# Project #35301 - Statics II

Learner Course ID: 358116

### Activity 4

Section 2: Assumptions and Common Statistical Strategies ï¿½ Correlation, Regression, and Comparing Means

This section begins with exploring assumptions and why they are important (and what to do if your data do not meet required assumptions). Prior to conducting statistical tests, you must examine your dataset to ensure that it does not violate the assumptions upon which the intended tests are based. Using the procedures outlined in Section 1, you may already have a good idea about your dataset with regards to the necessary assumptions. However, in this section we will formalize the evaluation of these assumptions. In your dissertation, it will be expected that you both understand and acknowledge assumptions, and that you are able to make modifications in your proposed analytical strategy, as necessary.

Once a firm understanding of assumptions related to statistical tests is gleaned, we jump into actually performing and interpreting common statistical tests; now the fun really begins!

The tests covered in this section include:
Correlation: Are two variables related; If so, how? A correlation tells you how and to what extent two variables are linearly related. A correlation coefficient will always fall between -1 and +1 with 0 indicative of no relationship between the variables. Rule of thumb effect sizes are as follows: Small (+.1), Medium (+.3) and Large (+.5), although these effect sizes should always be evaluated relative to research. An important point to remember: correlation does not equal causation!

Regression: A regression analysis is very similar to a correlation, but is the framework commonly used when one wants to predict one variable from another. For example: How much variance in happiness scores are predicted by hours of physical activity performed each week? With the simple regression framework, you have one predictor variable and one outcome variable; the outcome variable is measured on a continuous scale (soon you will learn how multiple regression can handle multiple predictor variables simultaneously).

Logistic Regression: A logistic regression is the framework one would use for prediction when the outcome variable is categorical. For example: Do numbers of hours spent in voluntary corporate training during the first year of employment predict whether an employee is still at the company in two years (yes/no)?

Comparing Means and ANOVA: While many questions can be answered by correlation and regression, frequent questions require the comparison of mean scores. For example: Are standardized test scores higher in a school that uses one reading method compared to another? Do men or women reap a greater benefit, in terms of pounds lost, from a certain exercise program? Questions that compare two groups can be answered with a simple t-test. An Analysis of Variance (ANOVA) can handle designs that compare more than two groups, like: Does Drug A, B, or C result in better life expectancies for people diagnosed with cancer? Or does Diet A, B, C, or D result in healthier cholesterol levels?

A lot of information is covered in these chapters, so please plan accordingly. Also, pay attention to how these techniques are fundamentally similar ï¿½ it seems like a ton of information, but if you master the statistical models at this level, the rest of the course will progress more smoothly.

It will be expected that you have gained an understanding of all analyses presented in the text. That is, should you require the use of an analytical strategy covered in the text but not performed in the Activity for your dissertation, you will have the core competencies to perform these alternative techniques.

Assignment 4   t test and ANOVA
Assignment Outcomes
Develop appropriate null and alternative hypotheses given a research question.
Calculate and interpret descriptive statistical analysis.
Create and interpret visual displays of data.
Apply appropriate statistical tests based on level of measurement.
Determine the appropriate use of inferential statistical analysis.
Demonstrate proficiency in the use of SPSS.
Demonstrate proficiency in reporting statistical output in APA format.

### Course Work

Course: BTM8107-8 Syllabus: 31479
 Northcentral University10000 University Drive, Prescott Valley, AZ 86314For more information call 888-327-2877.From outside the U.S. and Canada, call 928-541-7777.FAX 928-541-7817Copyright ï¿½ 2014 All rights reserved. Accredited by the Higher Learning Commissionand a member of the North Central Association.Privacy Statement

 Subject Business Due By (Pacific Time) 07/13/2014 12:00 am
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