Consider a model with an interaction term between being female and being married. The dependent variable is the log of the hourly wage: log(wage) = 0.151 -0.038 female + 0.1 married – 0.301 female* married +0.079 educ + 0.027 exper +0.029 tenure (0.072) (0.056) (0.055) (0.007) (0.005) (0.007) n =536, R2 =0.461 Numbers in parantheses are standard errors of coefficients. Given the estimation result and the observation number fill in the blanks below which aim at discussing the statistical significance of variables. The test statistic of the interaction term is The critical value at 1% significance level is Then the interaction term statistically significant at 1% significance level. (Hint: to fill the blank make a choice between "is" and "is not".)

Consider a model with an interaction term between being female and being married. The dependent variable is the log of the hourly wage: log(wage) = 0.151 -0.038 female + 0.1 married – 0.301 female* married +0.079 educ + 0.027 exper +0.029 tenure (0.072) (0.056) (0.055) (0.007) (0.005) (0.007) n =536, R2 =0.461 Numbers in parantheses are standard errors of coefficients. Given the estimation result and the observation number fill in the blanks below which aim at discussing the statistical significance of variables. The test statistic of the interaction term is The critical value at 1% significance level is Then the interaction term statistically significant at 1% significance level. (Hint: to fill the blank make a choice between "is" and "is not".)

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Description: Consider a model with an interaction term between being female and being married. The dependent variable is the log of thehourly wage:log(wage) = 0.151 - 0.038 female + 0.1 married - 0.301 female* married + 0.079 educ + 0.027 exper+0.029 tenure(0.07

MATLAB: An Introduction with Applications

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Author:Amos Gilat

Publisher:Amos Gilat

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Types of Bias

Statistics is the study of data collection, organization, analysis, interpretation, and presentation. Statistical bias is a characteristic of a statistical technique or its findings in which the expected value deviates from the actual root quantitative parameter being estimated. According to the actual definition of bias, it refers to the tendency of a statistic to overestimate or underestimate a parameter.

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Consider a model with an interaction term between being female and being married. The dependent variable is the log of the
hourly wage:
log(wage) = 0.151 - 0.038 female + 0.1 married - 0.301 female* married + 0.079 educ + 0.027 exper+0.029 tenure
(0.072)
(0.056)
(0.055)
(0.007)
(0.005)
(0.007)
n = 536, R2 = 0.461
Numbers in parantheses are standard errors of coefficients. Given the estimation result and the observation number fill in the
blanks below which aim at discussing the statistical significance of variables.
The test statistic of the interaction term is
The critical value at 1% significance level is
Then the interaction term
statistically significant at 1% significance level. (Hint: to fill the blank
make a choice between "is" and "is not".)

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