PARAMETRIC Pt.3 Flashcards
- Determines whether there exists a relationship between variables
Correlation
movement together
- ρ(x,y) = ρ(y,x)
Correlation
Describe the nature of the relationship between variables.
Regression
- one variable affects the other
- cause and effect
- one way
Regression
2 TYPES OF RELATIONSHIPS
Simple Relationship
Multiple Relationship
1 independent (explanatory/predictor) and 1 dependent (response) variable
- can also be positive (directly proportional) and negative (inversely proportional)
Simple Relationship
- multiple regression
- > 2 independent variables are used to predict 1 dependent variable
Multiple Relationship
This ___________ means that there is no correlation between the x and y variables in the population.
null hypothesis
This _________________ means that there is a significant correlation between the variables in the population.
alternative hypothesis
PEARSON PRODUCT MOMENT CORRELATION COEFFICIENT
SCATTER PLOTS
- determines the strength of the linear relationship between two variables.
Correlation Coefficient
2 types of Correlation Coefficient
population & linear
- denoted by the Greek letter ρ is the correlation computed by using all possible pairs of data values (x, y) taken from a population.
Population correlation coefficient
Population correlation coefficient is denoted by
ρ
measures the strength and direction of a linear relationship between two quantitative variables.
Linear correlation coefficient
The symbol for the sample correlation
coefficient is
r
Linear correlation coefficient is also called
Pearson product moment correlation coefficient (PPMC)
Pearson product moment correlation coefficient (PPMC) is also known as
Linear correlation coefficient
Range of the linear correlation coefficient is from
-1 to +1
the value of r will be close to +1.
Strong positive linear relationship
the value of r will be close to -1.
Strong negative linear relationship
weak realtionship
No linear relationship
the value of r will be close to 0.
No linear relationship
implies only that there is _______________ between the variables
No linear relationship
x causes y | There is a _______ cause-and-effect relationship between the variables.
direct
y causes x | There is a _________ cause-and-effect relationship
between the variables.
reverse
When the null hypothesis has been rejected for a specific α value, any of the following five possibilities can exist.
- There is a direct cause-and-effect relationship between the variables.
- There is a reverse cause-and-effect relationship between the variables.
- The relationship between the variables may be caused by a third variable.
- There may be a complexity of interrelationships among many variables.
- The relationship may be coincidental.
- The sample must be randomly selected from the population
- The population must be normally distributed for the variable under study
- The observations must be independent of one another
CHI-SQUARE DISTRIBUTION (1 VARIANCE)
