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# Correlation, Causation, Prediction, Confidence, and Errors

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Correlation, Causation, Prediction, Confidence, and Errors
Correlation, Causation, Prediction, Confidence, and Errors
University
Statistics I

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Correlation, Causation, Prediction, Confidence, and Errors
Correlation, Causation, Prediction, Confidence, and Errors
1) Correlation is a measure that determines the degree to which two variables movements are
associated (Miles & Shevlin, 2000). The graph below shows the positive correlation. The
positive correlation is a correlation in which the values of Y tend to increase with the increasing
values of X (Bennett, Briggs & Triola, 2009).
It can be seen from the above graph that there is one outlier as pointed out by an arrow. An
outlier is an element of the data set that distinctly stands out from the rest of the data (Starnes,
Yates & Moore, 2010).
2) The graph below shows the negative correlation. The negative correlation is a correlation in
which the values of Y tend to decrease with increasing values of X (Bennett, Briggs & Triola,
2009).
It can be seen from the graph below that there are four outliers as pointed out by arrows. An
outlier is an element of the data set that distinctly stands out from the rest of the data (Starnes,
Yates & Moore, 2010).

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Correlation, Causation, Prediction, Confidence, and Errors
3a) The following graph shows a negative correlation between mean daily calories intake and
infant mortality rate i.e. the infants consuming more calories will have less mortality rates.
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0 500 1000 1500 2000 2500 3000 3500 4000
Infant Mortality rate (per 1,000 births)
Mean daily calories
Calories and Infant Mortality

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