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Problem Set 6, 7, and 8 - Assignment Example

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Suppose a Non-Governmental Organization wants to determine the incomes of various households in Boston and Massachusetts. It is known that household incomes in the two states are normally distributed with a mean of 50000 dollars and a variance of 56250000 dollars. A random…
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Problem Set 6, 7, and 8
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Download file to see previous pages Thus it is a measure of the strength of relationship between the two variables. David states that when the two variables are continuous-level variables, the value is called Pearson correlation coefficient (David, 1997. Pg100).
Suppose an economist wants to determine if higher oil prices increase the cost of shipping and the degree of the relation between the two, in this case, his main concern is the cost of shipping and how it is being affected by the changes in the oil prices; of which he wants to determine whether the shipping cost changes depend on the changes in the oil prices. Thus the dependent variable is the shipping cost while the independent variable is the oil prices. This is basically because the shipping cost depends on the oil prices. To answer his question, the economist can go ahead to collect historical data on the same and probably tabulate his results as below:
To measure the strength and direction of relationship/association between two variables, Spearman rank-order correlation can be used. It is a nonparametric measure. David notes that before using Spearman rank-order correlation, it is vital to check the validity of the data (David, 1997. Pg230). This is because not all data is qualified to be analyzed by Spearman rank-order correlation. Thus to use the spearman rank-order correlation, data must meet the following prerequisites:
Thus I selected this test to analyze the data based on these two conditions that have been met by the data. Suppose football analysts want to find out the order of the teams between two years in English premier league, Spearman rank-order correlation may be an option. The analyst has collected the following data:
1. Predicting unknown values of a variable from the value of several known variables (predictors) is done using multiple regression analysis. For example, we can predict the value of a dependent variable y so long as we are given the value of xi (x1, x2, x3, …xn)
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