Q .Provide an example of where you could use correlation in real life. Explain why a t-test is necessary before you accept this correlation as being real in the population.

A. In statistics, correlation is any statistical relationship, whether causal or not, between two random variables or bivariate data. Correlations are useful because they can indicate a predictive relationship that can be exploited in practice. For example, an electrical utility may produce less power on a mild day based on the correlation between electricity demand and weather. In this example, there is a causal relationship, because extreme weather causes people to use more electricity for heating or cooling. However, in general, the presence of a correlation is not enough to infer the presence of a causal relationship. An example of positive correlation would be height and weight. Taller people tend to be heavier. A negative correlation is a relationship between two variables in which an increase in one variable is associated with a decrease in the other.

A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. A t-test looks at the t-statistic, the t-distribution values, and the degrees of freedom to determine the statistical significance. The test is used to show whether the answer is correct and can suppose the answer. Once you see the graph it will show if the answers are skewed in a perfect negative line or slope or in a positive line or slope.

REFERENCES

Lind, D. A. (2018). Statistical techniques in business and economics. New York: McGraw Hill Education.

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