MATH 1127 Syllabus

Subject Code

MATH

Course Number

1127

Course Title

Introduction to Statistics

Prerequisites

Degree program admission math competency or successful completion of required math learning support courses with grades of C* or higher

Corequisites

Terms Offered

Credit Hours

Course Description

This course emphasizes the concepts and methods fundamental to utilizing and interpreting commonly used statistics. Topics include descriptive statistics, basic probability, discrete and continuous distributions, sampling distributions, hypothesis testing, chi square tests, and linear regression.

Course Outcomes

Learning

  • Define populations, samples, parameters, and statistics.
  • Classify data as quantitative (discrete/continuous) or qualitative.
  • Distinguish types of samples including random and stratified.
  • Distinguish between observational studies and experiments.

Descriptive Statistics and Graphs

  • Create/interpret stem-leaf diagrams, histograms, and box pilots.
  • Create relative and cumulative frequency distribution tables.
  • Compute mean, median, mode, and standard deviation.
  • Identify skew and symmetry; investigate the empirical rule.
  • Compare relative position using z-scores, percentiles, quartiles.

Basic Probability

  • Define events, compound events, and complementary events.
  • Compute probabilities for unions, intersections, and complements.
  • Compute conditional probabilities.

Discrete and Continuous Distributions

  • Demonstrate the difference between discrete and continuous random variables.
  • Use probability distributions to compute expected value of a discrete random variable.
  • Compute probabilities for binomial distributed random variables.
  • Compute expected value and variance of a binomial distributed random variable.
  • Compute probabilities for normally distributed random variables.
  • Use the normal distribution to estimate probabilities for binomial distributed random variables.

Sampling Distributions and Estimation

  • Use the Central Limit Theorem to compute probabilities for sample means.
  • Determine confidence intervals for population proportions.
  • Determine confidence intervals for population means (if unknown).
  • Determine the sample size required to obtain a desired margin of error.

Hypothesis Testing

  • Interpret and determine the null and alternative hypothesis for testing a claim.
  • Define Type I (I+) error, Type II (AY) error, and the power of a test.
  • Test a hypothesis about a population proportion.
  • Test a hypothesis about a population mean (if unknown).

Linear Regression

  • Use scatter plots to explore positive and negative association between two variable quantities.
  • Use the correlation coefficient to establish a linear relationship between two variables.
  • Calculate linear regression line that best represents the relationship between two variables.
  • Use linear regression to make appropriate predictions.

Chi Square Tests

  • Examine how well a data set fits a claimed distribution.
  • Test a contingency table for row independence/association.
  • Compare two or more population proportions and test for differences in populations.