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Course Description Course Name : Probability And Statistics II Course Code : ENM220 Course Type : Compulsory Level of Course : First Cycle Year of Study : 2 Course Semester : Spring (16 Weeks) ECTS : 5 Name of Lecturer(s) : Assoc.Prof.Dr. MAHMUDE REVAN ÖZKALE Learning Outcomes of the Course : Learn some of the continous distributions Understand the sampling distributions and their properties Do sampling distribution and point estimate Estimate confidence Test hypothesis Do chi-square tests and goodness of fit tests Have prior knowledge about regresssion and correlation analysis Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : To compass some of the continuous distributions, to do sampling distributions, to estimate interval, to test hypothesis, to apply chi-square tests and to have prior knowledge about regression and correlation Course Contents : Normal distribution, hypothesis testing, confidence intervals, goodness of fit test, chi-square tests Language of Instruction : Turkish Work Place : Industrial Engineering Classrooms   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Normal distribution, standart normal distribution Source reading Lecture, problem solving 2 Normal approximation to Binomial distribution, continuos uniform and Gamma distribution Source reading Lecture, problem solving 3 Sampling Distribution, Point estimation, Confidence interval on the mean of a normal distribution, variance known Source reading Lecture, problem solving 4 Chebyshev inequality and sample size, chi-square and F distribution Source reading Lecture, problem solving 5 Confidence interval on the mean of a normal distribution, variance unknown, Confidence interval on the variance Source reading Lecture, problem solving 6 Confidence interval on the difference in means of two normal distribution, Source reading Lecture, problem solving 7 Confidence interval on the ratio of variances of two normal distribution, Confidence interval on p, Confidence interval onthe difference of binomial parameters Source reading Lecture, problem solving 8 Midterm exam Review the topics discussed in the lecture notes and sources Written exam 9 Hypothesis test on the mean of a normal distribution Source reading Lecture, problem solving 10 Hypothesis test on the variance of a normal distribution Source reading Lecture, problem solving 11 Hypothesis test on the equivalence of variance of two normal distribution, Hypothesis test on the binomial parameter and the difference of two binomial parameters Source reading Lecture, problem solving 12 Goodness of fit tests Source reading Lecture, problem solving 13 Independence Tests Source reading Lecture, problem solving 14 Independence Tests Source reading Lecture, problem solving 15 Regression and correlation analysis Source reading Lecture, problem solving 16/17 Final exam Review the topics discussed in the lecture notes and sources Written Exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  1. Akdeniz,F. (2010). Olasılık ve İstatistik , Nobel Kitabevi Required Course Material(s)  1. Montgomery, D. C., Runger, G. C. (2002). Applied Statistics and Probability for Engineers. John Wiley and Sons   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 100     Homeworks/Projects/Others 0 0 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Can collect and analyze data required for industrial engineering problems ,develops and evaluates alternative solutions. 5 2 Has sufficient background on topics related to mathematics, physical sciences and industrial engineering. 5 3 Gains ability to use the acquired theoretical knowledge on basic sciences and industrial engineering for describing, formulating and solving an industrial engineering problem, and to choose appropriate analytical and modeling methods. 5 4 Gains ability to analyze a service and/or manufacturing system or a process and describes, formulates and solves its problems . 0 5 Gains ability to choose and apply methods and tools for industrial engineering applications. 0 6 Can access information and to search/use databases and other sources for information gathering. 2 7 Works efficiently and takes responsibility both individually and as a member of a multi-disciplinary team. 0 8 Appreciates life time learning; follows scientific and technological developments and renews himself/herself continuously. 0 9 Can use computer software in industrial engineering along with information and communication technologies. 0 10 Can use oral and written communication efficiently. 1 11 Has a conscious understanding of professional and ethical responsibilities. 1 12 Uses English skills to follow developments in industrial engineering and to communicate with people in his/her profession. 0 13 Has a necessary consciousness on issues related to job safety and health, legal aspects of environment and engineering practice. 0 14 Becomes competent on matters related to project management, entrepreneurship, innovation and has knowledge about current matters in industrial engineering. 0 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 4 56     Out of Class Study (Preliminary Work, Practice) 14 4 56 Assesment Related Works     Homeworks, Projects, Others 0 0 0     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 15 15 Total Workload: 137 Total Workload / 25 (h): 5.48 ECTS Credit: 5