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 indepth 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.
Courses in the Honors Program emphasize independent inquiry, informed discourse, and direct
application within small, transformative, and seminarstyle classes that embrace detailed
examinations of the material and feature close working relationships with instructors. In addition, students learn to leverage course materials so that they can affect the world around them in positive ways.
5. Placement Tests Required:
Accuplacer (specify test): 
Writing Honors College Level and Math Introductory College Level or Algebra College Level and PreCalculus College Level and Calculus College Level 
Score: 

Other (specify test): 
ACT 
Score: 
20

6. Prerequisite Courses:
MATH 1461  Honors Introduction to Statistics
There are no prerequisites for this course.
7. Other Prerequisites
NG Accuplacer Reading 250 AND one of the following: NG Accuplacer QAS 265, NG Accuplacer AAF 236, MCA Math 1148, MATH 0810 Math Pathways, MATH 0820 Intermediate Algebra, MATH 0860 Pathway to Statistics
9. Corequisite 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:
Bemidji State University, MATH 2610 Applied Statistics, 4 credits
Normandale Community College, MATH 1080 Introduction to Statistics, 4 credits
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. CollegeWide Outcomes
CollegeWide Outcomes/Competencies 
Students will be able to: 
Demonstrate oral communication skills 
Produce an oral presentation, effectively demonstrating the use of statistical concepts. 
Analyze and follow a sequence of operations 
Give a detailed demonstration of how each stage of a study is developed and the data analyzed. 
Apply abstract ideas to concrete situations 
Take raw data and develop descriptive and inferential deductions based on that 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:
 Compute measures of center, zscores, quartiles, and percentile ranks from data and give interpretations of these numerical measures. MnTC Goal 4
 Construct graphical representations of data and estimate common numerical measures from them. MnTC Goals 2 & 4
 Calculate basic probabilities. MnTC Goal 4
 Use binomial distributions to determine characteristics of data. MnTC Goal 4
 Apply normal approximation to estimate projected outcomes and percentiles for data that is normally distributed. MnTC Goal 4
 Compute and interpret confidence intervals and sample sizes for means, proportions, and variances. MnTC Goals 2 & 4
 Perform hypothesis testing for claims about proportions, means, and variations and interpret the results of these tests. MnTC Goal 4
 Develop inferences about two proportions, two means and dependent samples. MnTC Goal 4
 Compute and interpret the correlation coefficient as a measure of the strength of the linear association between two numeric values. MnTC Goal 4
 Apply regression methods to estimate dependent variable values/interpret slope and constant in regression equations. MnTC Goal 4
 Conduct goodnessoffit test to determine correlations. MnTC Goal 4
 Perform analysis of variance. MnTC Goals 2 & 4
V. Topical Outline
Listed below are major areas of content typically covered in this course.
1. Lecture Sessions
 Introduction to Statistics
 Use terminology
 Classify Data
 Determine proper data collection and experimental design
 Summarizing and Graphing Data
 Frequency Distributions and Their Graphs
 Central Tendency
 Variation / Standard Deviation
 Measure of position
 Complete Project on Central Tendency
 Probability
 Counting Methods
 Conditional Probability / Multiplication Rule
 Addition Rule
 Discrete Probability Distributions
 Probability Distributions
 Binomial Distributions
 Using Technology
 Normal Probability Distributions
 Standard Normal Distributions
 Find probabilities based on normal distribution
 Use the Central Limit Theorem
 Confidence Intervals
 Find confidence intervals for mean of large samples
 Find confidence intervals for mean of small intervals
 Find confidence intervals for proportions
 Find confidence intervals for variance & std deviation
 Hypothesis Testing with one sample
 Hypothesis test for the Mean
 Hypothesis test for the Proportion
 Hypothesis test for Variance & Standard Deviation
 Use technology to determine test statistics and pvalue
 Complete hypothesis project.
 Inferences from two samples
 Hypothesis test for the differences of Means
 Hypothesis test for the differences of Proportions
 Hypothesis test for differences in Variance & Standard Deviation
 Use technology to determine test statistics and pvalue
 Correlation & Linear Regression
 Determine rvalues
 Perform hypothesis testing involving correlation
 Goodnessoffit and Contingency Tables
 Enter matrices
 Use ChiSquare comparison
 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 indepth 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.
Courses in the Honors Program emphasize independent inquiry, informed discourse, and direct
application within small, transformative, and seminarstyle classes that embrace detailed
examinations of the material and feature close working relationships with instructors. In addition, students learn to leverage course materials so that they can affect the world around them in positive ways.
5. Placement Tests Required:
Accuplacer (specify test): 
Writing Honors College Level and Math Introductory College Level or Algebra College Level and PreCalculus College Level and Calculus College Level 
Score: 

Other (specify test): 
ACT 
Score: 
20

6. Prerequisite Courses:
MATH 1461  Honors Introduction to Statistics
There are no prerequisites for this course.
7. Other Prerequisites
NG Accuplacer Reading 250 AND one of the following: NG Accuplacer QAS 265, NG Accuplacer AAF 236, MCA Math 1148, MATH 0810 Math Pathways, MATH 0820 Intermediate Algebra, MATH 0860 Pathway to Statistics
9. Corequisite 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:
Bemidji State University, MATH 2610 Applied Statistics, 4 credits
Normandale Community College, MATH 1080 Introduction to Statistics, 4 credits
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. CollegeWide Outcomes
CollegeWide Outcomes/Competencies 
Students will be able to: 
Demonstrate oral communication skills 
Produce an oral presentation, effectively demonstrating the use of statistical concepts. 
Analyze and follow a sequence of operations 
Give a detailed demonstration of how each stage of a study is developed and the data analyzed. 
Apply abstract ideas to concrete situations 
Take raw data and develop descriptive and inferential deductions based on that 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:
 Compute measures of center, zscores, quartiles, and percentile ranks from data and give interpretations of these numerical measures. MnTC Goal 4
 Construct graphical representations of data and estimate common numerical measures from them. MnTC Goals 2 & 4
 Calculate basic probabilities. MnTC Goal 4
 Use binomial distributions to determine characteristics of data. MnTC Goal 4
 Apply normal approximation to estimate projected outcomes and percentiles for data that is normally distributed. MnTC Goal 4
 Compute and interpret confidence intervals and sample sizes for means, proportions, and variances. MnTC Goals 2 & 4
 Perform hypothesis testing for claims about proportions, means, and variations and interpret the results of these tests. MnTC Goal 4
 Develop inferences about two proportions, two means and dependent samples. MnTC Goal 4
 Compute and interpret the correlation coefficient as a measure of the strength of the linear association between two numeric values. MnTC Goal 4
 Apply regression methods to estimate dependent variable values/interpret slope and constant in regression equations. MnTC Goal 4
 Conduct goodnessoffit test to determine correlations. MnTC Goal 4
 Perform analysis of variance. MnTC Goals 2 & 4
V. Topical Outline
Listed below are major areas of content typically covered in this course.
1. Lecture Sessions
 Introduction to Statistics
 Use terminology
 Classify Data
 Determine proper data collection and experimental design
 Summarizing and Graphing Data
 Frequency Distributions and Their Graphs
 Central Tendency
 Variation / Standard Deviation
 Measure of position
 Complete Project on Central Tendency
 Probability
 Counting Methods
 Conditional Probability / Multiplication Rule
 Addition Rule
 Discrete Probability Distributions
 Probability Distributions
 Binomial Distributions
 Using Technology
 Normal Probability Distributions
 Standard Normal Distributions
 Find probabilities based on normal distribution
 Use the Central Limit Theorem
 Confidence Intervals
 Find confidence intervals for mean of large samples
 Find confidence intervals for mean of small intervals
 Find confidence intervals for proportions
 Find confidence intervals for variance & std deviation
 Hypothesis Testing with one sample
 Hypothesis test for the Mean
 Hypothesis test for the Proportion
 Hypothesis test for Variance & Standard Deviation
 Use technology to determine test statistics and pvalue
 Complete hypothesis project.
 Inferences from two samples
 Hypothesis test for the differences of Means
 Hypothesis test for the differences of Proportions
 Hypothesis test for differences in Variance & Standard Deviation
 Use technology to determine test statistics and pvalue
 Correlation & Linear Regression
 Determine rvalues
 Perform hypothesis testing involving correlation
 Goodnessoffit and Contingency Tables
 Enter matrices
 Use ChiSquare comparison
 Analysis of Variance