The following R code fits a logistic regression model to pre…

The following R code fits a logistic regression model to predict the probability of credit default using credit balance, income, and student status. model |z|) (Intercept) -10.8690    0.4923  -22.0801   0.0000studentYes   -0.6468    0.2363   -2.7376   0.0062balance       0.0057    0.0002   24.7376   0.0000income        0.0000    0.0000    0.3698   0.7115 (a) Based on the output, is student status a statistically significant predictor of default? (3 points)   (b) Interpret the effect of being a student (studentYes) on the probability of default, relative to non-students. (5 points)

A large, national grocery retailer tracks productivity and c…

A large, national grocery retailer tracks productivity and costs of its facilities closely. Data were obtained from a single distribution center for a one-year period. Each data point for each variable represents one week of activity. The variables included are the number of cases shipped (X1), the indirect costs of the total labor hours as a percentage (X2), a qualitative regressor called holiday that is coded 1 if the week has a holiday and 0 otherwise (X3), and the total labor hours (Y). Use the R output below to answer the following questions.  Interpret the coefficient value of 623.55 for the binary variable X3 in the context of predicting the response variable. Provide your answer in a complete sentence.  

How, in World War II (unlike World War I), did Germany build…

How, in World War II (unlike World War I), did Germany build up territorial dominance (whether through annexation, occupation, or creating puppet states) over Europe so quickly? How did the Soviet Union figure into the war? Which countries in Europe suffered the most casualties, and why? In what way were Nazi death camps both secret and not-so-secret during the war? How did widespread awareness of their existence from 1944 onward change perceptions and memories of the war? (100 words)