# Apply Correlation, Causation, Predication, Confidence, and Errors

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Correlation, Causation, Prediction, Confidence, and Errors Name Institutional Affiliation Date Correlation, Causation, Prediction, Confidence, and Errors Chapter Seven Problem 1 The graphic representation of a scatter diagram can help in indicating the relationship between two different variables (Bennett, Briggs, & Triola, 2014). The scatter diagram illustrates a case of positive correlation since as the explanatory variable X increases, the response variable Y also increases. Additionally, as the X variable decreases, the Y variable decreases. Even though the two variables are positively correlated, there is one outlier entry at point (6.00, 22.5). Problem 2 In this case, the relationship between the X and the Y variables reveals a situation of negative correlation due to the downward trend. The values of the variables move in opposite directions since as the value of X increases, that of Y decreases. Conversely, as the value of X decreases, the value of Y increases. The outlier entry, in this case, occurs at point (8.00, 10.00). Problem 3 (Data attached in the Excel file) The scatter diagram reveals that the two variables (mean daily calories and infant mortality rate) have a strong negative correlation. Mean daily calories are negatively related to infant mortality rate, that is, as mean daily calories increases, there is a reduction in the infant mortality rate. Also, a decrease in infant mortality rate can be noted as the mean daily calories intake increases. The data entry for both variables has outliers at point (3429, 44) and (2671, 7). From the data, the correlation coefficient is r = -0.901784376. Since the Pearson’s

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