ข้อมูลสำหรับสอบกลางภาคปลายปีการศึกษา 2557-58

1. SPSS format
    https://drive.google.com/file/d/0B18QOrxCLeTVSVM3VG9yZnYzYjQ/view?usp=sharing

2. Excel format
    https://drive.google.com/file/d/0B18QOrxCLeTVX0o2S0JGVGtreTA/view?usp=sharing

Power Point ปรับปรุงและเพิ่มเติม ภาคปลายปีการศึกษา 2557

6-Mahalanobis Distance.ppt
   https://drive.google.com/file/d/0B18QOrxCLeTVQUtvM1JyZmN0NVE/view?usp=sharing

7-MLR-1 Journal example and how to test assumption
   https://drive.google.com/file/d/0B18QOrxCLeTVM2xnRktfR1BrQjg/view?usp=sharing

8-Introduction to Principal Component Analysis การวิเคราะห์องค์ประกอบหลัก (PCA)
   https://drive.google.com/file/d/0B18QOrxCLeTVcWc2TjlWd1dtUzA/view?usp=sharing

17-Calibration and validation-2
   https://drive.google.com/file/d/0B18QOrxCLeTVVDBtUHBQdmJRQk0/view?usp=sharing

ข้อมูลสำหรับทำแบบฝึกหัด (Data for practices)

1. Data for outlier detection
    1.1 Example (GSS2000.sav)
          https://drive.google.com/file/d/0B18QOrxCLeTVeU9hTUhOQy1EMzg/view?usp=sharing
    1.2 Practice: Car sale
          https://drive.google.com/file/d/0B18QOrxCLeTVcGVlTUpPYWRHMms/view?usp=sharing

2. Data for regression analysis
    2.1 Hypothesis tests (iqsize)
          https://drive.google.com/file/d/0B18QOrxCLeTVLUJnV1ozZEczZ1k/view?usp=sharing
    2.2 Training and validation/Model building (oxygen.txt)
          https://drive.google.com/file/d/0B18QOrxCLeTVVWRXa0F2YlJiNWc/view?usp=sharing
    2.3 Stepwise regression (cement.sav/cement.txt)
          1. cement.sav
          https://drive.google.com/file/d/0B18QOrxCLeTVUkVROTlSN1FRdEU/view?usp=sharing
          2. cement.txt
          https://drive.google.com/file/d/0B18QOrxCLeTVNnVLXzlkcHRSWFE/view?usp=sharing
          3. bloodpress.txt
          https://drive.google.com/file/d/0B18QOrxCLeTVNFNBc2tMSWd0azA/view?usp=sharing
3. Multicollinearity
    3.1 Correlated data (bloodpress.txt)
          https://drive.google.com/file/d/0B18QOrxCLeTVNFNBc2tMSWd0azA/view?usp=sharing
    3.2 Uncorrelated data (Uncorelated.txt)
          https://drive.google.com/file/d/0B18QOrxCLeTVazFsS1lIblc4cVk/view?usp=sharing
    3.3 Effect of correlated predictor variables (allentest.txt)
          https://drive.google.com/file/d/0B18QOrxCLeTVNjNWMEtVM1hlX0k/view?usp=sharing
    3.4 Variance Inflation Factor (cement.txt)
          https://drive.google.com/file/d/0B18QOrxCLeTVNnVLXzlkcHRSWFE/view?usp=sharing
    3.5 Exercise (usair.txt)
          https://drive.google.com/file/d/0B18QOrxCLeTValZQOXVBWnNRa28/view?usp=sharing
4. Principal Component Analysis (PCA)
    4.1 Use of PCA for indirect classification of iris flowers (iris practice for DA.sav)
          https://drive.google.com/file/d/0B18QOrxCLeTVMVBaLUVKLW40Wjg/view?usp=sharing
    4.2 Multiple linear regression based on PCA to solve multicollinearity (cement.sav)
          https://drive.google.com/file/d/0B18QOrxCLeTVNnVLXzlkcHRSWFE/view?usp=sharing
5. Factor analysis (FA) (oxygen.sav)
          https://drive.google.com/file/d/0B18QOrxCLeTVeFRuSFBhZDh4QW8/view?usp=sharing
6. Partial Least Squares Regression (PLSR)
          http://aendt.blogspot.com/2014/08/unscrambler.html
7. Cluster Analysis (CA)
          https://drive.google.com/file/d/0B18QOrxCLeTVYnhFZkdjM1RTMjQ/view?usp=sharing