Skagit Valley College

Catalog Course Search Details

This course has been changed from the previous catalog, the changed field(s) are highlighted in red:

 Course Title:   Introduction to Stats

 Title Abbreviation:   INTRODUCTION TO STATS

 Department:    MATH&

 Course #:    146

 Credits:    5

 Variable:     No

 IUs:    5

 CIP:    270501

 EPC:    n/a

 REV:    2021

 Course Description  

This course is an introduction to probability and statistics using statistical inference as its theme. Topics include sampling techniques, probability and probability distributions, inferential methods including confidence intervals and hypothesis tests, regression and correlation. Designed to serve students of all interests requiring an introductory statistics course, including social science, business, and nursing majors. Statistical technology required.


Prerequisite: A grade of “C” or better in Math 096 and concurrent enrollment in Math 046; placement into Math 097 or Math 098 and concurrent enrollment in Math 046; placement into Math& 146.

Additional Course Details

Contact Hours (based on 11 week quarter)

Lecture: 55

Lab: 0

Other: 0

Systems: 0

Clinical: 0

Intent: Distribution Requirement(s) Status:  

Academic Natural Sciences, Quantitative  

Equivalencies At Other Institutions

Other Institution Equivalencies Table
Institution Course # Remarks
EWU 121
OTHER Meets GUR at 3 BIS
U of W STAT 220
WWU 240

Learning Outcomes

After completing this course, the student will be able to:

  1. Distinguish between categorical and quantitative data.
  2. Use correct vocabulary to describe surveys, experiments, and observational studies.
  3. Construct appropriate graphical displays of categorical and quantitative data.
  4. Compute summary statistics for quantitative data.
  5. Perform computations using the normal distribution.
  6. Perform computations using probability distributions including the binomial, normal, and t-distribution. May also include the uniform, Poisson, and chi-square distributions.
  7. Perform calculations using the Central Limit Theorem.
  8. Construct confidence intervals from one- and two-variable data and use them to make inferences about parameters.
  9. Make inferences about population parameters using hypothesis tests, from one- and two-variable data.
  10. Construct and interpret a linear regression model on bivariate data.
  11. Determine if linear correlation is statistically significant using the linear regression test.

General Education Learning Values & Outcomes

Revised August 2018 and affects outlines for 2019 and later.


Definition: Apply mathematical skills quantitatively, logically, creatively, and critically.

Course Contents

  1. Introduction to Statistics
    • Data
    • Design of Experiments
  2. Summarizing and Graphing Data
    • Frequency Distributions
    • Histograms
    • Statistical Graphs
  3. Describing Data
    • Measures of Center
    • Measures of Dispersion
    • Measures of Relative Standing
  4. Probability
    • Definitions and interpretation of basic probabilities
    • Addition Rule and Multiplication Rule
    • Bayes’ Theorem (optional)
    • Counting (optional)
  5. Discrete Probabilities
    • Basics of Probability Distributions
    • Binomial Distribution
    • Poisson Distribution (optional)
  6. Normal Probability Distributions
    • Standard Normal Distribution
    • Applications of Normal Distributions
    • Sampling Distributions
    • Assessing Normality
  7. Estimates, Confidence Intervals, and Sample Sizes
    • Estimating Population Proportion
    • Estimating Population Mean
    • Estimating Population Variance and Standard Deviation (optional)
    • Intervals about Two Proportions
    • Intervals about Two Means
    • Intervals about Two Standard Deviations (optional)
  8. Hypothesis Testing
    • Fundamentals of Hypothesis Testing
    • Testing a Claim about a Proportion
    • Testing a Claim About a Mean
    • Testing a Claim about Standard Deviation or Variance (optional)
    • Testing a Claim about Two proportions
    • Testing a Claim about Two Means (dependent and independent samples)
  9. Linear Correlation and Regression
    • Linear correlation
    • Linear regression
    • Prediction Intervals (optional)