Executive Summary – Cost of Living
According to the final MRL and summary output, the regression is 11.74895, which is equivalent to a p-value of 0.0004996. Using a significance level of 0.05, the results show a 5% significance, since 0.0004996 is less than 0.05. In that case, the model fits the data value presented at the defined significance level. The overall results of this regression show that when estimating and predicting NYC’s cost of living in the future, data value presented closely relates to the model. When evaluating independent variables of the cost of living, independent variables without a great significance in the determination of cost of living in New York City include rent, milk, a bottle of wine, and coffee, whereby they all have a p-value higher than 0.05. As seen in the output summary, rent has a p-value of 0.44, milk 0.17, a bottle of wine 0.26; and coffee has 0.70. Values higher than 0.05 indicate that these independent variables have no substantial effect on cost of living in New York City.
Independent variables with a significant influence on the cost of living include bread and public transport. Whereby the p-value of bread is 0.03, and that of public transport is 0.003, concluding that they are both less than 0.05 significance level. Secondly, the p-value of R Square, according to summary outputs, is 0.87, which shows that the model explains approximately 87% of the data value. The data value presented on the cost of living in NYC closely fits the regression line by 87%, making the model a good fit. According to the p-value and regression results, the null hypothesis that predicts NY’s cost of living index as 97.7% is firmly rejected, hence, a better cost of living for the company in the future.
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