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. Courses in the Honors Program emphasize independent inquiry, informed discourse, and direct application within small, transformative, and seminar-style 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 Pre-Calculus College Level and Calculus College Level	Score:
Other (specify test):	ACT	Score:	20

6. Prerequisite Courses:

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. Co-requisite Courses:

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. College-Wide Outcomes

College-Wide 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, z-scores, 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 goodness-of-fit 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

1. Introduction to Statistics
• Use terminology
• Classify Data
• Determine proper data collection and experimental design
2. Summarizing and Graphing Data
• Frequency Distributions and Their Graphs
• Central Tendency
• Variation / Standard Deviation
• Measure of position
• Complete Project on Central Tendency
3. Probability
• Counting Methods
• Conditional Probability / Multiplication Rule
• Addition Rule
4. Discrete Probability Distributions
• Probability Distributions
• Binomial Distributions
• Using Technology
5. Normal Probability Distributions
• Standard Normal Distributions
• Find probabilities based on normal distribution
• Use the Central Limit Theorem
6. 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
7. 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 p-value
• Complete hypothesis project.
8. 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 p-value
9. Correlation & Linear Regression
• Determine r-values
• Perform hypothesis testing involving correlation
10. Goodness-of-fit and Contingency Tables
• Enter matrices
• Use Chi-Square comparison
11. Analysis of Variance
• Use F-Distribution