Kocaeli University / European Credit Transfer System (ECTS)

Name of Lecturer(s)

Prof. Dr. Kasım BAYNAL
Associate Prof. Dr. Aysen ŞİMŞEK KANDEMİR

Learning Outcomes of the Course Unit
Program Competencies-Learning Outcomes Relation

Learning Outcomes of the Course Unit

1) Explaning the sampling the main engineering problems and identifing samples
2) Solving the engineering problems with statistical applications.
3) Interpreting the solved problems with statistical applications
4) Conducting the statistical analysis of single and multi-factorial experiments
5) Establishing the hypotheses related to the event and making factors' analysis of variance
6) Carrying out the Time Series Analysis (the nature of time series, the affecting factors of time series, the methods which used in calculation of trend).

Program Competencies-Learning Outcomes Relation

Program Competencies

Learning Outcomes

Low

No relation

Mode of Delivery

Face to Face

Prerequisites and Co-Requisites

None

Recommended Optional Programme Components

Not Required

Course Contents

This course covers statistical analysis and its importance, sampling theory and its applications, statistical forecast methods, hypothesis tests and industrial applications, regression - variance - covariance analysis, analysis of time series and introduction to multi variate statistics .

Weekly Schedule

1) Short of theoretical knowledge about the theory of sampling, probabilistic sampling (probabilistic nature of sampling, sampling methods (methods that increase the forecast is hit and, to facilitate the selection and monitoring methods)
2) Simple random sampling (sample selection, estimation errors, estimates the degree of accuracy), Point Forecast (Point Forecasts properties, Point Estimation methods)
3) Sampling distributions (distribution of sample averages, the distribution of sample proportions,the distribution of the differences between the sample means, sample distribution of the differences between the rates and practices), Central Limit Theorem, Distribution of mass taken a large sample statistics, sampling distributions of the main unknown.
4) Short Theoretical knowledge about the theory of the forecast, the point estimate and confidence Intervals, confidence Intervals for means, confidence Intervals for Proportions, confidence Intervals for Standard Deviations, the differences between confidence Intervals for Averages, confidence Intervals for Differences between Proportions, and applied to studies, Student's t-table
5) Statistical Decision (the nature of statistical Decision, hypotheses, testing hypotheses, and steps)
6) Applied Hypothesis tests (tests of averages and proportions, the small sample tests, tests for variances, standard deviations of the masses of the main distribution uncertain hypothesis testing, chi-square test, chi-square table, the F-Table
7) Time Series Analysis (the nature of time series, the affecting factors of time series, the methods which used in calculation of trend)
8) Midterm examination/Assessment
9) Measurement of seasonal fluctuations (adjusted for price changes, the methods which used in seasonal fluctuations)
10) Linear and non-linear regression and correlation analysis
11) Multiple Regression and Correlation Analysis, ANOVA and F-test
12) Indexes (location index, time indexes, the Basic Indexes, the composite indexes)
13) Presentation software package SPSS
14) The application of the SPSS software package for statistical problems
15) The application of the SPSS software package for statistical problems
16) Final examination

Planned Learning Activities and Teaching Methods

Assessment Methods and Criteria

Contribution of Midterm Examination to Course Grade	30%
Contribution of Final Examination to Course Grade	70%
Total	100%

Language of Instruction

Turkish

Work Placement(s)