Test
Regression Analysis Test - Machine Learning
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Preguntas del test (22)
1All explanatory variables are significant at 90% confidence level
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2The most important variable in explaining the target is Years of education
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3Living in the South decreases the average wage
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4Living in a Metropolitan Statistical Area increases the average wage
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5A stepwise method was used to produce the above output
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6If we enter a new variable most likely our R Square will increase
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7If sample size were 20 observations, we still may rely on the results above
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8R Square is the proportion of variance of the target variable explained by the predictors
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9Individual significance of variables in Linear Regression is checked with t-student test
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10Autocorrelation is produced if we have high correlation between explanatory variables
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11Main uses of Linear Regression are simulation, structural analysis and prediction
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12A telco company wants to predict a flag field named churn. Linear Regression is the appropriate technique to use
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13Heteroscedasticity is having unequal variances of explanatory variables
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14If Beta(x2) = 5 * Beta (x1), then x2 is five times more important than x1 in predicting the dependent
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15
Average wage “penalty” for Afro-American (AA) workers is on average approx. 18%
In this regression, the LOGARITHM of the yearly wage of workers (log of WAGE) is explained by the AGE of worker in years, father’s education in years and being Afro-American (AA) or not (1=yes / 0= no). (***) Be aware that COMMA “,” is used as decimal separator
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16An additional year of worker father’s education yields an average wage increase of 0.027% on average
In this regression, the LOGARITHM of the yearly wage of workers (log of WAGE) is explained by the AGE of worker in years, father’s education in years and being Afro-American (AA) or not (1=yes / 0= no). (***) Be aware that COMMA “,” is used as decimal separator- AVerdadero
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17We can REJECT the null hypothesis of individual significance for all variables with a 99% confidence
In this regression, the LOGARITHM of the yearly wage of workers (log of WAGE) is explained by the AGE of worker in years, father’s education in years and being Afro-American (AA) or not (1=yes / 0= no). (***) Be aware that COMMA “,” is used as decimal separator- AVerdadero
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18In this regression, the LOGARITHM of the yearly wage of workers (log of WAGE) is explained by the AGE of worker in years, father’s education in years and being Afro-American (AA) or not (1=yes / 0= no). (***) Be aware that COMMA “,” is used as decimal separator.
Elasticity between Wage and Age is approximately 0.027.- AVerdadero
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19Comparing coefficients size, AA is clearly the most influential variable
In this regression, the LOGARITHM of the yearly wage of workers (log of WAGE) is explained by the AGE of worker in years, father’s education in years and being Afro-American (AA) or not (1=yes / 0= no). (***) Be aware that COMMA “,” is used as decimal separator.- AVerdadero
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201 unit increase in Age yields an average 2.7% increase in Wage
In this regression, the LOGARITHM of the yearly wage of workers (log of WAGE) is explained by the AGE of worker in years, father’s education in years and being Afro-American (AA) or not (1=yes / 0= no). (***) Be aware that COMMA “,” is used as decimal separator.- AVerdadero
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21Confidence interval for AA variable includes the value -0.1
In this regression, the LOGARITHM of the yearly wage of workers (log of WAGE) is explained by the AGE of worker in years, father’s education in years and being Afro-American (AA) or not (1=yes / 0= no). (***) Be aware that COMMA “,” is used as decimal separator.- AVerdadero
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22Father’s education Beta real value in population might be 20
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Información
- Descripción
- Informática Test sobre Regression Analysis Test - Machine Learning, creado por Angel Intriago el 12/12/2021.
- Autor
- Angel Intriago
- Idioma
- English
- Visitas
- 3
- Etiquetas
- Valoración
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