Big or small-let’s save them all: Uncovering the factors responsible- Multiple Regression Analysis

Multiple regression analysis is tool that allows you to expand on your research question, and conduct a more rigorous test of the association between your explanatory and response variable by adding additional quantitative and/or categorical explanatory variables to your linear regression model. I discuss this in detail in this post

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Big or small-let’s save them all: How the data was collected

For this research study, I have chosen the “Gapminder Codebook”. It combines longitudinal survey data on respondents’ social, economic, psychological and physical well-being with contextual data on the family, neighbourhood, community, school, friendships, peer groups, and romantic relationships, providing unique opportunities to study how social environments and behaviours in adolescence are linked to health and achievement…

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Big or small: let’s save them all: Exploring Statistical Interactions

Statistical interaction describes a relationship between two variables that is dependent upon, or is moderated by, a third variable. The effect of a moderating variable is often characterized statistically as an interaction. That is a third variable that affects the direction and or strength of the relation between your explanatory, or x variable, and your…

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Big or small: let’s save them all: Pearson’s Correlation Coeffecient

The Pearson’s correlation (denoted by r) is the inferential test that will be used to examine the association between two quantitative variables. I previously discussed that a scatter plot is an appropriate way to visualize two quantitative variables when you want to examine the relationship between them. Now, let me first briefly review the scatter plot and…