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Correlation and Regression: How to use in data analysis: Using IBM SPSS and Eviews
Many people have difficulties in distinguishing between correlation and regression; consequently they cannot apply these two procedures correctly. The aim of this book is to clarify the basic concepts of correlation and regression so that we can use them easily. Correlation belongs to independent relationship. That is why there is no independent and dependent variables in correlation. While regression belongs to dependent relationship. Accordingly, in regression there must be a variable that can be identified as an independent variable and another variable that can be identified as a dependent variable. To make it easy to conduct calculation, the analysis process of the data analysis will use IBM SPSS and Eviews. The contents of the book are as follows
Part Correlation
1. Definition
2. Uses of Correlation
3. Linearity Concepts
4. Assumption
5. Characteristics
6. Coefficient of Correlation
7. Significance / Probability
8. Interpretation
9. Hypothesis Testing
10. The basic Differences between Correlation and Causation
11. Advantages and Disadvantages Using Correlation
12. Spearman Rank Correlation, Pearson Product Moment Correlation and Partial Correlation
13. Exercises
Part Regression
1. Definition
2. Goals of Using Regression
3. Underlying Assumptions
4. Requirements of Using Regression
5. Linearity Concepts in Regression
6. Hypothesis Testing
7. Good Model Characteristics
8. Advantages and Disadvantages Using Regression
9. Main Parameters in R square, Adjusted R square, F, t, Constant (a), Unstandardised Coefficient (b) and Significance (p-value)
10. Simple Linear Regression, Multiple Linear Regression and Robust Regression
11. Exercises
Part Correlation versus Regression
1. When to Use Correlation
2. When to Use Regression
3. Similarities, Differences and Relationship Between Correlation and Regression
4. Understanding the How to Calculate Simple Linear Regression Manually
5. Exercises
Part Correlation
1. Definition
2. Uses of Correlation
3. Linearity Concepts
4. Assumption
5. Characteristics
6. Coefficient of Correlation
7. Significance / Probability
8. Interpretation
9. Hypothesis Testing
10. The basic Differences between Correlation and Causation
11. Advantages and Disadvantages Using Correlation
12. Spearman Rank Correlation, Pearson Product Moment Correlation and Partial Correlation
13. Exercises
Part Regression
1. Definition
2. Goals of Using Regression
3. Underlying Assumptions
4. Requirements of Using Regression
5. Linearity Concepts in Regression
6. Hypothesis Testing
7. Good Model Characteristics
8. Advantages and Disadvantages Using Regression
9. Main Parameters in R square, Adjusted R square, F, t, Constant (a), Unstandardised Coefficient (b) and Significance (p-value)
10. Simple Linear Regression, Multiple Linear Regression and Robust Regression
11. Exercises
Part Correlation versus Regression
1. When to Use Correlation
2. When to Use Regression
3. Similarities, Differences and Relationship Between Correlation and Regression
4. Understanding the How to Calculate Simple Linear Regression Manually
5. Exercises
100 pages, Kindle Edition
Published October 30, 2017
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