Dis4

part 1) 1 page

Below are 10 “sociopolitical” categories which may influence a transitioning leader’s early (and enduring) effectiveness. These include, in no particular order, the ability to:

• enter into an established leader/employee “network”;
• sense, as well as use, appropriate communication and influence behaviors;
• sense and appropriately act on keen organizational norms;
• become aware of, and sensitively interact with, organizational “blockers” and “enablers”;
• build political bridges by identifying and relating to key formal/informal power sources;
• become known as a “go-to/can-do” individual;
• be perceived as a “team player”;
• sense key organizational issues upon which to create early vision, initiatives, and value;
• identify and appropriately respond to the requirements of superiors/peers/subordinates; and
• be perceived as having organizationally appropriate ethics, values, and beliefs.

Does this list of “sociopolitical” skill categories match your sense of what is most required to help ensure a leader’s early success in a new role/position? What might you add or subtract to help ensure the most effective transition into a new job?

(part 2) 1 page

Writing for Publication

Organizations often require managers to write short articles for newsletter or blog publication. Becoming comfortable writing this style of commentary for a general audience is important in an era in which media can be authored by anyone.

The focus of your work will be communications ethics (which includes media, advertising, and marketing). Research a controversial scenario/event and create a  word article that discusses the real-world incident.

Today’s consumers of media want quick, informative, and exciting information.

(part 3) 1 page

• 1.Based on the text on regression assumptions below, and your additional research, discuss the potential impact of assumption violation on interpretation of regression results.
• 2.Is there any influence of the assumption violation on the business decision making? If so, how? If not, why?

Assumptions

No assumptions are necessary for computing the regression coefficients or for partitioning the sum of squares. However, there are several assumptions made when interpreting inferential statistics. Moderate violations of Assumptions 1-3 do not pose a serious problem for testing the significance of predictor variables. However, even small violations of these assumptions pose problems for confidence intervals on predictions for specific observations.

Residuals are normally distributed:

As in the case of simple linear regression, the residuals are the errors of prediction. Specifically, they are the differences between the actual scores on the criterion and the predicted scores. A Q-Q plot for the residuals for the example data is shown below. This plot reveals that the actual data values at the lower end of the distribution do not increase as much as would be expected for a normal distribution. It also reveals that the highest value in the data is higher than would be expected for the highest value in a sample of this size from a normal distribution. Nonetheless, the distribution does not deviate greatly from normality.

Homoscedasticity:
It is assumed that the variances of the errors of prediction are the same for all predicted values. As can be seen below, this assumption is violated in the example data because the errors of prediction are much larger for observations with low-to-medium predicted scores than for observations with high predicted scores. Clearly, a confidence interval on a low predicted UGPA would underestimate the uncertainty.

Linearity:

It is assumed that the relationship between each predictor variable and the criterion variable is linear. If this assumption is not met, then the predictions may systematically overestimate the actual values for one range of values on a predictor variable and underestimate them for another.