The unit of measurement used to calculate the intensity in the linear relationship between the variables involved in a correlation analysis.

The closer r is to +1.0 or -1.0 the stronger is the co-varying association between the two variables.

The closer r is to 0.0 the weaker the co-varying association is between the variables.

A positive correlation between two variables means both the variables move in the same direction.

An increase in one variable leads to an increase in the other variable and vice versa.

For example, spending more time on a treadmill burns more calories.

A negative correlation between two variables means that the variables move in opposite directions.

An increase in one variable leads to a decrease in the other variable and vice versa.

For example, increasing the speed of a vehicle decreases the time you take to reach your destination.

No correlation exists when one variable does not affect the other.

Descriptive Statistics can be seen as a snapshot of a scene; inferential statistics can give insights into what came before and what is likely to come afterwards.

Inferential statistics can be used to prove or disprove theories, determine associations between variables, and determine if findings are significant and whether or not we can generalize from our sample to the entire population.

Regression is a proposition placed under examination, which cannot be true unless proven and tested statistically.

The truth or falsity of a statistical hypothesis is never known with certainty unless we examine the entire population.

It refers to the statement about the absence of any effect claimed for a certain action.  It also asserts the absence of difference between the observed and expected values.

Hypothesis is a precise, testable statement of what the researcher(s) predict will be the outcome of the study.

If your test result p-value>0.05 your observed difference is deemed to be NOT statistically significant

If your test result p-value<0.05 your observed difference is statistically NOT significant.

It is used to predict the value of a variable based on the value of another variable.

The variable you want to predict is called the independent variable.

The variable you are using to predict the other variable's value is called the dependent variable.

Dependent variable (aka criterion variable) is the main factor you are trying to understand and predict.

Independent variables (aka explanatory variables, or predictors) are the factors that might influence the dependent variable.

A one-way Analysis of Variance tells you if there are any statistical differences between the means of three or more independent groups.

It is used when you want to know if there is an association between two categorical (nominal) variables (i.e., between an exposure and outcome)

Usually, the higher the chi-square statistic, the greater likelihood the finding is significant, but you must look at the corresponding p-value to determine significance

Are blood pressure and weight correlated? What analytical test can be used?

In the statement, "Do normal weight (group 1) patients have lower blood pressure than obese patients (group 2)?" what statistical test can be used?

Are obese individuals (obese vs. not obese) significantly more likely to have a stroke (stroke vs. no stroke)? What statistical test can be used?

What statistical test can be used to determine "Does obesity predict stroke (stroke vs. no stroke) when controlling for other variables"?

In the depicted figure, determine the Strength of Association.

In the depicted figure, determine the Strength of Association.

In the depicted figure, determine the Strength of Association.

In the depicted figure, determine the Strength of Association.

In the depicted figure, determine the Strength of Association.

Coefficient range:

+ or – 0.21   to   + or – 0.40

Coefficient range:

+ or – 0.71   to   + or – 0.90