Conducting an Anova and Multiple Linear Regression

Conducting an Anova and Multiple Linear Regression. Suppose that you, as the executive director of the organization, decide that you need to consider making changes to the job training program as well as the education program. You have the following things to consider:
•The education program director continues to push for the elimination of the expensive manufacturing training program. An analysis that found manufacturing program graduates were less likely to become employed supports that position, which has emboldened the education program director to argue the point more strenuously.

The job training director argues that the manufacturing training program still has some benefit and that closing it would disproportionately affect Latino participants and immigrants, since they are more likely to successfully complete the manufacturing program rather than the technology program. 
•The education program director argues that the job training director’s point about Latino participants only makes it more important to keep the ESL program, since the reason that Latino and immigrant participants are more likely to prefer manufacturing is that language barriers are easier to overcome in the manufacturing program.
So, you need to get a better sense of who is taking, and successfully completing, the manufacturing training program and the technology training program. The first thing you want to do is conduct some exploratory analyses. Given the concerns about how changes in the job training program might disproportionately impact some groups, you want data on the racial and ethnic, and English-speaking versus non-native English-speaking participants in the programs.

Conducting an Anova


Then, you want to conduct some outcome analyses:
•You want to conduct a multiple logistic regression to see if there is a difference between enrollment in manufacturing versus technology programs by racial or ethnic groups.
•You want to conduct a phi coefficient to see if there is a relationship between English-speaking participants (native versus non-native) and whether or not they choose the manufacturing or technology program.
•You want to conduct an independent samples t-test, looking at native English-speaking participants and non-native English-speaking participants and their final grades.


•You want to conduct an ANOVA, looking at race and final grades.
•You want to conduct a multiple linear regression to see if there are relationships between language (English or non-native English speakers), program type (manufacturing or technology), and final grades.
The results of all of these analyses are provided in the two multimedia images and the Outcome Analyses PDF document linked in the Resources for this assignment.
Write a paper analyzing these results. Your paper should consist of two sections with subsections, as follows:
•Describe each figure in its own paragraph.
•Identify the variables of each analysis. Is each variable categorical or continuous? What type of categorical or continuous variable (nominal, ordinal, interval, or ratio) is it? Which are the independent variables and which are the dependent variables?
•Describe the result of each analysis.

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