This paper examines how the pricing strategy of BWI airport and how the market share values between air carriers impact BWI’s pricing of airline tickets. In analyzing the airfare, distance, average passenger, and market share over four years, the airport can determine which airline has the largest and smallest percent stake in the market. By finding out the market share’s impact, the airport can then adjust its flights and pricings to entice and attract more travelers to generate more profit.
After computing a statistical and tabular summary of data on the airfares from BWI (Figure 1), one can draw many conclusions about the distribution of airfares. The lowest price a ticket was purchased at, also known as the minimum price, was $55. The most …show more content…
The mean is a measure of central tendency can be used to explain the average price a ticket was sold for. If one were to estimate the airfares for a randomly chosen route, based on the data the airfare would be the mean of $159. By constructing a pivot table that shows the relationship between fare and the number of tickets purchased within a certain price range (Figure 2a), one can also see the frequency at which prices most people bought their tickets. In Figure 2a, 109 passengers out of the total 164 bought tickets within the price range of $95 to $195. Here 66.5% of people fall within this price range, and this supports why the mean fare is $159. The statistical summary Figure 1 is a descriptive statistic as it is a summary in either tabular, graphical or numerical form. Also, “the average mean is $159” a simple, yet valid example of a descriptive statistic is it reflects the sample data. Also Figure 2a and 2b provide a table and a histogram, respectively, that present the data in an organized way to be interpreted. A statistical inference or inferential statistic is using data from a sample …show more content…
Fare is dependent because it is a compilation of the other variables together, while the other variables are independent as they are not composed of any other factors, in this data set. The dependent variable and each of the independent variables should be analyzed so that the variability in each relationship of the dependent variable to the independent variables is clear. The estimated multiple regression equation for sale price based on all of the variables, excluding destination, is: estimated fare (in $) = 65.975 +.057*distance-.035*avg passenger +90.004*mkt share +.681(if 2012) +.312(if 2013) +10.058(if 2014). The estimated equation does match my expectations of an independent and dependent variable relationship, because all the independent variables are shown in the equation to represent their effect on the dependent variable: fare. The market share coefficient shows the fraction of that market that the biggest carrier possesses for a particular airline and its effect on the fare of the flight. Now, to interpret the coefficients in terms of year, depending on which year is being analyzed (with 2011 the baseline year), the coefficient of the analyzed year will be multiplied by a 1 and the other coefficients will be multiplied by a 0 (once again 1 denotes the year is the desired year to be analyzed and a 0 denotes the years not being
After computing a statistical and tabular summary of data on the airfares from BWI (Figure 1), one can draw many conclusions about the distribution of airfares. The lowest price a ticket was purchased at, also known as the minimum price, was $55. The most …show more content…
The mean is a measure of central tendency can be used to explain the average price a ticket was sold for. If one were to estimate the airfares for a randomly chosen route, based on the data the airfare would be the mean of $159. By constructing a pivot table that shows the relationship between fare and the number of tickets purchased within a certain price range (Figure 2a), one can also see the frequency at which prices most people bought their tickets. In Figure 2a, 109 passengers out of the total 164 bought tickets within the price range of $95 to $195. Here 66.5% of people fall within this price range, and this supports why the mean fare is $159. The statistical summary Figure 1 is a descriptive statistic as it is a summary in either tabular, graphical or numerical form. Also, “the average mean is $159” a simple, yet valid example of a descriptive statistic is it reflects the sample data. Also Figure 2a and 2b provide a table and a histogram, respectively, that present the data in an organized way to be interpreted. A statistical inference or inferential statistic is using data from a sample …show more content…
Fare is dependent because it is a compilation of the other variables together, while the other variables are independent as they are not composed of any other factors, in this data set. The dependent variable and each of the independent variables should be analyzed so that the variability in each relationship of the dependent variable to the independent variables is clear. The estimated multiple regression equation for sale price based on all of the variables, excluding destination, is: estimated fare (in $) = 65.975 +.057*distance-.035*avg passenger +90.004*mkt share +.681(if 2012) +.312(if 2013) +10.058(if 2014). The estimated equation does match my expectations of an independent and dependent variable relationship, because all the independent variables are shown in the equation to represent their effect on the dependent variable: fare. The market share coefficient shows the fraction of that market that the biggest carrier possesses for a particular airline and its effect on the fare of the flight. Now, to interpret the coefficients in terms of year, depending on which year is being analyzed (with 2011 the baseline year), the coefficient of the analyzed year will be multiplied by a 1 and the other coefficients will be multiplied by a 0 (once again 1 denotes the year is the desired year to be analyzed and a 0 denotes the years not being