Curriculum Guideline

Introduction to Statistics

Effective Date:
Course
Discontinued
No
Course Code
MATH 1160
Descriptive
Introduction to Statistics
Department
Mathematics
Faculty
Science & Technology
Credits
3.00
Start Date
End Term
201810
PLAR
No
Semester Length
15 weeks
Max Class Size
35
Contact Hours
4 hours lecture and 1 hour tutorial
Method Of Instruction
Lecture
Tutorial
Methods Of Instruction

Lectures, group work, assignments.

Course Description
A pre-calculus introduction to descriptive statistics, measures of central tendency and variation, elementary probability, probability distributions, sampling, hypothesis testing, regression, correlation and chi-square testing.
Course Content

Introduction to Statistics

  • The nature of data, uses and abuses of statistics, design of experiments statistics with calculator and computers.

Describing exploring and comparing data

  • Summarizing data with frequency tables, pictures of data, measures of central tendency, measures of variation, measures of position, exploratory data analysis.

Probability

  • Definitions, addition rule, multiplication rule, probabilities through simulation, counting.

Probability Distributions

  • Random variables, binomial experiments, mean, variance and standard deviation for the Binomial distribution.

Normal Probability Distributions

  • The Standard Normal distribution, non-standard Normal distributions, the Central Limit Theorem, Normal approximation to the Binomial distribution.

Estimates and Sample Sizes

  • Estimating a population mean using large and small samples, estimating a population proportion.

Hypothesis Testing

  • Fundamentals of Hypothesis Testing, testing a claim about a mean using large and small samples, testing a claim about a proportion.
  • Confidence intervals.

Inferences from Two Samples

  • Inferences about two means: dependent samples, inferences about two means: independent and large samples, inferences about two means: independent and small samples, inferences about two proportions

Correlation and Regression

  • Correlation, regression variation
Learning Outcomes

At the end of the course, the successful student should be able to:

  •  Define the terms “population” and “sample” as they apply to Statistics
  • Define and differentiate between the nominal, ordinal, interval and ratio levels of measurement
  • Explain the proper use of Statistics within real world application and provide examples of its abuse
  • Have an understanding of experimental design and the use of random number tables and generators
  • Create and interpret frequency tables, histograms, cumulative frequency tables, stem and leaf displays and scatter plots
  • Calculate and interpret measures of central tendency and variation
  • Calculate and interpret standard scores
  • Understand the classical and relative frequency approaches to probability and employ counting techniques
  • Know and apply the addition and multiplication rules for probability and the concept of conditional probability
  • Be able to differentiate between discrete and continuous random variables
  • Determine whether the conditions for a Binomial experiment apply and compute the Binomial probabilities
  • Compute the mean, variance and standard deviation for the Binomial distribution
  • Determine probabilities of standard and non-standard normal random variables
  • Use the Normal distribution to approximate Binomial probabilities
  • Understand and apply the Student t distribution
  • Apply the Central Limit Theorem to estimate population parameters using large and small samples
  • Apply the Central Limit Theorem to estimate the difference between population parameters        
  • Perform hypothesis tests on population parameters or the difference between population parameters using large and small samples
  • Create confidence intervals for population parameters or their difference using large and small samples.
  • Create Contingency Tables and perform goodness-of-fit testing in multinomial experiments using the Chi-square test. (optional)
  • Understand and apply Chebychev’s theorem (optional)
  • Understand and apply the Poisson and other probability distributions (optional)
Means of Assessment

Evaluation will be carried out in accordance with Douglas College policy.  The instructor will present a written course outline with specific evaluation criteria at the beginning of the semester.  Evaluation will be based on some of the following:

Weekly quizzes 0-20%
Term tests 20-70%
Tutorials 0-10%
Participation/attendance 0-5%
Assignments 0-10%
Final exam 30-40%

Note:  Students may be required to pass the final exam in order to be eligible to pass the course.

Textbook Materials

Textbooks and Materials to be Purchased by Students

  • Moore, The Basic Practice of Statistics, current edition, Freeman
  • Calculator TI83+ or TI84 (optional)
Prerequisites

Math 1105; or B.C. Principles of Math 11 with a B or better; or B.C. Applications of Math 11 with an A- or better; or B.C. Principles of Math 12 with a C or better; or B.C. Applications of Math 12 with a B or better; or Precalculus 11 with a B or better; or Precalculus 12 with a C or better; or Foundations of Math 11 with a B or better; or Foundations of Math 12 with a C or better.