tailieunhanh - Exploratory Data Analysis_6

Tham khảo tài liệu 'exploratory data analysis_6', kỹ thuật - công nghệ, điện - điện tử phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | . Spectral Plot Random Data hW ENGINEERING STATISTICS HANDBOOK TOOLS raids I search back NEKT 1. Exploratory Data Analysis . EDA Techniques . Graphical Techniques Alphabetic . Spectral Plot . Spectral Plot Random Data Spectral Plot of 200 Normal Random Numbers Conclusions We can make the following conclusions from the above plot. 1. There are no dominant peaks. 2. There is no identifiable pattern in the spectrum. 3. The data are random. Discussion For random data the spectral plot should show no dominant peaks or distinct pattern in the spectrum. For the sample plot above there are no clearly dominant peaks and the peaks seem to fluctuate at random. This type of appearance of the spectral plot indicates that there are no significant cyclic patterns in the data. http div898 handbook eda section3 1 of 2 5 1 2006 9 57 07 AM . Spectral Plot Random Data SEMMECH MOME rTOOLS IM SEARCH BACK NEXT http div898 handbook eda section3 2 of 2 5 1 2006 9 57 07 AM . Spectral Plot Strong Autocorrelation and Autoregressive Model hW ENGINEERING STATISTICS HANDBOOK TOOLS raids I search back NEKT 1. Exploratory Data Analysis . EDA Techniques . Graphical Techniques Alphabetic . Spectral Plot . Spectral Plot Strong Autocorrelation and Autoregressive Model Spectral Plot for Random Walk Data 0 006 to 0 003 tn Conclusions We can make the following conclusions from the above plot. 1. Strong dominant peak near zero. 2. Peak decays rapidly towards zero. 3. An autoregressive model is an appropriate model. http div898 handbook eda section3 1 of 2 5 1 2006 9 57 07 AM