#### Prerequisites & Bulletin Description

#### Course Objectives

The main purpose of the course is to introduce the student to the nature and
scope of statistical methods. The increasing use of statistical techniques in
almost all fields of human endeavor has led to a proliferation of techniques of
varying complexity. Math 124 is not meant to be a cram course in such
methods. The main problems of statistical inference are introduced and several
important techniques are discussed with the emphasis at all times on the
underlying concepts. The student must be led to appreciate the fact that the
validity of any statistical analysis rests heavily on the realism of the underlying
probability models and on the proper experimental design.

The students will learn how to perform basic sampling and review experimental
designs. They will be able to calculate descriptive statistics and present data in
various forms such as tables and graphs. They will be able to analyze data
using inferential techniques. They will learn to use probability to discuss
everyday events such as playing Lotto and other games and use probability to
bridge the gap between descriptive statistics and inferential statistics.

#### Evaluation of Students

InstructorsÂ’ assessment is usually based on homework, quizzes, computer assignments, exams, and a final.

#### Course Outline

- Introduction: Some examples where statistics is applicable; Need for mathematical models
- Descriptive Statistics: Frequency tables, Histograms, Bar graphs, Pie charts, Stem-and-Leaf plots, Time plots, Boxplots, and their interpretation; Measures of central tendency (Mean, Median, Mode); Measures of dispersion (Range, Percentiles, Interquartile range, Variance, Standard Deviation) (2 weeks)
- Correlation and Linear Regression; Scatter plot, Correlation Coefficient; Least-Square Linear Regression, Coefficient of determination (2 weeks)
- Probability Theory: Random Variables, General Probability Rules, Conditional Probability, Independent Events, BayesÂ’s Theorem (2 weeks)
- Probability and Sampling Distributions: Binomial distribution, Normal distribution, Approximation of Binomial Distribution with normal distribution, Law of Large Numbers, Central Limit Theorem (2 weeks)
- Sampling & Designing of Experiments: Simple Random Samples, Use of random number tables, Probability Samples, Stratified Random Samples, Multistage Samples; Comparative Experiments, Randomized Comparative Experiments; Completely Randomized Designs, Block Designs (1week)
- Estimation: Point Estimation, Margin of Error, Confidence Interval Estimation for means and proportions, for One-Sample and Two-Sample problems (3 weeks)
- Test of Significance: Null and Alternative Hypotheses, Type I and Type II Errors, Significance Level, p-value, Hypothesis testing for means and proportions, for One-Sample and Two-Sample problems (2 weeks)
- Chi-Square Tests: Contingency Tables; Test of independence; Goodness of fit test; Test of Homogeneity (1 week)

#### Textbooks & Software

- Michael Sullivan, III,
*Statistics Informed Decisions Using Data*. - David S. Moore, Freeman,
*The Basic Practice of Statistics*. - Microsoft Excel

Submitted by: M.R. Kafai

Date: June 11, 2003