Statistical Methods I

Revised: November 2006

Course Description

Descriptive statistics, probability and sampling distributions, correlation, regression, estimation, hypothesis testing. Prerequisite: Math 170 or Math 146. (Three semester hours)

Objectives

   1. Acquaint students with the concepts of population and sample, and some of the basic descriptive measures used to describe them.

   2. Acquaint students with graphical methods for data presentation.

   3. Introduce concepts of probability, random variables, and distributions.

   4. Examine properties of common distributions, especially the normal and binomial.

   5. Introduce the ideal of correlation and regression.

   6. Provide an introduction to estimation and hypothesis testing by applying these procedures to population means and properties.

Text

Jay DeVore & Roxy Peck. Statistics the Exploration and Analysis of Data, Fourth Edition. W. H. Freeman, 2001.

Grading Procedure

Grading procedures and factors influencing course grade are left to the discretion of individual instructors, subject to general university policy.

Attendance Policy

Attendance policy is left to the discretion of individual instructors, subject to general university policy.

Course Outline

• Chapter 1: The Role Of Statistics. (2 class days)
        Three Reasons to Study Statistics. Statistics and Data Analysis. The Nature and Role of Variability
• Chapter 2: The Data Analysis Process And Collecting Data Sensibly. (4 days)
        Types of Data. The Data Analysis Process. Collecting Data Sensibly: Observation and Experiment. Sampling. Simple Comparative Experiments.
• Chapter 3: Graphical Methods For Describing Data. (5 days)
        Displaying Categorical Data: Frequency Distributions, Bar Charts and Pie Charts. Displaying Numerical Data: Dotplots and Stem-and-Leaf Displays. Displaying Numerical Data: Frequency Distributions and Histograms. Interpreting the Results of Statistical Analyses.
• Chapter 4: Numerical Methods For Describing Data. (5 days)
        Describing the Center of a Data Set. Describing Variability in a Data Set. Summarizing a Data Set: Boxplots. Interpreting Center and Spread: Chebyshev's Rule, The Empirical Rule, and z-Scores. Interpreting the Results of Statistical Analyses.
• Chapter 5: Summarizing Bivariate Data. (5 days)
        Scatter Plots. Correlation. Fitting a Line to Bivariate Data. Assessing the Fit of a Line. Nonlinear Relationships and Transformations. Interpreting the Results of Statistical Analyses. First steps, design of experiments, sampling design, toward statistical inference.
• Chapter 6: Probability. (4 days)
        Interpreting Probabilities and Basic Probability Rules. Probability as a Basis for Making Decisions. Estimating Probabilities.
• Chapter 7: Population Distributions. (7 days)
        Describing the Distribution of Values in a Population. Population Models for Continuous Numerical Variables. Normal Distributions. Checking for Normality and Normalizing Transformations.
• Chapter 8: Sampling Variability And Sampling Distributions. (4 days)
        Statistics and Sampling Variability. The Sampling Distribution of a Sample Mean. The Sampling Distribution of a Sample Proportion.
• Chapter 9: Estimation Using A Single Sample. (4 days)
        Point Estimation. A Large Sample Confidence Interval for a Population Proportion. A Confidence Interval for a Population Mean. Interpreting the Results of Statistical Analyses.
• Chapter 10: Hypothesis Testing Using A Single Sample. (5 days)
        Hypotheses and Test Procedures. Errors in Hypothesis Testing. Large-Sample Hypothesis Tests for a Population Proportion. Hypothesis Tests for a Population Mean. Power and Probability of Type II Error (Optional). Interpreting the Results of Statistical Analyses.

* Note:

         1. At appropriate places in this course, time should be allotted to elaborate on the historical aspects relevant to the subject.

         2. Most instructors for this course require the use of statistical calculators.