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STAT 380: Statistics for Applications
Course Description: Descriptive statistics; basic probability rules and distributions; inferences for
means, variance and proportions; regression analysis. Prerequisite: MATH 152 with a grade of C or
better
Textbooks: (1) Statistical Methods for Engineers by G. Geoffrey Vining, Brooks/Cole Pub., 3rd Ed.
(2) Probability and Statistics for Engineers and Scientists 9th Ed, By Walpole, Myers, Ye, Prentice Hall
Course Outline and Topics∗
(1) 2. Data Displays (2 class periods)
2.2 Stem-and-Leaf Displays
2.3 Boxplots
2.4 Using Computer Software
(2) 2. Probability (2 class periods)
2.1 Sample Space
2.2 Events
2.4 Probability of an Event
2.5 Additive Rules
2.6 Conditional Probability
2.7 Multiplicative Rules
(2) 3. Random Variables and Probability (2 class periods)
3.1 Concept of a Random Variable
3.2 Discrete Probability Distributions
3.3 Continuous Probability Distributions
(2) 4. Mathematical Expectation (1 class period)
4.1 Mean of a Random Variable
4.2 Variance and Covariance of Random Variables∗∗
(2) 5. Some Discrete Probability Distributions (2 class periods)
5.3 Binomial and Multinomial Distributions∗∗∗
5.5 Negative Binomial and Geometric Distributions
5.6 Poisson Distribution and the Poisson Process
(2) 6. Some Continuous Probability Distributions (5 class periods)
6.1 Continuous Uniform Distribution
6.2 Normal Distribution
6.3 Areas under the Normal Curve
6.4 Applications of the Normal Distributions
6.5 Normal Approximation to the Binomial
6.6 Gamma and Exponential Distributions
6.7 Applications of the Exponential and Gamma Distributions
6.8 Chi-Squared Distribution
(2) 8. Fundamental Sampling and Data Descriptions (3 class periods)
8.2 Some Important Statistics
8.4 Sampling Distributions
8.5 Sampling Distribution of Means
8.6 Sampling Distribution of S2
8.7 t-Distribution
(1) 4. Estimation and Testing (8 class periods)
4.1 Estimation
4.2 Hypothesis Testing
4.3 Inference for a Single Mean
4.4 Inference for Proportions
4.5 Inference for Two Independent Samples
4.6 Paired t-Test
4.7 Inference for a Single Variance
4.8 p-Values
(1) 6. Linear Regression Analysis (3 class periods)
6.1 Relationship Among Data
6.2 Simple Linear Regression
6.3 Multiple Linear Regression
6.4 Residual Analysis
∗The suggested class period per chapter assumes two 75-minute class periods per week (total of 30
class periods per semester) with two exams per semester.
∗∗Only expected to discuss variance and standard deviation of a random variable.
***Expected to do just the binomial distribution
Course objectives (added by Department consent, Fall 2015):
After the completion of Stat 380, students will be able to
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understand basic concepts in probability including random experiments, sample spaces and
events, mutual exclusivity, conditional probability, independence, and Bayes theorem.
solve problems in counting and probability using techniques including permutations and
combinations.
understand the motivation for using probability models to describe the behavior of real-life
processes.
understand the concept of random variables, probability mass functions and densities, and
cumulative distributions.
understand the concept of expectation and be able to apply it in decision making.
understand summary measures such as the mean and variance of a random variable.
know families of discrete and continuous probability models and how they are used in practice.
understand the significance of the connection between probability and statistics and how it
relates in applications.
understand the role of randomness and sampling distributions in statistical applications.
understand and perform basic statistical inference such as confidence intervals, hypothesis
testing, regression, and analysis of variance.
organize and represent data, recognize and describe relationships, and perform basic statistical
inference using a statistical software such as Minitab, R, etc.