Ders Bilgileri

#### Ders Tanımı

Ders Kodu Yarıyıl T+U Saat Kredi AKTS
COMPUTER APPLICATIONS IN STATISTICS ENM 446 8 3 + 0 3 5
 Dersin Dili Türkçe Dersin Seviyesi Lisans Dersin Türü SECMELI Dersin Koordinatörü Dr.Öğr.Üyesi NEVRA AKBİLEK Dersi Verenler Dersin Yardımcıları Res.Assit. E.Elçin GÜNAY Dersin Kategorisi Dersin Amacı Analyze data by statistical techniques and interpret the results of the study to assist managers. Dersin İçeriği
 Dersin Öğrenme Çıktıları Öğretim Yöntemleri Ölçme Yöntemleri 1 - Student will be able to classify raw data 1 - 2 - 6 - C - 2 - Student will be able to make the data ready to analyze 1 - 4 - 6 - C - 3 - Student will be able to run SPSS for analyzing the data 1 - 4 - 6 - A - C - 4 - Student will be able to decide on appropriate test procedure to analize data 3 - 4 - 1 - A - C - 5 - Student will be able to structure engineering decision-making problems as hypothesis tests 1 - 3 - 4 - A - C - 6 - Student will be able to test hypothesis 4 - 3 - 1 - C - A - 7 - Student will be able to analize data by SPSS 1 - 4 - 6 - A - C - 8 - Student will be able to visualize data and analyze graphics 6 - 4 - 1 - C - A - 9 - Student will be able to construct the results of the statistical tests 1 - 3 - 4 - A - C - D - 10 - Student will be able to report the results of the study 4 - 3 - 1 - A - C - D -
 Öğretim Yöntemleri: 1:Lecture 2:Question-Answer 6:Motivations to Show 4:Drilland Practice 3:Discussion Ölçme Yöntemleri: C:Homework A:Testing D:Project / Design

#### Ders Akışı

Hafta Konular ÖnHazırlık
1 Sampling Distribution
2 Hypothesis Tests
3 Nonparametric Tests
4 Nonparametric Tests
5 Chi-Square Independent Test
6 ANOVA
7 One way and Two way ANOVA
8 Regression and correlation Analysis
9 Assumptions of linear regression, simple linear regression models
10 Multiple regression models
11 Tests for regression
12 Cluster Analysis
13 Factor Analysis
14 Discriminant Analysis

Ders Notu
Ders Kaynakları

#### Dersin Program Çıktılarına Katkısı

No Program Öğrenme Çıktıları KatkıDüzeyi
1 2 3 4 5
1 Engineering graduates with sufficient knowledge background on science and engineering subjects of their related area, and who are skillful in implementing theoretical and practical knowledge for modelling and solving engineering problems. X
2 Engineering graduates with skills in identifying, describing, formulating and solving complex engineering problems, and thus,deciding and implementing appropriate methods for analyzing and modelling. X
3 Engineering graduates with skills in designing a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; for this purpose, skills in implementing modern design methods.
4 Engineering graduates with skills in developing, selecting and implementing modern techniques and tools required for engineering applications as well as with skills in using information technologies effectively. X
5 Engineering graduates with skills in designing and conducting experiments, collecting data, analyzing and interpreting the results in order to evaluate engineering problems. X
6 Engineering graduates who are able to work within a one discipline or multi-discipline team,as well as who are able to work individually X
7 Engineering graduates who are able to effectively communicate orally and officially in Turkish Language as well as who knows at least one foreign language X
8 Engineering graduates with motivation to life-long learning and having known significance of continuous education beyond undergraduate studies for science and technology X
9 Engineering graduates with well-structured responsibilities in profession and ethics X
10 Engineering graduates having knowledge about practices in professional life such as project management, risk management and change management, and who are aware of innovation and sustainable development. X
11 Engineering graduates having knowledge about universal and social effects of engineering applications on health, environment and safety, as well as having awareness for juridical consequences of engineering solutions.

#### Değerlendirme Sistemi

YARIYIL İÇİ ÇALIŞMALARI SIRA KATKI YÜZDESİ
AraSinav 1 50
KisaSinav 1 12
Odev 1 12
KisaSinav 2 13
Odev 2 13
Toplam 100
Yıliçinin Başarıya Oranı 50
Finalin Başarıya Oranı 50
Toplam 100

#### AKTS - İş Yükü

Etkinlik Sayısı Süresi(Saat) Toplam İş yükü(Saat)
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 1 16
Mid-terms 1 10 10
Quiz 2 6 12
Assignment 2 9 18
Final examination 1 10 10
Toplam İş Yükü 114
Toplam İş Yükü /25(s) 4.56
Dersin AKTS Kredisi 4.56
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