Data from the Hospital Data Set

 

 

Analyze the data from the Hospital Data Set and identify trends and patterns in the current operations. You will submit a written
current state analysis report. You will also create multiple charts to present the descriiptive analysis you performed. To organize this information, you will
create and submit a Data Analysis Workbook in Excel. For this milestone, you will add three sheets to the workbook, and you’ll add more as you complete
your Milestone Two assignment.
Specifically, you must address the following rubric criteria:
Descriiptive Statistics: Create an Excel file and title it Data Analysis Workbook. Within your workbook, create a sheet titled P1_Descriiptive Analysis. Present
descriiptive statistics (mean, median, standard deviation, and range) in a table for four attributes from the data set.
Analyze the current hospital data to identify trends and patterns in hospital admissions and costs.
Admission Trends and Charts: Create a sheet titled P1_Admission Trends in your Data Analysis Workbook. Then, create two pie charts and two column/bar
charts.
For Pie Chart #1: One slice should be labeled Admissions. Choose another attribute for the second slice. For Pie Chart #2, both slices can be attributes of
your choice.
For the two column/bar charts: Ensure that one column is titled Admissions for both charts. Choose a different attribute for the other column.
Expense Trends and Charts: Create a sheet titled P1_Expense Trends in your Data Analysis Workbook. Then, create two column charts and two line charts.
For the two column/bar charts: Ensure that one column is titled Expense for both charts. Choose a different attribute for the other column.
For Line Chart #1, one of the lines should represent Expense; choose another attribute for the second line. For Line Chart #2, both lines can represent
attributes of your choice.
Outliers: Identify any outliers that you see and explain how they have an impact on the overall admission and expense trends. Outliers are the data points that
can have an impact on your averages and basic descriiptive analysis.
Analysis: Form and explain your initial hypothesis based on your analysis of the charts and trends in Excel.
Which option (Option A or Option B) would you choose at this point in your analysis, given the expense range of $55–$75 million?
Why did you choose your selected attributes?
Describe any relationships or trends you observed while conducting your analysis.

 

Sample Solution

when predicting attitude stability and the corresponding behavior and judgments of those behaviors. Moreover, Gantman and Van Bavel (2014) found evidence for a moral pop-out effect, such that participants were more likely to recognize moral words over nonmoral words in a lexical decision task.

With regard to group evaluations, it has been shown that moral judgments of one’s ingroup are more important than judgments of competence or sociability (Leach, Ellemers, & Barreto, 2007). Perceiving one’s ingroup as moral has been shown to lead to more positive outcomes of a group’s self-concept, such that positive moral evaluations of one’s ingroup leads to less distancing from that group and greater group identification (Leach et al., 2007). This line of research further extends to the evaluation of outgroups, with the main finding that moral traits are weighted more heavily when members of one group form impressions about an outgroup (Brambilla et al., 2013a). A limitation of this line of research is its focus on conscious, controlled perceptions of morality. Unconscious perception enjoys an extensive influence on social behavior (e.g., Greenwald & Banaji, 1995), and as such studying morality at the unconscious level may reveal interesting differences in explicit versus implicit evaluations of outgroups.

While previous research has provided a solid foundation for understanding just how important moral judgments are to individuals, more work needs to be done to fully examine how quickly moral judgments are made. Limited work has studied the role of implicit cognition in moral judgments, though there is reason to believe that moral judgments may be susceptible to nonconscious influences (e.g., Ma, Vandekerckhove, Baetens, Van Overwalle, Seurinck, & Fias, 2012; Willis & Todorov, 2006). Given that judgments of morality are deemed to be more relevant than other traits when judging whether a target represents a threat (Brambilla et al., 2013b; Willis & Todorov, 2006), we contend that research into the implicit attribution of moral personality traits is warranted to delineate whether morality is attributed automatically or through cognitive processes. This led to our first hypothesis, which predicts that participants will be more likely to recognize moral (versus nonmoral) traits

Spontaneous Trait Inferences

A spontaneous trait inference (STI) occurs when an individual makes a nonconscious, unintentional judgment about the character of another individual (Winter & Uleman, 1984). These inferences occur

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