Life Data Classification

Statistical models rely extensively on data to make predictions. In our case, the models are the statistical distributions and the data are the life data or times-to-failure data of our product. The accuracy of any prediction is directly proportional to the quality, accuracy and completeness of the supplied data. Good data, along with the appropriate model choice, usually results in good predictions. Bad, or insufficient data, will almost always result in bad predictions.

In the analysis of life data, we want to use all available data sets, which sometimes are incomplete or include uncertainty as to when a failure occurred. Life data can therefore be separated into two types: complete data (all information is available) or censored data (some of the information is missing). Each type is explained next.

Statistical models rely extensively on data to make predictions. In our case, the models are the statistical distributions and the data are the life data or times-to-failure data of our product. The accuracy of any prediction is directly proportional to the quality, accuracy and completeness of the supplied data. Good data, along with the appropriate model choice, usually results in good predictions. Bad, or insufficient data, will almost always result in bad predictions.

In the analysis of life data, we want to use all available data sets, which sometimes are incomplete or include uncertainty as to when a failure occurred. Life data can therefore be separated into two types: complete data (all information is available) or censored data (some of the information is missing). Each type is explained next.

Censored Data

In many cases, all of the units in the sample may not have failed (i.e., the event of interest was not observed) or the exact times-to-failure of all the units are not known. This type of data is commonly called censored data. There are three types of possible censoring schemes, right censored (also called suspended data), interval censored and left censored.

Censored Data

In many cases, all of the units in the sample may not have failed (i.e., the event of interest was not observed) or the exact times-to-failure of all the units are not known. This type of data is commonly called censored data. There are three types of possible censoring schemes, right censored (also called suspended data), interval censored and left censored.

Statistical models rely extensively on data to make predictions. In our case, the models are the statistical distributions and the data are the life data or times-to-failure data of our product. The accuracy of any prediction is directly proportional to the quality, accuracy and completeness of the supplied data. Good data, along with the appropriate model choice, usually results in good predictions. Bad, or insufficient data, will almost always result in bad predictions.

In the analysis of life data, we want to use all available data sets, which sometimes are incomplete or include uncertainty as to when a failure occurred. Life data can therefore be separated into two types: complete data (all information is available) or censored data (some of the information is missing). Each type is explained next.

Censored Data

In many cases, all of the units in the sample may not have failed (i.e., the event of interest was not observed) or the exact times-to-failure of all the units are not known. This type of data is commonly called censored data. There are three types of possible censoring schemes, right censored (also called suspended data), interval censored and left censored.

Statistical models rely extensively on data to make predictions. In our case, the models are the statistical distributions and the data are the life data or times-to-failure data of our product. The accuracy of any prediction is directly proportional to the quality, accuracy and completeness of the supplied data. Good data, along with the appropriate model choice, usually results in good predictions. Bad, or insufficient data, will almost always result in bad predictions.

In the analysis of life data, we want to use all available data sets, which sometimes are incomplete or include uncertainty as to when a failure occurred. Life data can therefore be separated into two types: complete data (all information is available) or censored data (some of the information is missing). Each type is explained next.

Censored Data

In many cases, all of the units in the sample may not have failed (i.e., the event of interest was not observed) or the exact times-to-failure of all the units are not known. This type of data is commonly called censored data. There are three types of possible censoring schemes, right censored (also called suspended data), interval censored and left censored.

Statistical models rely extensively on data to make predictions. In our case, the models are the statistical distributions and the data are the life data or times-to-failure data of our product. The accuracy of any prediction is directly proportional to the quality, accuracy and completeness of the supplied data. Good data, along with the appropriate model choice, usually results in good predictions. Bad, or insufficient data, will almost always result in bad predictions.

In the analysis of life data, we want to use all available data sets, which sometimes are incomplete or include uncertainty as to when a failure occurred. Life data can therefore be separated into two types: complete data (all information is available) or censored data (some of the information is missing). Each type is explained next.

Censored Data

In many cases, all of the units in the sample may not have failed (i.e., the event of interest was not observed) or the exact times-to-failure of all the units are not known. This type of data is commonly called censored data. There are three types of possible censoring schemes, right censored (also called suspended data), interval censored and left censored.