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:

7. Other Prerequisites
Math ACT of 20

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

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. College-Wide Outcomes

College-Wide 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

1. Introduction to statistics
• Define types of data
• Use critical thinking skills
• Describe methods of collecting data
2. Summarizing and graphing data
• Construct graphical representations of data and estimate common numerical measures from them
• Analyze misuse of graphs for data
3. Describe and compare data in statistical terms
• Compute measures of center, z-scores, variation, quartiles, and percentile ranks from data, and give interpretations of these numerical measures
• Develop boxplot representations and interpret distribution characteristics
4. Probability
• Calculate basic probabilities
• Addition rule
• Multiplication rule
• Counting
5. Discrete probability distributions
• Use binomial distributions to determine characteristics of data
6. Normal probability distributions
• Apply normal approximation to estimate projected outcomes and percentiles for data that is normally distributed
7. Estimates and sample sizes
• Compute and interpret confidence intervals and sample sizes for means, proportions, and variances
8. Hypothesis testing
• Perform hypothesis testing for claims about proportions, means, and variations, and interpret the results of these tests
9. Inferences from two samples
• Develop inferences about two proportions, two means, and dependent samples.
10. Correlation
• Compute and interpret the correlation coefficient as a measure of the strength of the linear association between two numeric values
11. Linear regression
• Apply regression methods to estimate dependent variable values
• Interpret slope and constant in regression equations
12. Goodness-of-fit and contingency tables
• Determine correlations conducting goodness-of-fit test
13. Analysis of variance
• Analyze and interpret ANOVA test results
• Identify and analyze multinomial experiment data