When we want to predict the value of two or more variables, then we use the method of multiple regression. This is the extension of linear regression. A variable which we have to predict is termed as a dependent variable, which sometimes called target, outcome or criterion variable. However, the using variable which helps to find the other variable is called independent variable or regress or variable, predictor, explanatory. If you want to know more information regarding this topic, hire Use of SPSS in Data Analysis assignment help.

Example of Multiple Regression in SPSS

Multiple regression helps to analyse the exam performance which can be determined with the lecture attendance, test anxiety, revision time, etc. Alternately, you can use it to determine consumption of cigarette by knowing the age, smoking duration of any person.

For going through the process of multiple regression, it is necessary to observe these significant assumptions as it can affect your result. Wanted to see how? Have a look

Assumption 1

Two chosen variable must be measured at a continuous level, that means either in ratio or interval variable. The most significant examples of continuous level variables include intelligence which is measured by IQ score, revision time (time is taken in hours), weight which is measured in kilograms, exam performance which is calculated from 0 to 100 and so on. If you want to explore more examples, hire assignment writer sydney.

Assumption 2

The linear relationship is needed between these two variables. There are several ways which can be used to find the linear relationship between them like, you can create a scatter plot with the assignment help of SPSS statistics in which your plot dependent variable against the predictor and check visually for linearity. You can see how your scatter plot will look like:

If in a scatterplot, the relationship is not linear, then either you have to perform polynomial regression or non-linear regression analysis. Both could be done with SPSS statistics.

Assumption 3

There must be no significant outliers where outliers mean that observed data point has any value which is dependent or varies with the predicted or given regression equation. If the outliers are far from the regression line than it signifies that there is a large residual.

Still, outliers show negative effects as it reduces the value of the regression equation which means you are predicting the value of independent or predictor value and dependent variable, i.e. outcome. This will surely change the SPSs statistics; resultant less predictive accuracy. Fortunately, it’s great to know that when you use SPSS statistics for running the linear regression, then it easily detects outliers which may be present.

These three assumptions are often taken as a note while doing multiple regression. However, there are more assumptions which must be taken care of. To know more assumptions, hire assignment writer sydney.

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