Predictive Modeling And Regression Analysis Using SPSS Assignment Help

Predictive Modeling training aims to teach and improve predictive modelling skills in a variety of business sectors/domains. Quantitative methods and predictive modelling concepts could be widely applied in understanding current customer behaviour, financial market movements, and researching tests and effects in the medical and pharmaceutical sectors after drugs are administered. For predictive analysis, the course selects theoretical and practical datasets. SPSS software is used for implementation. As we progress through the training, we explain observations, interpretations, predictions, and conclusions as we go through the examples. Other online courses do not cover higher order regression models like quadratic and polynomial regressions, which are covered in this course.

1. Essential skill sets include Prior knowledge of quantitative methods, MS Office, and Paint are required.
2. Desired skill sets include knowledge of data analysis and the VBA toolpack in Microsoft Excel.

KEY FEATURE

Predictive modelling is a mathematical process that analyses patterns in a set of input data to predict future events or outcomes.

Predictive modelling is a type of data mining that analyses historical data to identify trends or patterns and then uses those insights to forecast future outcomes.

Unsupervised models classify data directly using traditional statistics such as logistic regression, time series analysis, and decision trees.

Supervised models employ newer machine learning techniques, such as neural networks, to identify patterns buried in previously labelled data.

Predictive Modeling And Regression Analysis Using SPSS Assignment Help

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Key Topics

    What All You Can Learn From This Course:

    • The course works across multiple software packages such as SPSS, MS Office, PDF writers, and Paint.
    • This course is to specifically learn about Descriptive Statistics, Means, Standard Deviation and T-test Understanding Means, Standard Deviation, Skewness, Kurtosis and T-test concepts
    • Learn Importing Dataset and Correlation Techniques
    • Learn Linear Regression Modeling
    • Learn Multiple Regression Modeling
    • Learn Logistic Regression
    • Learn Multinomial Regression.

    Generalized Linear Model

    The Generalized Linear Model (GLE) is a widely used analytical algorithm for a variety of data types. It not only covers widely used statistical models like linear regression for normally distributed targets, logistic models for binary or multinomial targets, and log linear models for count data, but it also covers many useful statistical models due to its very general model formulation. Other useful features of the Generalized Linear Model include variable selection, automatic selection of distribution and link function, and model evaluation statistics. This model can handle a wide range of data and has regularisation options such as LASSO, ridge regression, elastic net, and so on.

    Linear Regression Analysis Using SPSS Statistics

    Following correlation, the next step is linear regression. It is used to forecast the value of one variable based on the value of another. The variable we wish to predict is referred to as the dependent variable (or sometimes, the outcome variable). The variable we are using to predict the value of the other variable is known as the independent variable (or sometimes, the predictor variable). You could, for example, use linear regression to determine whether exam performance can be predicted based on revision time, or whether cigarette consumption can be predicted based on smoking duration, and so on. You must use multiple regression if you have two or more independent variables rather than just one.

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