b. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? Positive relationship Does there appear to be any outliers and/or influential observations? Observation 9 (U.S.) appears to be an observation with high leverage and may be very influential in terms of fitting a linear model to the data. c. Using the entire data set, develop the estimated regression equation that can be used to predict the debt of a country given the total value of its gold holdings. = 49.076 (to 4 decimals)+.1230 (to 4 decimals) Gold Value d. Suppose that after looking at the scatter diagram in part (a) that you were able to visually identify what appears to be an influential observation. Drop this observation from the data set and fit an estimated regression equation to the remaining data. = 30.768 (to 4 decimals)+.3422 (to 4 decimals) Gold Value Compare the estimated slope for the new estimated regression equation to the estimated slope obtained in part (c). Does this approach confirm the conclusion you reached in part (d)? The slope of the estimated regression equation is now as compared to a value of impact on the value of the slope of the fitted line and hence we would say .4652 .4652 when this observation is included. Thus, we see that this observation has big that it is an influential observation.

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Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related. 

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.