The previous content of correlation analysis which I wrote is concerned with explaining the concept of  positive correlation coefficient.

![Screenshot (13).png](https://images.hive.blog/DQmZDPYmoz4ucwoYQCR7U27JdKMGwgD1aQuBUMJB5m5hhP6/Screenshot%20(13).png)

 This article will also focus on another kind of question as regards negative correlation coefficient.

If you are interested in learning the use of spss, then this guide is mearnt for you.

Overview of correlation analysis
As stated in my last post on correlation, it’s been defined as a test that finds the relationship amongst variables.
 when it is only two variables, It is referred to as bivariate correlation. .Bivariate simply means there is a consideration of only two  variables.
The theories attached to correlation analysis are;

Hypothesis
Hypothesis is a statement of fact of a data depending on the nature of test concerned about. 

Null hypothesis for correlation test is that there is no relationship between the variables.

The relationship is mearnt for variables linked in a process.
Secondly, the correlation coefficient is determined by considering the following fact.

Positive correlation
A positive correlation means that the higher one variable, the higher the other variable. This entails the coefficient of correlation is positive as well and tends towards a positive one.

Negative correlation
A negative correlation between two variables indicates that the coefficient is negative as well and tends towards a negative one. Meaning that the higher one variable, the lower the other variable as well.

Zero correlation
Zero correlation indicates that there is no relationship at all between the variables in consideration and the correlation coefficient on a number line tends towards zero.

The coefficient of correlation resides on a line of -1 to 0 to +1.

Practical Question As Regards Negative Correlation Analysis
The data below were results gotten from the gae and strength of a man.
Age (years)        strength (joules)
18            40
19            38    
20            40
22            37    
25            39        
30            37
35            36
37            35
40            34
45            32
56            28
Use the data below to explain what is the relationship between the age of the man and the level of strength he has.

Of course what we are to do is to open the spss application and navigate to variable mode to enter the     variables which is as seen below.

![Screenshot (15).png](https://images.hive.blog/DQmaP6w2LWDs9zebUjS2TKLt7QuMH4wGsTtWXMap5SupoFh/Screenshot%20(15).png)

 You have to also change your measure to scale because both variables can be measured or quantified.
And also set decimals to zero.
After that, toggle to the data view mode to key in the data     as seen below

To carry out the correlation analysis test,
Click on analyze
Select correlate,
Click on bivariate
Move the variables to the variable dialogue box. Or better still use the arrow provided and click ok.

![Screenshot (16).png](https://images.hive.blog/DQmQU62dn86wnNFr43s2f6Xa9Lm7w4wcmcwdFJ7TFijBMGE/Screenshot%20(16).png)

The result of the analysis is as shown

![Screenshot (17).png](https://images.hive.blog/DQmew9GfZxz4zPTEuwuQn44HzoPVD3PNb5LR21zRp9NyWmL/Screenshot%20(17).png)

We can thus interprete that the null hypothesis should be rejected because there is a highly negative correlation which indicates the younger the age of a man, the stronger he will  be than when he is old such man may not be as strong as we was when he was young.

Conclusively
This article has been able to present the concept of correlation analysis. Especially negative correlation and it's analysis. The negative correlation analysis was used as an example for explanation.