Executive Summary This is an analysis conducted on a dataset for transportation for various cities of the United States of America. The analysis studies the dataset from the perspective of how the number of weekly riders level of the population of the cities varies with respect to a set of predictors. It explores the effect of each of the selected predictors on the number of weekly riders. The analysis derives an equation to predict the number of weekly riders of any given number of the city based on its predictor values. It also attempts to bring out the cross-dependence of the selected predictors. The independent variable is depended on four dependent variables, the average parking rates per month and the populations of the city are seen …show more content…
This model also analyzes the impact of several factors that influence the number of weekly riders on the transportation in various cities in the United States of America. The factors considered to demand of transit in various cities are riding price per week, the population of the city, monthly income of riders, average parking rates per months. Another contributing factor is the percentage of people living in cities, which is easily accessible by the rider. This paper also gives an insight into the areas where any corrective measures can be incorporated to increase demand of the number of weekly riders in various cities in the United States of America. This paper also analyzes the effect of the number of weekly riders on demand for transit in various cities in the United States of America. Data The data report shows the number of weekly riders who visit various cities in the United States of America. The report shows the number of weekly riders various cities for predictors. The predictors used are riding price per week, Population of the city, Monthly income of riders, and Average parking rates per