CLASSROOM CATALYST

OBJECTIVES:

Students will be able to understand the concept of correlation, identify the strength and direction of relationships between two variables, and interpret correlation coefficients to make informed conclusions about data patterns."

This objective aims to guide students toward a solid grasp of both the theoretical and practical aspects of correlation in statistics.

Correlation

Correlation refers to a statistical relationship between two variables, showing how one variable changes in relation to another. It helps us understand if and how strongly pairs of variables are connected. Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage Publications

Applications of Correlation in Research

Economics and Finance

Correlation is used to assess the relationship between variables like stock prices, interest rates, and GDP growth. Portfolio managers often analyze correlations to diversify risk in investment portfolios.

Social Sciences

Understanding correlations helps in identifying trends between socio-economic variables (e.g., income and education levels).

Healthcare and Medical Research

Researchers use correlation to study relationships between lifestyle factors (e.g., diet, exercise) and health outcomes (e.g., blood pressure, cholesterol levels).

Types of correlation

Positive Correlation

When one variable increases, the other variable also increases. For example, as the temperature rises, ice cream sales might increase.

Negative Correlation

When one variable increases, the other decreases. For example, as rainfall increases, the demand for umbrellas might rise, but the amount of sunlight might decrease.

No Correlation

When there is no discernible relationship between the variables. For example, shoe size and intelligence may have no correlation.

Table 1

Positive correlation

Study hours	Exam score	correlation	Interpretation
1	50	Positive	As study hour increases exam score increases
2	60	Positive
3	70	Positive

Table 2

Negative correlation

Exercise hours

Body fat %

correlation

Interpretation

Negative

As hours increase body fat decreases

Table 3

No correlation

Shoe size	IQ Score	correlation	Interpretation
3	119	No correlation	No relationship between IQ and shoe size.
4	113
5	117
7	154

Figure 1

REFERENCE

1.Anderson, Sweeney, and Williams (2019) Anderson, D. R., Sweeney, D. J., & Williams, T. A. (2019). Statistics for business and economics (13th ed.). Cengage Learning. (p. 567)

2.Moore and McCabe (2017) Moore, D. S., & McCabe, G. P. (2017). Introduction to the practice of statistics (9th ed.). W.H. Freeman and Company. (p. 143)

3.Kutner, Nachtsheim, and Niter (2004) Konner, M. H., Nachtsheim, C. J., & Niter, J. (2004). Applied linear regression models (4th ed.). McGraw-Hill/Irwin. (p. 157)

4.Weisberg (2014) Weisberg, S. (2014). Applied linear regression (4th ed.). Wiley. (p. 101)

5.Wheelan (2013) Whelan, C. (2013). Naked statistics: Stripping the dread from the data. W.W. Norton & Company. (p. 134)

6.Utts and Hecker (2017) Tuts, J. M., & Hecker, R. F. (2017). Mind on statistics (5th ed.). Cengage Learning. (p. 153)

7.Levine, Stephan, and Shabbat (2017) Levine, D. M., Stephan, D. F., & Shabbat, K. A. (2017). Statistics for managers using Microsoft Excel (8th ed.). Pearson. (p. 237)

8.Hair, Black, and Babine (2019) Hair, J. F., Black, W. C., & Babine, B. J. (2019). Multivariate data analysis (8th ed.). Cengage Learning. (p. 123)

9.Chatterjee and Hade (2015) Chatterjee, S., & Hade, A. S. (2015). Regression analysis by example (5th ed.). Wiley. (p. 78)

10.Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications.

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