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: 
35 
Other (specify test): 
Elementary Algebra 
Score: 
76

6. Prerequisite Courses:
MATH 1460  Introduction to Statistics
Applies to all requirements
Accuplacer College Level Math score of 50 or higher, or Math 0810 Math Pathways, or Math 0820 Intermediate Algebra, or MATH 1520 Intro to College Algebra
7. Other Prerequisites
Math ACT of 20
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:
Bemidji State University, MATH 2610 Applied Statistics, 4 credits
Normandale Community College, MATh 1080 Introduction to Statistics, 4 credits
3. Prior Learning  the following prior learning methods are acceptable for this course:
Advanced Placement (AP)
III. Course Purpose
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: 
Assess alternative solutions to a problem 
Reason and solve quantitative problems from a wide array of authentic contexts and everyday life situations. 
Apply abstract ideas to concrete situations 
Develop descriptive and inferential deductions based on raw data. 
Utilize appropriate technology 
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:
 Define and apply the meaning of descriptive statistics and statistical inference, describe the importance of statistics, and interpret examples of statistics in a professional context (MnTC Goal 4);
 Distinguish between a population and a sample (MnTC Goal 4);
 Calculate and explain the purpose of measures of locations and variability (MnTC Goal 4);
 Apply simple principles of probability (MnTC Goal 4);
 Compute probabilities related to both discrete and continuous random variables (MnTC Goal 4);
 Identify and analyze sampling distributions for statistical inferences (MnTC Goal 4);
 Identify and analyze confidence intervals for means and proportions (MnTC Goal 4);
 Compare and analyze data sets using descriptive statistics, parameter estimation, and hypothesis testing (MnTC Goal 4);
 Explain how the central limit theorem applies in inference, and use the theorem to construct confidence intervals (MnTC Goal 4);
 Calculate and interpret confidence intervals for one population average and one population proportion (MnTC Goal 4);
 Differentiate between type I and type II errors (MnTC Goal 4);
 Conduct and interpret hypothesis tests (MnTC Goal 4);
 Identify and evaluate relationships between two variables using simple linear regression (MnTC Goal 4);
 Discuss concepts pertaining to linear regression, and use regression equations to make predictions;
 Analyze and interpret ANOVA test results; and
 Identify and analyze multinomial experiment data.
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
 Analyze and interpret ANOVA test results
 Identify and analyze multinomial experiment data
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: 
35 
Other (specify test): 
Elementary Algebra 
Score: 
76

6. Prerequisite Courses:
MATH 1460  Introduction to Statistics
Applies to all requirements
Accuplacer College Level Math score of 50 or higher, or Math 0810 Math Pathways, or Math 0820 Intermediate Algebra, or MATH 1520 Intro to College Algebra
7. Other Prerequisites
Math ACT of 20
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:
Bemidji State University, MATH 2610 Applied Statistics, 4 credits
Normandale Community College, MATh 1080 Introduction to Statistics, 4 credits
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 
Develop descriptive and inferential deductions based on raw data. 
Utilize appropriate technology 
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:
 Define and apply the meaning of descriptive statistics and statistical inference, describe the importance of statistics, and interpret examples of statistics in a professional context (MnTC Goal 4);
 Distinguish between a population and a sample (MnTC Goal 4);
 Calculate and explain the purpose of measures of locations and variability (MnTC Goal 4);
 Apply simple principles of probability (MnTC Goal 4);
 Compute probabilities related to both discrete and continuous random variables (MnTC Goal 4);
 Identify and analyze sampling distributions for statistical inferences (MnTC Goal 4);
 Identify and analyze confidence intervals for means and proportions (MnTC Goal 4);
 Compare and analyze data sets using descriptive statistics, parameter estimation, and hypothesis testing (MnTC Goal 4);
 Explain how the central limit theorem applies in inference, and use the theorem to construct confidence intervals (MnTC Goal 4);
 Calculate and interpret confidence intervals for one population average and one population proportion (MnTC Goal 4);
 Differentiate between type I and type II errors (MnTC Goal 4);
 Conduct and interpret hypothesis tests (MnTC Goal 4);
 Identify and evaluate relationships between two variables using simple linear regression (MnTC Goal 4);
 Discuss concepts pertaining to linear regression, and use regression equations to make predictions;
 Analyze and interpret ANOVA test results; and
 Identify and analyze multinomial experiment data.
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
 Analyze and interpret ANOVA test results
 Identify and analyze multinomial experiment data