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Biến tính có thể được sử dụng như các biến giải thích trong các mô hình hồi quy. Một trường hợp điển hình là khi một số bộ dữ liệu tương tự ngoại trừ việc mỗi thiết lập được đo bởi một nhà hóa học khác nhau (hoặc công cụ khác nhau hoặc phòng thí nghiệm), hoặc thiết lập từng xuất phát từ một vị trí khác nhau, hoặc thiết lập từng được đo vào một ngày khác nhau. Các biến chất lượng - nhà hóa học, địa điểm, hoặc ngày thường trên các giá trị rời rạc (ví dụ,. | 40 Regression Analysis with Categorical Variables KEY WORDS acid rain pH categorical variable F test indicator variable east squares linear model regression dummy variable qualitative variables regression sum of squares t-ratio weak acidity. Qualitative variables can be used as explanatory variables in regression models. A typical case would be when several sets of data are similar except that each set was measured by a different chemist or different instrument or laboratory or each set comes from a different location or each set was measured on a different day. The qualitative variables chemist location or day typically take on discrete values i.e. chemist Smith or chemist Jones . For convenience they are usually represented numerically by a combination of zeros and ones to signify an observation s membership in a category hence the name categorical variables. One task in the analysis of such data is to determine whether the same model structure and parameter values hold for each data set. One way to do this would be to fit the proposed model to each individual data set and then try to assess the similarities and differences in the goodness of fit. Another way would be to fit the proposed model to all the data as though they were one data set instead of several assuming that each data set has the same pattern and then to look for inadequacies in the fitted model. Neither of these approaches is as attractive as using categorical variables to create a collective data set that can be fitted to a single model while retaining the distinction between the individual data sets. This technique allows the model structure and the model parameters to be evaluated using statistical methods like those discussed in the previous chapter. Case Study Acidification of a Stream During Storms Cosby Creek in the southern Appalachian Mountains was monitored during three storms to study how pH and other measures of acidification were affected by the rainfall in that region. Samples were
