SPSS linear and logistic regression
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My case study assignment includes applying linear and logistic regression tests using SPSS research methods. Please, explain the difference between these methods and when they are used. Thanks a lot!

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Linear and Logistic regression are SPSS research methods that are used to investigate the relationship between a dependent and a set of predictor or independent variables. Linear regression, on one hand, is a parametric SPSS research method that seeks to investigate the causal effect of the independent variables versus the dependent variables. Being a parametric statistical tool, a linear regression model must satisfy a number of assumptions in order to conclude the validity or significance of the model. One of the key assumptions that a linear regression model must satisfy is the assumption of a normal of the distribution, which is usually addressed by having a large sample size (by virtue of the Central Limit Theorem – a distribution approaches a normal distribution if the sample size is increased). In addition, the dependent variable in a regression model should be numerical or interval scale. The linear regression coefficients signify the relative change on the dependent variable for every unit change in a specific predictor variable.
Logistic regression, on the other hand, is a nonparametric SPSS research method. That is, it does not require a large sample size to satisfy the normality assumption. It is also used is the dependent variable is a binary variable (e.g. success vs failure). Unlike in linear regression, logistic regression coefficients signify the likelihood or odds of observing the predefined “success” in the dependent variable.
There are many differences between linear regression and logistics regression. I am trying to explain it in an easier way.
Suppose you are working with two variables and both the variables are continuous as well as linearly related. We can call these variables as X variable and Y variable respectively. Besides, we think X variable is a predictor and Y variable is the response. On the other hand, for SPSS research, we also can assume that one variable will be called as dependent variable and another variable will be called as independent variable. These names give us an idea that one variable is dependent to other variable.
Now, if we increase the value of independent variable, we will find a result for dependent variable whether it will increase or decrease or remain the same value. In this situation, for SPSS research, we can run linear regression. However, it is not necessary to take only one independent variable, you can take more than one or a number of independent variable to see the change of dependent variable. If you do this, you will find the regression in multiple dimensions. The equation of linear regression is given below.
y = ax + c
For example, you can take two variables in your SPSS research like as teachers’ age and students’ performance and find out whether there is any relation between the increasing of age and increasing of students’ performance.
On the other hand, the idea of logistic regression is very different than the linear regression. The main character of logistic regression is that the nature of the predictor is continuous in all time but the nature of the response can be changed. It can bear either categorical value or dichotomous value. From that analysis, you can get the idea about a situation of probability of an event. The equation for logistic regression is
y = c / (1 + ae^ (bx))
I hope you understand the differences between the linear and logistics regressions which is very important in statistics and research. Using SPSS help, you can learn more about these issues.