Regression
 Apply regression methods to estimate dependent variable values
 Interpret slope and constant in regression equations.
 Goodnessoffit and Contingency Tables
 Determine correlations conducting goodnessoffit test
 Analysis of Variance