Overview

Stat 240 is a one-semester, 4 credit course. This is the introductory course in statistics.It covers descriptive statistics, basics of probability, random variables, binomial and normal distributions, Central Limit Theorem, the sampling distribution of the population mean and proportion, estimation, hypothesis testing, analysis of categorical data, simple linear regression. (Gen.Ed. R2 and R1 requirement upon successful completion).

Objective

When students complete this course, they should have a working understanding of the methods and skills used to organize data, conduct meaningful analysis, and draw inferences from the data. Students should become critical consumers of statistically based results reported in popular media, recognizing whether reported results reasonably follow from the study and analysis conducted. This is the absolute prerequisite if you want to take any more advanced courses in Statistics.

Prerequisites

Knowledge of high school algebra(knowledge of addition, subtraction, multiplication, division, exponents, square roots, summations, and factorials) and willingness to create a schedule to learn the material even though you have other obligations

Course Delivery

This course will be delivered entirely online through the course management system Blackboard.The Blackboard website is where all course content and announcements will be.

 Homework assignments and quizzes will be assigned and completed through WileyPlus. WileyPlus is an online homework and course study system. It also contains an e-book of our textbook.

 It is very important that you are aware of this grading policy for this course before you register.

 

Syllabus

Topics:

  1. What is statistics, types of statistics, population and sample, basic terms, types of variables (Chapter 1)
  2. Organizing and graphing qualitative and quantitative Data (Chapter2).
  3. Numerical descriptive measures, Empirical Rule, box plot (Chapter 3)
  4. Bivariate data: analysis of two categorical or two numerical data. Test for independence, correlation,regression line,estimation and prediction (notes + part of Chapter 13)
  5. Probability: Event, sample space, event composition, calculating probabilities, counting rules,mutually exclusive (or disjoint) events, conditional probabilities, Bayes rule, independent events, tree diagram(Chapter4).
  6. Discrete random variables and their probability distributions: random variables, mean and standard deviation of a discrete random variable, factorials, combinations and permutations, binomial distribution (Chapter5).
  7. Normal distributions: Normal probability distribution, use of standard normal distribution table, applications of the normal distribution (Chapter6).
  8. Sampling distributions of the sample mean and proportion, Central Limit Theorem (Chapter 7).
  9. Estimation of the mean and proportion – confidence intervals (Chapter 8) 
  10. Hypothesis tests about the mean and proportion (Chapter9)

  11.  Estimation and hypothesis testing – two populations (Chapter 10) (if time allows)