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Hi Barbara,
This is a good start. Could please provide us with an example from your area of research interest of a multiple regression question?

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Running Head: WEEK 6 DISCUSSION 1

WEEK 6 DISCUSSION 4

Week 6 Discussion: T-test, ANOVA and Regression Analysis

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Difference between T-test and ANOVA Verses Regression

T-test measures the significance of difference in means between two groups only. ANOVA measures the significant difference in means between more than two groups (Kim, 2017). However, regression measures the relationship between the independent variables and the dependent variables of a given study. T-test and ANOVA in general, examines the significance of the difference in means while regression determines whether a relationship exists between variables. If one is determining whether income of males and females differ significantly, or whether a treatment group and control group differ, then a researcher would use t – test. When the researcher wants to examine the difference in means for income between three or more groups say, 1) people with no prior work experience, 2) people with 1 – 5 years of work experience and 3) people with 5+ years of work experience, then ANOVA can be utilized. A researcher may want to examine whether income influences the consumption of an individual. In this case, regression analysis will be utilized.

Groups allowed in independent t-test and one-way ANOVA

Independent t-test only allows comparison of means for two groups. These can be for example males’ and females’ ages, those who agreed and those who disagreed salaries, among other comparisons of two groups. One – way ANOVA allows comparison of two or more groups. There is no specific maximum number groups that one-way ANOVA can analyze. It can be the comparison of scores in a statistics test based on 3 or more races example, Whites, Spanish, Black and Asians.

Multiple regression is used when the researcher wants to examine if two or more independent variables influence the dependent variable. It can tell the researcher which of the given independent variables have a significant impact and which variables don’t. When examining factors that influence performance of students for example, the predictors used can be time spent studying, mode of teaching, family income, parents’ performance and gender. Multiple regression will help in determining which of the variables have significant impact on performance and eliminates those variables without significant impact.

Multiple regression can also determine the anomalies and outliers of variables. It can tell the researcher which variables have a strong correlation with the dependent variable.

Similarity and difference between correlation and simple regression

Both simple regression and correlation can be used to examine the direction of the relationship between two variables. A positive correlation implies a positive coefficient in the simple regression analysis.

The difference between the two is that, simple regression examines the impact of x on y which helps in prediction of y given x while correlation just looks at the linear relationship between the variables of interest.

Reference

Kim, T. K. (2017). Understanding one-way ANOVA using conceptual figures. Korean journal of anesthesiology70(1), 22.

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