Correlation and Regression Analysis: Everything You Need to Know

Select at least three variables that you believe have a linear relationship. 

In my project, I will be examining on how I can find the linear relationship between three different variables; the dependent variable, the variable of age, and the variable of gender under which I will calculate it into SPSS calculations. 

Specify which variable is dependent and which are independent. 

Age is an independent variable 

I will use body mass index as the dependent variable 

The third variable will be gender 

Collect the data for these variables and describe your data collection technique and why it was appropriate as well as why the sample size was best. 

In obtaining my data, I chose a group of clients from my gym classes — analysis of the data through the Citrix Receiver. I used an IBM SPSS data editor to collect the needed information ( Kashyap, Wahid, & Shokeen, 2019). With the help of the receiver, I keenly applied their procedures for keying in data. To help in the collection of data, the design used was amongst the subjects, and it involved 20 participants all of the different ages, BMI’s, and the compilation was inclusive of both gender, male and female. 

Submit the data collected by submitting the SPSS data file with your submission. 

From the Citrix receiver, I gathered enough data and is was shown at the end of the document in the appendix section (Kashyap, Wahid, & Shokeen, 2019). 

Find the Correlation coefficient for each of the possible pairings of dependent and independent variables and describe the relationship in terms of strength and direction. 

Correlations 

From the above data it's noticeable that there exists a positive correlation between both BMI and the age. There is also a correlation coefficient of +1 between BMI and gender (Messick, 1995). The observation is that as a person ages then the BMI rises and in women, you will realize that the rate of rising is faster. 

Find a linear model of the relationship between the three (or more) variables of interest. 

Explain the validity of the model. 

We find the Ideal Linear regression when we square the value of r, and the result lies between 0 and 1. The explanation we get here is on determining how much variability exists in the dependent parameter ( Kashyap, Wahid, & Shokeen, 2019) . The independent parameter makes the explanation for this. When analysis s done in the linear model, there will be a suggestion of a solid correlation between rising BMI and aging. It is realized that when aging, gender does play a big role in the increase in BMI. 

Any linear regression model will always have an equation in the form of Y= a + bX. 

X= explanatory variable 

Y= dependent variable 

b= refers to the slope of the line, and a= the intercept of the line which is the value of y when x =0 

The results for P-value are; 

Chi 2 = 233.458 and   DF=1   

From the above data, the 2-tailed P value is less than 0.0001 

Using set criteria then we will agree that the difference here is tremendously statistically important. 

References 

Kashyap, K., Wahid, A., & Shokeen, V. (2019). Establishing the Correlation Relationship between Size of Code and New Functionalities Using Regression Line Equation. In Emerging Technologies in Data Mining and Information Security (pp. 511-517). Springer, Singapore. 

Messick, S. (1995). The validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning.  American Psychologist, 50 , 741-749. 

Appendix