3 Credit Course
Offered in Lecture Format
Prerequisite required  (MATH 1430)

SYLLABUS

I. GENERAL CONCEPTS

A. Population and Samples
        1. Target population versus sampled population
        2. Census versus sampling
        3. Population parameters and sampling statistics
        4. Types of samples
B. Random variables
        1. Discrete and continuous variables
        2. Numeric and categorical variables
C. Measuring Scales
        1. Nominal
        2. Ordinal
        3. Interval
        4. Ratio

II. PRESENTATION OF DATA

A. Stem and leaf plot
B. Single and many value classes
C. Frequency distributions and histograms
D. Relative, cumulative and relative cumulative frequency tables
E. Shape, center dispersion, skewness kurtosis and outliers by observation only

III. CONDENSATION OF DATA

A. Measures of central tendency
        1. Mean (population)
        2. Median
        3. Mode
        4. Advantages and disadvantages of each
B. Measures of dispersion
        1. Range
        2. Standard deviation (population)
C. Distribution of values in a data set
        1. General rule (68%, 95%, 99⁺%)
        2. Standardized (z) values
        3. Identification of outliers using numerical criteria
D. Grouped data
        1. Measures of center
        2. Measures or dispersion
        3. Percentiles

STATISTICAL PACKAGE: Use of a statistical package should be integrated with the development of topics presented in I - III. In particular, the following topics should be covered:

A. Creating a file
B. Defining and editing the structure
C. Entering and editing the data
D. Adding and deleting variables and cases
E. Sorting and grouping data
F. Transformation of data
G. Statistical procedures
        1. Statistics
        2. Statistics on subgroups
        3. Stem and leaf plots
        4. Histograms
        5. Frequency distributions

IV. INFERENTIAL STATISTICS

A. General concepts of probability
        1. Relative frequency approach
      2. Histograms
B. Normal distributions
        1. Equivalency of area, proportion and probability
        2. Standard normal
        3. Transformation of ~ N ( ) to z~ N( 0,1 )
        4. Proportions, probabilities and percentiles
        5. Distribution of sample mean ~ N
      6. Standard error of the estimate
C. Estimation
        1. Point estimates(and s)
        2. Confidence interval estimates of
D. Hypothesis testing
        1. Mean of one population
        2. Means of two populations
                a. t test
                b. Grouping - independent samples
                c. Pairing - dependent samples
        3. Test for independence of classifications
                 a. distribution
        4. Regression analysis
                a. Simple linear regression
                b. Multiple linear regression

STATISTICAL PACKAGE: Topics presented in IV, C and D, should be illustrated and analyzed using statistical procedures in a statistical package. Analysis and interpretation of outputs should be emphasized.

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