Course Code: 5610303
METU Credit (Theoretical-Laboratory hours/week): 3(3-0)
ECTS Credit: 5.0
Language of Instruction: English
Level of Study: Undergraduate
Course Coordinator: Assoc.Prof.Dr. DİLEK KESKİN
Offered Semester: Fall and Spring Semesters.
Prerequisite: Set 1: 2360155
Set 2: 2360157
Set 3: 2360119
Set 4: 2360151
Set 5: 3570119
One of the sets above should be completed before taking ES303 STATISTICAL METHODS FOR ENGINEERS .

Course Objective

This course is mainly designed to give undergraduates in engineering relevance and practical significance of statistical concepts in their fields through essential mathematical principles and applications.

Course Content

Descriptive statistics, histograms, central tendency, dispersion and correlation measures. Basic probability concepts, random variables, probability density and mass function. Hypothesis testing, confidence intervals. Law of large numbers and central limit theorem. Regression analysis. Applications in engineering.

Learning Outcomes

1.to understand the basic concepts of Probability and statisticals and comprehend its importance in different disciplines of Engineering. 2.To be able to organize data and use the tools of descriptive statistics like histograms, frequency diagrams, etc, to show the distribution and skewness of data. 3. To be able to differentiate the different measures of central location and variability for a data with excel applications 4. To understand basic probability concepts like possibilities and probability, axioms of probability, conditional probability, statistical independence, theorem of total probability and Bayes theorem 5. To comprehend the concept of random variables and distributions, 6. To find the probability mass density and cumulative distribution functions descriptors of a random variable. 7. To understand and compare some useful distributions, uniform, binomial, negative binomial, Poisson, geometric, hypergeometric, normal, lognormal and exponential. 8. To analyze the joint mass density and cumulative distributions, marginal densities of multivariate distributions. 9. To know the concept of Independence, covariance, correlation, conditional mean and variance for joint distributions 10.To solve the problems related to functions of random variables, sum and difference of normal variates, mean and variance of a general function 11. To describe Statistical inferences, estimation of parameters, properties of estimators, 12. To understand the necessity of central limit theorem 13. To make interval estimation for the mean, 14. To comprehend how to use hypothesis testing for the mean and testing validity of assumed distribution,, 15. To learn alternative nonparametric methods: Wilcoxon signed Rank Test 16. To develop least squares estimation, lack of fit for regression and correlation analyses of data 17. To learn residual analysis and residual plots) 18. To dseing One way Analysis of Variance (ANOVA) for data