Question

Question 2. CORRELATION Technique

a. Describe the purpose of the Correlation technique

b. State at least two research problems or question that requires the use of the Correlation technique

c. Identify, describe and test the assumptions related to the Correlation technique

d. State examples of the Correlation technique outcomes

e. State examples of the Correlation technique report results

Answer 1

a) to find out if there is any relationship particularly linear between variables.

b)1)in finding out the relation between the poverty and population

2)price and demand of a commodity

c)1. Normality means that the data sets to be correlated should approximate the normal distribution.

2. Homoscedascity means ‘equal variances’. It means that the size of the error term is the same for all values of the independent variable. If the error term, or the variance, is smaller for a particular range of values of independent variable and larger for another range of values, then there is a violation of homoscedascity. It is quite easy to check for homoscedascity visually, by looking at a scatter plot. If the points lie equally on both sides of the line of best fit, then the data is homoscedastic.

3. Linearity simply means that the data follows a linear relationship. this can be examined by looking at a scatter plot. If the data points have a straight liner relationship, then the data satisfies the linearity assumption.

4. Continuous variables: variables must be continuous

5. Equal in number: no. of observations of the variables must be equal

6. forces operating on each variable should not be independent

d)the outcomes may be positively correlated, negatively correlated or no correlation. for example, if we get the correlation between two variables as 0.5 then the variables are positively correlated ie as one variable increases the other decreases.if we get -0.2 then the variables are negatively correlated ie., as one variable increases the other decreases.

e)if we get the correlation between two variables as 0.5 then we can say that there is 50%  positive association between the variables. if we get -0.9 then we say that there is 90% negative correlation between the variables. if the correlation is 1 then there is a perfect positive linear relationship between variables and if it is -1 then there is a perfect negative correlation between variables.