MAT-121 Introductory Statistics I

This course is designed to introduce descriptive statistics of one and two variables, and probability; and to assimilate those concepts into an understanding of probability distributions. Topics include measures of central tendency, variability, graphical displays, linear correlation, and regression, dependent and independent probability, discrete and continuous probability distributions. The course will emphasize computer or calculator use to obtain results. This course carries SUNY General Education Mathematics (and Quantitative Reasoning) credit.

Credits

3

Lecture Contact Hours

3

Lab Contact Hours

0

Other Contact Hours

0

Semesters Course Will Be Offered

• Fall
• Spring
• Summer
• Winter

Course Learning Outcomes

1. Use the language of statistics to present, interpret, and critically analyze data.
2. Summarize univariate data visually through graphs and numerically through statistical measures.
3. Analyze bivariate data using linear correlation and regression.
4. Apply the concepts of probability and probability distributions to problem situations, with an emphasis on the binomial and normal distributions.

View Course Outline

MAT 121: Introductory Statistics I

Course Description

This course is designed to introduce descriptive statistics of one and two variables, and probability; and to assimilate those concepts into an understanding of probability distributions. Topics include measures of central tendency, variability, graphical displays, linear correlation, and regression, dependent and independent probability, discrete and continuous probability distributions. The course will emphasize computer or calculator use to obtain results. This course carries SUNY General Education Mathematics (and Quantitative Reasoning) credit.

Credit Hours

3

Contact Hours

Lecture	3
Lab	0
Other	0

Semester(s) Course Will Be Offered

Fall, Spring, Summer, Winter

SUNY General Education Course

• Mathematics and Quantitative Reasoning

Course Learning Outcomes

1. Use the language of statistics to present, interpret, and critically analyze data.
2. Summarize univariate data visually through graphs and numerically through statistical measures.
3. Analyze bivariate data using linear correlation and regression.
4. Apply the concepts of probability and probability distributions to problem situations, with an emphasis on the binomial and normal distributions.

Topic Outline

Note: The following is a list of topics. The order in which the material is covered need not follow the ordering below.

1. Fundamental Terminology and Concepts
   1. Population vs. Sample
   2. Parameter vs. Statistic
   3. Variable
   4. Data
      1. Quantitative vs. Qualitative
      2. Discrete vs. Continuous
      3. Nominal vs. Ordinal
   5. Appropriate Use and Misuse of Statistics
2. Sampling
   1. Methods
      1. Simple Random Sample
      2. Cluster
      3. Stratified
      4. Systematic
      5. Convenience
   2. Sampling Bias
3. Organization of Data
   1. Graphical
      1. Quantitative Data
      2. Qualitative Data
   2. Tabular
      1. Ungrouped vs. Grouped Frequency Distribution
      2. Relative Frequencies
      3. Cumulative Relative Frequencies
4. Shapes of Distributions of Quantitative Data
   1. Skewed vs. Symmetrical
   2. Bimodal
   3. Uniform
   4. Normal
5. Computing and Interpreting Measures of Central Tendency
   1. Mean
   2. Median
   3. Mode
   4. Midrange
6. Computing and Interpreting Measures of Position
   1. Quartiles
   2. Percentiles
   3. Standard score (z-score)
7. Computing and Interpreting Measures of Dispersion
   1. Range
   2. Variance
   3. Standard Deviation
   4. Inter-Quartile Range (IQR)
8. Applications of Measures of Center, Shape, and Spread
   1. The relationship between the shape of a graph and the appropriate measures of center and spread
   2. Outliers
   3. Chebyshev's Theorem
   4. Empirical Rule
9. Bivariate Data
   1. Scatterplot
   2. Types of Correlation
   3. Linear Correlation
      1. Correlation vs. Causation
      2. Linear Regression to Make Predictions
      3. Restrictions on Using Linear Regression
10. Probability
    1. The Concept of Probability
    2. Sample Space Representations
    3. Simple and Compound Events
    4. Types of Probability: Empirical, Theoretical, Subjective
    5. Mutually Exclusive
    6. Independent/Dependent events
    7. Introduction to General Addition and Multiplication Rules
    8. Introduction to Conditional Probability
11. Probability Distributions of a Random Variable
    1. Random Variable: Discrete and Continuous
    2. The Concept of a Probability Distribution
    3. Computing and Interpreting Mean and Standard Deviation for Probability Distribution
12. Binomial Probability Distribution
    1. Characteristics of a Binomial Experiment
    2. Computing Binomial Probabilities
    3. Computing and Interpreting the Mean
13. Normal Distribution
    1. Characteristics of a Normal Distribution
    2. Computing Normal Probabilities
    3. Applications of a Normal Distribution