Statistical Data Analysis (MAE832): Διαφορά μεταξύ των αναθεωρήσεων

Τελευταία αναθεώρηση της 15:25, 16 Αυγούστου 2024

Ελληνική Έκδοση
Undergraduate Courses Outlines
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Department of Mathematics
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General

School	School of Science
Academic Unit	Department of Mathematics
Level of Studies	Undergraduate
Course Code	ΜΑΕ832
Semester	8
Course Title	Statistical Data Analysis
Independent Teaching Activities	Lectures-Laboratory (Weekly Teaching Hours: 3, Credits: 6)
Course Type	Special background
Prerequisite Courses	-
Language of Instruction and Examinations	Greek
Is the Course Offered to Erasmus Students	Yes (in English, reading Course)
Course Website (URL)	See eCourse, the Learning Management System maintained by the University of Ioannina.

Learning Outcomes

Learning outcomes	In this course, various statistical methodologies are applied with the help of the computer and the use of statistical programs (SPSS, JASP, R). Emphasis is placed on choosing the appropriate statistical methodology and examining whether the assumptions of its application are met.
General Competences	Working independently Decision-making Production of free, creative and inductive thinking Criticism and self-criticism.

Syllabus

In this course, various statistical methodologies are applied with the help of the computer and the use of statistical programs (SPSS, JASP, R).

Upon completion of the course, the student will be able to:

enter data into the computer
conduct descriptive statistical analysis, that is to summarize the available data,
conduct basic data analyzes (testing hypotheses concerning the mean of a population, the means of two populations with dependent and independent samples, one way analysis of variance etc.,)
fits simple-multiple linear regression models, binomial logistic regression models, checking whether the assumptions of their application are violated
applies basic methodologies of multidimensional analysis (clustering, factor analysis)
present the results of the above analyzes (reference report).

Teaching and Learning Methods - Evaluation

Delivery

Classroom (face-to-face)

Use of Information and Communications Technology

Use of ICT in communication with students

Teaching Methods

Activity	Semester Workload
Lectures	39
Working independently	78
Exercises-Homeworks	33
Course total	150

Student Performance Evaluation

Final written exam in Greek (in case of Erasmus students in English) which concentrates on the solution of problems which are motivated by the main themes of the course.

Attached Bibliography

See the official Eudoxus site or the local repository of Eudoxus lists per academic year, which is maintained by the Department of Mathematics. Books and other resources, not provided by Eudoxus:

Andy Field. Η Διερεύνηση της Στατιστικής με τη χρήση του SPSS της IBM. Εκδόσεις Προπομπός
Julie Pallant. SPSS Οδηγός ανάλυσης δεδομένων με το ΙΒΜ SPSS. Εκδόσεις Κλειδάριθμος ΕΠΕ.
Δ. Φουσκάκης. Ανάλυση Δεδομένων με χρήση της R. Εκδόσεις Τσότρας Αν. Αθανάσιος
Joaquim P. Marques de Sá. Applied Statistics Using SPSS, STATISTICA, MATLAB and R. Springer Berlin Heidelberg Διαθέτης (Εκδότης) HEAL-Link Springer ebooks
Μαλεφάκη, Σ., Μπατσίδης, Α., & Οικονόμου, Π. (2023). Στατιστική Ανάλυση Δεδομένων [Προπτυχιακό εγχειρίδιο]. Κάλλιπος, Ανοικτές Ακαδημαϊκές Εκδόσεις.

@@ Γραμμή 1: / Γραμμή 1: @@
-[[Undergraduate Courses Outlines]] - [https://math.uoi.gr  Department of Mathematics]
+* [[Στατιστική Ανάλυση Δεδομένων (ΜΑΕ832)|Ελληνική Έκδοση]]
+{{Course-UnderGraduate-Top-EN}}
+{{Menu-OnAllPages-EN}}
 === General ===
@@ Γραμμή 48: / Γραμμή 50: @@
 |-
 ! Course Website (URL)
-| http://users.uoi.gr/abatsidis/832.html
+| See [https://ecourse.uoi.gr/ eCourse], the Learning Management System maintained by the University of Ioannina.
 |}
@@ Γραμμή 57: / Γραμμή 59: @@
 ! Learning outcomes
 |
+In this course, various statistical methodologies are applied with the help of the computer and the use of statistical programs (SPSS, JASP, R). Emphasis is placed on choosing the appropriate statistical methodology and examining whether the assumptions of its application are met.
-The aim of this course is the implementation of the statistical theory which was developed in "633-Statistical Inference" and "733-Regression and Analysis of Variance" in analyzing (statistical) data by using statistical packages (for instance JMP, SPSS, S-Plus). At the end of the course the student should be able to:
-# enter data on the computer
-# conduct descriptive statistical analysis that summarizes the available data
-# perform basic data analysis (testing for outliers and normality, basic hypothesis testing with dependent and independent samples, one way anova)
-# adjust linear models, mainly simple regression, controlling on whether the assumptions of the model are violated or not
-# present and interpret the results of the above analysis.
 |-
 ! General Competences
@@ Γραμμή 72: / Γραμμή 68: @@
 * Criticism and self-criticism.
 |}
 === Syllabus ===
-The implementation of the statistical theory which was developed in "633-Statistical Inference" and "733-Regression and Analysis of Variance" in analyzing data using statistical packages (for instance JMP, SPSS, S-Plus) is the main aim of the course. In particular, the following subjects are discussed: testing hypotheses, simple and multiple linear regression analysis, one way and two way Anova (with and without interaction). The course is laboratorial.
+In this course, various statistical methodologies are applied with the help of the computer and the use of statistical programs (SPSS, JASP, R).
+Upon completion of the course, the student will be able to:
+# enter data into the computer
+# conduct descriptive statistical analysis, that is to summarize the available data,
+# conduct basic data analyzes (testing hypotheses concerning the mean of a population, the means of two populations with dependent and independent samples, one way analysis of variance etc.,)
+# fits simple-multiple linear regression models, binomial logistic regression models, checking whether the assumptions of their application are violated
+# applies basic methodologies of multidimensional analysis (clustering, factor analysis)
+# present the results of the above analyzes (reference report).
 === Teaching and Learning Methods - Evaluation ===
 {| class="wikitable"
@@ Γραμμή 109: / Γραμμή 115: @@
 === Attached Bibliography ===
-See [https://service.eudoxus.gr/public/departments#20 Eudoxus]. Additionally:
+<!-- In order to edit the bibliography, visit the webpage -->
-* Carver and Nash (2006). Doing data analysis with SPSS version #
+<!-- https://wiki.math.uoi.gr/index.php/%CE%A0%CF%81%CF%8C%CF%84%CF%85%CF%80%CE%BF:MAE832-Biblio -->
-* Coakes and Steed (1999).SPSS: Analysis Without Anguish
-* Απόστολος Μπατσίδης (2014). Στατιστική Ανάλυση Δεδομένων με το S.P.S.S. (διαθέσιμες στην ιστοσελίδα του μαθήματος καθώς και διδακτικό υλικό).
+See the official [https://service.eudoxus.gr/public/departments#20 Eudoxus site] or the [https://cloud.math.uoi.gr/index.php/s/62t8WPCwEXJK7oL local repository] of Eudoxus lists per academic year, which is maintained by the Department of Mathematics. Books and other resources, not provided by Eudoxus:
+{{MAE832-Biblio}}