2019-01-21T11:19:18+00:00 Assignments

Topic: Univariate vs. Bivariate Analyses and Regression (MHS506 Case)

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Topic: Univariate vs. Bivariate Analyses and Regression (MHS506 Case)

Instructions:

Module 4 - Case
UNIVARIATE VS. BIVARIATE ANALYSES AND REGRESSION
Case Assignment
Using the materials in the module homepage and in the background section, please address the following:
What is the difference between "univariate" and "multivariate" analyses? (1 page)
Define and contrast dependent versus independent variables. (1 page)
Describe the difference between logistical regression and linear regression. What types of variables are used for the dependent variable? (1 page)
Assignment Expectations
Length: Case Assignment should be at least 3 pages (750 words) in length.
References: At least two references from academic sources must be included (e.g., peer-reviewed journal articles). You may use any required readings from this module for your two references. Quoted material should not exceed 10% of the total paper (since the focus of these assignments is critical thinking). Use your own words and build on the ideas of others. When material is copied verbatim from external sources, it MUST be enclosed in quotes. The references should be cited within the text and also listed at the end of the assignment in the References section (APA formatting recommended).



Organization: Subheadings should be used to organize your paper according to question.



Format: APA formatting is recommended for this assignment. See Syllabus page for more information on APA formatting.



Grammar and Spelling: While no points are deducted, assignments are expected to adhere to standards guidelines of grammar, spelling, punctuation, and syntax. Points may be deducted if grammar and spelling impact clarity.



Your assignment will not be graded until you have submitted an Originality Report with a Similarity Index (SI) score <20% (excluding direct quotes, quoted assignment instructions, and references). Papers not meeting this requirement by the end of the session will receive a score of 0 (grade of F). Do keep in mind that papers with a lower SI score may be returned for revisions. For example, if one paragraph accounting for only 10% of a paper is cut and pasted, the paper could be returned for revision, despite the low SI score. Please use the report and your SI score as a guide to improve the originality of your work.



The following items will be assessed in particular:



Achievement of learning outcomes for Case Assignment.

Relevance: all content is connected to the question.

Precision: specific question is addressed; statements, facts, and statistics are specific and accurate.

Depth of discussion: points that lead to deeper issues are presented and integrated.

Breadth: multiple perspectives, references, and issues/factors are considered.

Evidence: points are well supported with facts, statistics, and references.

Logic: presented discussion makes sense; conclusions are logically supported by premises, statements, or factual information.

Clarity: writing is concise, understandable, and contains sufficient detail or examples.

Objectivity: use of first person and subjective bias are avoided.

Content:

CASE MOD 4 MHS506 univariate vs. Bivariate analyses and regression Name Institution Date Difference between "univariate" and "multivariate" analyses Under univariate analysis the number of variable are considered separately, but in multivariate analysis, the multiple regression techniques are used when there are more than two levels of measurement analyzing there or more simultaneously (Daniel, 2009). Since univariate analysis focuses on individual variables it is associated with the measures of central tendency. Univariate analysis extends from multivariate analysis, and both focus on how variables affect outcomes. Analyzing the statistically significant factors under multivariate analysis highlights that the approach gives better accuracy by considering multiple attributes. Under univariate analysis only one dependent variable is considered for analysis, compared to multiple dependent variables considered in multivariate analysis

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