DUMMY VARIABLEREGRESSION MODELS1. D

DUMMY VARIABLE
REGRESSION MODELS

1. Dummy variables, taking values of 1 and zero (or their linear transforms),
are a means of introducing qualitative regressors in regression
models.
2. Dummy variables are a data-classifying device in that they divide a
sample into various subgroups based on qualities or attributes (gender,
marital status, race, religion, etc. ) and implicitly allow one to run individual
regressions for each subgroup. If there are differences in the response of the
regressand to the variation in the qualitative variables in the various subgroups,
they will be reflected in the differences in the intercepts or slope
coefficients, or both, of the various subgroup regressions.
3. Although a versatile tool, the dummy variable technique needs to be
handled carefully. First, if the regression contains a constant term, the number
of dummy variables must be one less than the number of classifications
of each qualitative variable. Second, the coefficient attached to the dummy
variables must always be interpreted in relation to the base, or reference,
group—that is, the group that receives the value of zero. The base chosen
will depend on the purpose of research at hand. Finally, if a model has several
qualitative variables with several classes, introduction of dummy variables
can consume a large number of degrees of freedom. Therefore, one
should always weigh the number of dummy variables to be introduced
against the total number of observations available for analysis.
4. Among its various applications, this chapter considered but a few.
These included (1) comparing two (or more) regressions, (2) deseasonalizing
time series data, (3) interactive dummies, (4) interpretation of dummies
in semilog models, and (4) piecewise linear regression models.
5. We also sounded cautionary notes in the use of dummy variables in
situations of heteroscedasticity and autocorrelation. But since we will cover
these topics fully in subsequent chapters, we will revisit these topics then.

Hasil (Bahasa Indonesia) 1:

DUMMY VARIABELMODEL REGRESI1. dummy variabel, mengambil nilai-nilai 1 dan nol (atau transformasi linear mereka),sarana memperkenalkan kualitatif regressors di regresimodel.2. dummy variabel adalah perangkat mengklasifikasi data dalam bahwa mereka membagicontoh ke dalam berbagai sub-kelompok yang didasarkan pada kualitas atau atribut (gender,status perkawinan, ras, agama, dll.) dan secara implisit memungkinkan seseorang untuk menjalankan individuregresi untuk subgrup masing-masing. Jika ada perbedaan dalam responregressand untuk variasi dalam variabel kualitatif dalam berbagai sub-kelompok,mereka akan tercermin dalam perbedaan di penyadapan atau lerengKoefisien, atau keduanya, regresi subgrup berbagai.3. meskipun alat serbaguna, teknik variabel dummy perluditangani dengan hati-hati. Pertama, jika regresi berisi istilah yang konstan, nomordummy variabel harus menjadi salah satu kurang dari jumlah klasifikasiSetiap variabel yang kualitatif. Kedua, Koefisien melekat bonekavariabel harus selalu ditafsirkan sehubungan dengan dasar, atau referensi,Grup — yaitu kelompok yang menerima nilai nol. Tempat yang dipilihakan tergantung pada tujuan dari penelitian di tangan. Akhirnya, jika model memiliki beberapavariabel kualitatif dengan beberapa kelas, pengenalan dummy variabeldapat mengkonsumsi sejumlah besar derajat kebebasan. Oleh karena itu, salah satuselalu harus menimbang dummy variabel yang akan diperkenalkanagainst the total number of observations available for analysis.4. Among its various applications, this chapter considered but a few.These included (1) comparing two (or more) regressions, (2) deseasonalizingtime series data, (3) interactive dummies, (4) interpretation of dummiesin semilog models, and (4) piecewise linear regression models.5. We also sounded cautionary notes in the use of dummy variables insituations of heteroscedasticity and autocorrelation. But since we will coverthese topics fully in subsequent chapters, we will revisit these topics then.