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QNT561Dr. Heidi carty12017Christophe Agbande, Lee DeVau.docx
- 1. QNT/561 Dr. Heidi carty 1/20/17 Christophe Agbande, Lee DeVaughan, sheri lebeau, qiana Reynolds, Andrew rice Business Research Project Introduction Dyeus Airlines, an international airline, was founded in 2013. Dyeus Airlines is facing challenging competition from other, larger airlines around the world. In the hopes of competing with the various airline companies, Dyeus has decided to pilot a system where the airline waives all excess luggage fees. Most airlines charge a fee for excess baggage, as well as overweight luggage, and it will be the strategy of Dyeus Airlines to waive the first two pieces of luggage from all additional fees. Due to weight restrictions of the Federal Aviation Administration, any bag over the weight of 50 pounds will be subject to a $50 fee for special handling guidelines.
- 2. Research question Null hypothesis/alternate hypothesis Literature review Sampling approach
- 3. Descriptive statistics Inferential statistics Summary of research results Summary of challenges Implications and recommendations
- 4. charts references Buleon, C. (2014, Spring). Checked Bags Around the Airline Business. Retrieved from http://traveltips.usatoday.com/southwest-airlines-checked- baggage-rules-63778.html Carter, D., Roger, D., & Simkins, B. (2012, September). Does Fuel Hedging Make Economic Sense? The Case of the US Airline Industry. Retrieved from http:/dx.doi.org/10.2139/ssrn.325402 Lind, D., Marchal, W., & Wathan, S. (2014). Statistical techniques in business and economics (16th ed.). New York, NY: McGraw-Hill Irwin. Schumann, H., & Singh, H. (2014, September). The Market Structure Dynamics Created by De-Bundling of Airline Bag Fees. Journal of Economics and Economic Education, 15(3), 186-197. Scotti, D., & Dresner, M. (2015, October). The Impact of Baggage on Passenger Demand on US Air Routes. Transport Policy, 43(1), 4-10. doi:10.1016/j.tranpol.2015.05.017 Sumit, A., Souphala, C., Neale, M., & Johannes, S. (2014, June). Benefit-Cost Analysis Financial Regulation. Journal of
- 5. Legal Studies, 43(2), 239-252. Zane, C., & Reyes, P. (2014, June). Airlines Plight' Where has all the Luggage Gone? Mamagement Research Review, 33(7), 767-782. Retrieved from http://dx.doi.org/10.1108/01409171011055834 Collect the team members' Week 4 individual descriptive statistics and the Week 5 individual Inferential Statistics and Findings assignment papers and spreadsheets. Review each team member's descriptive statistics and inferential statistics and findings, incorporating the best elements into one team inferential statistics and findings paper and spreadsheet. If anyone changed the variables or research question, then you will need to redo the statistics to be using the same two variables so that your research paper is consistent. Points will be deducted if the same research question and variables are not consistent throughout the paper. · Note the strengths and weaknesses (you don't need to discuss this in the paper) · Where there are differences, collaborate with your team to find the best approach. (you don't need to discuss this in the paper). Complete the cumulative Business Research Project by combining all parts into a 2,450- to 2,800- word paper that is between by collaborating with your team to include the following: · sampling approach · data collection methods · descriptive statistics
- 6. · Revised tables or figures based on prior instructor feedback · Description or interpretation of the tables or figures · inferential statistical analyses · Summary of the results of testing the null hypothesis, with a clear statement whether or not the null hypothesis was rejected · Answers to the research questions · Conclusions that were derived from this study · An inconclusive result is acceptable for this project. Should that be the case, suggest potential future research efforts and a new research questions that might provide more definitive results. · Recommendations based on the results · Observations (reflection) on the business problem and its solution · Research challenges your team experienced in this study · Steps to minimize challenges in future research · Suggested future research based on your research results, challenges, and implications Format your Business Research Report consistent with APA guidelines. Develop a 12- to 16-slide Microsoft® PowerPoint® presentation that includes the following elements: · Overall picture of the research process (e.g., a slide for research question and hypothesis statements, literature review, sampling approach, data collection, descriptive statistics, etc.) · Summary of the research results including specific answers to the research question · Summary of the challenges, implications, and recommendations Submit the Business Research Report, Microsoft® Excel® spreadsheet, and Microsoft® PowerPoint® presentation to the Assignment Files tab.
- 7. Business Research Project Inferential Statistics Sheri LeBeau QNT/561 1/23/17 Dr. Heidi Carty Running head: BUSINESS RESEARCH PROJECT 1 BUSINESS RESEARCH PROJECT 6 Inferential Statistics The Hypothesis being tested is, “Passenger boarding times will be decrease when the bags are free.” And the Null Hypothesis is “There is no difference in boarding times when bags are free.” The data collected for the study was boarding times, with paid luggage, and boarding times, with free luggage.
- 8. To reject the null hypothesis, there will be a difference between the boarding times. The Independent variable is whether or not the first two checked bags are free, and the dependent variable is flight boarding times. The data was mined from collected data, both six months in the past and six months in the future. The independent variable data, whether the bags were free or not free, has already been collected. The t-test was used for averages of two samples, boarding times when baggage is free, and boarding times when baggage is paid. The t-test chosen was the t-test paired. The confidence level alpha is 1, with .05 for a confidence level of 95%. If the P value<alpha, then we reject the null hypothesis. If the P value is >alpha then we fail to reject the null hypothesis. P= 4.16187E-08>1, so the null hypothesis is rejected. Appendix A t-Test: Paired Two Sample for Means Free
- 9. Paid Mean 9.666666667 13.88333333 Variance 12.97175141 12.88446328 Observations 60 60 Pearson Correlation -0.041079213 Hypothesized Mean Difference 0 df 59 t Stat -6.29536021 P(T<=t) one-tail 2.08094E-08 t Critical one-tail 1.671093032 P(T<=t) two-tail 4.16187E-08 t Critical two-tail 2.000995378
- 10. Appendix C Reference Lind, D., Marchal, W., & Wathan, S. (2014). Statistical techniques in business and economics (16th ed.). New York, NY: McGraw-Hill Irwin. FreePaid 515 717 1513 1416
- 12. 1314 712 1519 1512 1510 617 510 1310 1218 918 88 1210 1511 1112 1018 1120 1216 515 515 914 SHORT TITLE OF PAPER 1 Running head: DESCRIPTIVE STATISTICS 1Descriptive StatisticsSheri LeBeauQNT/561 1/16/17 Dr. Heidi CartyDescriptive Statistics The following descriptive statistics are based on analyzing a sample of raw data obtained from data mining for 6 months of charged baggage, and six months of free baggage, to determine if there is any impact on boarding times. The raw data is contained in Appendix A. Free Baggage Central Tendency: The mean of the data set is 9.789041
- 13. Dispersion: The standard deviation is 3.214304 Number: 365 Min/Max: 5 MIN/15 MAX Confidence Interval: 9.789041+-.160 Charged Baggage Central Tendency: The mean of the data set is 13.967123 Dispersion: The standard deviation is 3.5392639 Number: 365 Min/Max: 8 MIN/20 MAX Confidence Interval: 13.96712 +-.176 Descriptive Statistics Interpretation Free Baggage The first two items of checked baggage are free, with the exception of overweight fees. Charged Baggage The first two items of checked baggage are charged for, with the addition of overweight fees. Appendix A Raw data used in the analysis FREEColumn1Column2Column3Column4Column5Column6Colu mn7Column8Column9Column10Column11Column12Column13 Column14Column15Column16Column17Column18Column19 51513711107910713111214135768 76712961461478141186141165 1515159891111681112913156675 14131515151285158151511811912117 6111561411611101115812159141278 67612116135111214711979712 66515511913101212914131313135 139131078915969612677610 1512121212157151381512615118156 789614121151156614614989
- 15. Appendix B Charts and Tables Appendix C Descriptive Statistics RUNNING HEAD: INFERENTIAL STATISTICS 1 ( Inferential Statistics Christophe Agbande QNT/561 January 23 , 2017 Dr. Heidi Carty ) The research question is whether there is a correlation in Dreyus Airlines profit depending on the decision to charge for checked bags or not to charge for checked bags. For this project, the dependent variable to charge for checked bags or not to and the independent variable is the policies effect on the airlines profitability. To accomplish this study, forty flights were sampled, making a dataset of sample size forty. The customers with checked bags was on a Likert scale of 1-10, and the profitability was measured in checked bags. In helping establish whether there is a relationship between the two variables, a regression slope test was performed. The hypotheses turned out this way: (Not a significant linear relationship between charging or not charging for checked bags and airline profits)
- 16. (Is a significant linear relationship between charging or not charging for checked bags and airline profits) I have inputted the regression analysis from the data plugged into Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.045291 R Square 0.002051 Adjusted R Square -0.02421 Standard Error 2.805821 Observations 40 ANOVA df SS MS F Significance F Regression 1 0.614932 0.614932 0.07811
- 17. 0.781390647 Residual 38 299.1601 7.872633 Total 39 299.775 Coefficients Standard Error t Stat P-value Intercept 6.383032 0.866527 7.366223 7.9E-09 X Variable 1 -0.00833 0.029804 -0.27948 0.781391 Lower 95% Upper 95% Lower 95.0% Upper 95.0% 4.628839423
- 18. 8.137223891 4.628839423 8.137223891 -0.068664042 0.05200485 -0.068664042 0.05200485 Based on what the regression statistics show; the correlation between the two variables is barely present. Based on inspection, the correlation coefficient of 0.04528 (square root of R squared) represents no correlation. Coefficient of determination of 0.002051 represents the fact that 99.7949% variation is not explained. Further establishing this claim; stating there is not a relationship, the regression hypothesis test could be analyzed. Based on the table inserted above, the numbers for the dependent variable is reviewed its significance. T-statistic for the hypothesis test is -0.27948, showing a respective p-value of 0.781391. Based on a significance level of 0.01, 0.05, or 0.10, the null hypothesis would be rejected. Descriptive StatisticsChristophe AgbandeQNT/561 January 16, 2017 Dr. Heidi Carty SHORT TITLE OF PAPER 1 Running head: DESCRIPTIVE STATISTICS 1 Descriptive Statistics The Independent Variable is Carry -on bags with fee. The
- 19. Dependent Variable is the carry-on bags w/o fee. Variable #1 Carry-on bags with fee Normally Distributed Central Tendency: Dispersion: Number: Min/Max: Confidence Interval: (if distribution is normal) Variable #2 Carry-on bags w/o fee Not normally distributed Central Tendency: Dispersion: Number: Min/Max: Confidence Interval: (if distribution is normal) Attribute Variable Name (if applicable) . Descriptive Statistics Interpretation Numeric Variable Descriptive Statistics- 2016 Checked baggage Sales by Month Paid Unpaid
- 20. Mean 1481.667 Mean 1432.5 Standard Error 136.5949 Standard Error 142.5032 Median 1942.5 Median 2015 Mode #N/A Mode #N/A Standard Deviation 473.1787 Standard Deviation 493.6456 Sample Variance 223898.1 Sample Variance 243686 Kurtosis -1.57635 Kurtosis -1.69217 Skewness 0.399261 Skewness 0.345375 Range 1345 Range
- 21. 1365 Minimum 1606 Minimum 1659 Maximum 2951 Maximum 3024 Sum 25880 Sum 26770 Count 12 Count 12 Confidence Interval(95.0%) 267.7211 Confidence Interval(95.0%) 279.3012 Numeric Variable Descriptive Statistics- 2016 Checked baggage Sales by Month Paid Unpaid Mean 1555.75 Mean 1623.667 Standard Error
- 22. 151.7854 Standard Error 81.52899 Median 1671 Median 1724 Mode #N/A Mode #N/A Standard Deviation 525.7999 Standard Deviation 282.4247 Sample Variance 276465.5 Sample Variance 79763.72 Kurtosis 4.981176 Kurtosis 2.340538 Skewness -2.14873 Skewness -1.5396 Range 1866 Range 989 Minimum 0 Minimum 933 Maximum
- 23. 1866 Maximum 1922 Sum 17190 Sum 19484 Count 12 Count 12 Confidence Interval(95.0%) 297.4938 Confidence Interval(95.0%) 159.7939 Scatter Plot- 2015 carry ons w paid Checked Baggage Sales by Month Scatter Plot- 2016 carry-ons w paid Checked baggage Sales by Month DESCRIPTIVE STATISTICS 2 Appendix A Raw data used in the analysis carry ons w paid Checked baggage Sales by Month Company
- 25. 3024 1939 2091 1661 2728 2753 2720 26770 2157 2231 carry one w/o paid baggageSales by Month Company Jan Feb March April May
- 27. 1726 1778 19484 1433 1624 Appendix B Charts and Tables Appendix C Descriptive Statistics 2015 Jan Feb March April May June July August Sept Oct Nov Dec 906 1246 1692 1388 1866 1546 1680 1848 1672 1670 1676 0 2016 Jan Feb March April May June July August Sept Oct Nov Dec 933.18000000000018 1283.3799999999999 1742.76 1429.6399999999999 1921.98 1592.3799999999999 1730.4 1903.44 1722.1599999999999 1720.1 1726.28 1778.0683999999999 2015 Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 1628 1812 1790 1825 2501 2951 1870 2015 1606 2622 2646 2614 Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 2016 Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 1659 1853 1830 1916 2596 3024 1939 2091 1661 2728 2753 2720 2015 Jan Feb March April May June July August Sept Oct Nov Dec 906 1246 1692 1388 1866 1546 1680 1848 1672 1670 1676 0 2016 Jan Feb March April May June July August
- 28. Sept Oct Nov Dec 933.18000000000018 1283.3799999999999 1742.76 1429.6399999999999 1921.98 1592.3799999999999 1730.4 1903.44 1722.1599999999999 1720.1 1726.28 1778.0683999999999 2015 Jan Feb March April May June July August Sept Oct Nov Dec 1628 1812 1790 1825 2501 2951 1870 2015 1606 2622 2646 2614 2016 Jan Feb March April May June July August Sept Oct Nov Dec 1659 1853 1830 1916 2596 3024 1939 2091 1661 2728 2753 2720 SHORT TITLE OF PAPER 1 Running head: DESCRIPTIVE STATISTICS 1Descriptive StatisticsLee DeVaughnQNT/561 January 16, 2017 Dr. Heidi CartyDescriptive Statistics Weight Distribution: State if not normally distributed Central Tendency: Mean = 149 Lbs. Dispersion: Standard Deviation = 30 Lbs Number: 100 Min/Max: 99 pounds and 234 Lbs. Confidence Interval: 144 to 155 Lbs. Age Distribution: State if not normally distributed Central Tendency: Median = 36 years Dispersion: Interquartile Range = 20.5 years / 2 = ± 10 years Count: 100 Min/Max: 18 years and 74 years Confidence Interval: Not applicable (data is not normally
- 29. distributed) See the histogram in Appendix A, and descriptive statistics in Appendix B. A scatter plot is in Appendix C. Descriptive Statistics Interpretation Numeric Variable Weight Body Weight One hundred subjects were randomly selected. Their body weight was observed between 99 and 234 pounds. Their average weight was 149 pounds, with a variation of plus or minus 30 pounds. One half or more were above 149 pounds. There is 95% confidence that the population body weight average is between 144 and 155 pounds. Numeric Variable Age Age The data was significantly skewed. One hundred subjects were randomly selected. Their ages were between 18 and 74 years, with a variation of plus or minus 10 years. One half or more subjects were 36 years of age or older. The middle half of the subjects’ ages fell between 27 and 47 years. The most frequent age was 36 years. Appendix A Age and Weight Appendix B Charts and Tables
- 30. Appendix C Descriptive Statistics Descriptive Statistics: Weight, Age Variable Percent Mean StDev Min Q1 Median Q3 Max Weigh 100 168.77 16.01 99 151.1 172 180 234 Age 100 17.82 2.40 18 16 17 20 74 N for Variable Range IQR Mode Mode Weight 151.1 39.2 NA NA Age 16 25.5 2 18 CI for Two Variances: Price, Distance Method Null hypothesis σ (Weight) / σ (Age) = 1 Alternative hypothesis σ(Weight ) / σ(Age) ≠ 1 Significance level α = F method was used. Statistics 95% CI for Variable N StDev Variance Weight 155 16.01 1011.1 Age 30 2.40 33.3
- 31. Ratio of standard deviations = Ratio of variances = _1546107177.xls Chart1151617181920212215161717.81818181822.41716204 Weigth(lbs) 170 150 151 152 151.5 172 175 175 180 180 200 168.7727272727 16.01 172 151.5 180 28.5 Sheet1TimeAgeWeigth(lbs)015170116150217151318152419151 .55201726211757221758151809161801017200mean17.8181818 182168.7727272727SD2.416.01median17172Q116151.5Q32018 0interquartile428.5TimeAgeWeigth(lbs)01517011615021715131 8152419151.55201726211757221758151809161801017200mean 17.8181818182168.7727272727SD2.416.01median17172Q11615 1.5Q320180interquartile428.5 Sheet1 Age Weigth(lbs) Age and Weight Weigth(lbs) _1546107123.xls
- 33. Age and Weight Weigth(lbs) Sheet1Time AgeWeigth(lbs)015170116150217151318152419151.552017262 11757221758151809161801017200mean17.8181818182168.772 7272727SD2.416.01median17172Q116151.5Q320180interquarti le428.5Time AgeWeigth(lbs)015170116150217151318152419151.552017262 11757221758151809161801017200mean17.8181818182168.772 7272727SD2.416.01median17172Q116151.5Q320180interquarti le428.5 Age and Weight Age 0 1 2 3 4 5 6 7 8 9 10 15 16 17 18 19 20 21 22 15 16 17 Weigth(lbs) 0 1 2 3 4 5 6 7 8 9 10 170 150 151 152 151.5 172 175 175 180 180 200 Weigth(lbs) 15 16 17 18 19 20 21 22 15 16 17 17.818181818181817 2.4 17 16 20 4 170 150 151 152 151.5 172 175 175 180 180 200 168.77272727272728 16.010000000000002 172 151.5 180 28.5 Sheet2 Sheet3 Sheet4
- 34. Sheet11149.92$443KeyTravel Code Boundries for check cost 1149.91$650Checked bages1051149.93$650Carry on bages 21101135.93$4432251140.94$600Travel times 1-2 times a week 13351148.94$4433-7 times a week 24451144.94$450once a month35551149.94$735Twice a month 46601149.92$4437702220.91$4008802221.91$4189902235.92$ 335101002244.91$6182240.91$4202230.94$3502233.91$45522 21.91$4432220.92$6502220.93$505Business class traveler Checked/Carry onWeight of bagTimes traveled Cost Checked BagesCarry on$443$400$650$418$650$335$443$618$600$420$443$350$45 0$455$735$443$443$650$505t-Test: Two-Sample Assuming Unequal Variances443400Mean551.75466Variance14435.3571428571117 71Observations89Hypothesized Mean Difference0df14t Stat1.53706703P(T<=t) one-tail0.0732830222t Critical one- tail1.7613101358P(T<=t) two-tail0.1465660444t Critical two- tail2.1447866879If the t stat is larger than the critical two tail vaule we will contenue to charge for checked bags Column1Confidence Level(95.0%)90.7712019758Column1Confidence Level(95.0%)74.6810378317 Sampling and Data Collection Plan Lee DeVaughn QNT/561 January 9, 2016 Dr. Heidi Carty Running head: SAMPLING AND DATA COLLECTION PLAN 1
- 35. SAMPLING AND DATA COLLECTION PLAN 2 Sampling and Data Collection Plan Dyeus Airlines, founded in 2013, is an international airlines serving most major cities all over the world. The airlines has decided to waive all excess luggage fess for their customers, they have decided to do this to compete with the competition. From the survey Dyeus Airlines is wanting to learn the volume of flyers, based on a lower luggage cost or no luggage fee at all. The target population used will be the business traveler, the reason for using this population is because they travel the most and the change will have a bigger impact on this traveler. The method of sampling used will be Stratified Random Sampling, the decision to use this method is based on the fact that the business domestic and international traveler will be represented in a smaller group size. Other information to be gain flight cost in comparison to all other airlines. To test the validity of the questionnaire, Dyeus Airlines will use a small sample of customers (100). From this sample Dyeus Airlines can compute the standard deviation of the number of business travelers that travel frequently and how many pieces of luggage they carry on or check. The reliability can be compared to travel manifest of business travelers. Collected Data The data will be physically collected by telephone solicitation, other means of collecting data will be asking customers to take a short survey after they have booked a flight. By doing this it will give Dyeus Airlines a wider range of customers to reach with their survey. Six survey question have been created to collect the information needed to see if having no luggage fees are a sound business decision. The information collected will be
- 36. stored in Dyeus Airlines data base at the corporate offices. Only a few key people will have access to the information, which will be pass word protected. Survey Questions · How long have you used our airlines? Less than 6 months 1 year to less than 3 years 3 years to less than 5 years 5 years or more · How frequently do you fly with this airline domestically? Every day Every week 2-3 times a week Once a week 2-3 times a month 4-6 times a month Once or twice a year · How frequently do you fly with this airline internationally? Every day Every week 2-3 times a week Once a week 2-3 times a month 4-6 times a month Once or twice a year · How many pieces of luggage do you carry with you? 1 carry on 2 carry on 1 carry on 1 checked bag 1 carry on 2 checked bags · How would you rate your overall satisfaction with us? Very satisfied Somewhat satisfied Neutral Somewhat dissatisfied Very dissatisfied
- 37. · How likely is it that you would recommend us to a friend/collogue? Very likely Somewhat likely Neutral Somewhat unlikely Very unlikely Inferential Statistics and findings Lee DeVaughn QNT/561 January 23, 2017 Dr. Heidi Carty Running head: INFERENTIAL STATISTICS AND FINDINGS 1 INFERENTIAL STATISTICS AND FINDINGS
- 38. 4 Inferential Statistics and findings The hypothesis test will tell Dyeus Airlines if overall flight cost in comparison to all other airlines, compared to overall cost of passengers with less baggage fees, and having the potential of fewer flight delays. In this hypothesis test Dyeus Airlines will test business class traveler with one checked bag at a rate of $25, and a business class traveler with two carryon bags. The dependent variable would be flight cost with checked bags, and the independent variable would be cost with a carryon bag. The hypothesis test used in this research is t-test two-sample assuming unequal variances, this test along with the research questions and the two variables, will give Dyeus Airlines the information needed to consider if they should discontinue charging for checked bags. Research questions used are: · How many times a week do business class travelers travel? · How many bags do they carry on? The method of hypothesis testing used is t-test two sample assuming unequal variances. This will help Dyeus Airlines in their decision on whether or not to continue with charging for checked bags. Dyeus Airline will have to consider fuel cost, time loading and unloading passengers from the plane, and losing a portion of transportation fees that would be received from the federal excise tax. Descriptive Statistics: Checked bags/ Carryon Variable Percent Mean StDev Min Q1
- 39. Median Q3 Max Check bag 551.75 90.77 16.01 99 151.1 172 180 234 Carryon 466 74.68 2.40 18 16 17 20 74 N for Variable Range IQR Mode Mode Check bag 151.1 39.2 NA NA Carry on 16 25.5 2 18 CI for Two Variances: Price, Distance Method Null hypothesis σ (Weight) / σ (Age) = 1 Alternative hypothesis σ(Weight ) / σ(Age) ≠ 1 Significance level α = F method was used. Statistics 95% CI for Variable N StDev Variance Check bag 155 16.01 14435.36 Carryon 30 2.40 11771 Ratio of standard deviations = Ratio of variances = t-Test: Two-Sample Assuming Unequal Variances
- 41. Observations 8 9 Hypothesized Mean Difference 0 df 14 t Stat 1.537067
- 42. P(T<=t) one-tail 0.073283 t Critical one-tail 1.76131 P(T<=t) two-tail 0.146566 t Critical two-tail 2.144787
- 43. If the t stat is larger than the critical two tail value we will continue to charge for checked bags, as shown in the test t stat is not larger from the t critical two-tail. In conclusion Dyeus Airlines should continue to charge for checked bags. SHORT TITLE OF PAPER 1 Running head: DESCRIPTIVE STATISTICS 1Descriptive Statistics Andrew Rice QNT/561 1/16/2017 Dr. Heidi Carty Descriptive Statistics Interpretation By not charging for luggage, more passengers would check their luggage and allow hired baggage handlers to handle their bags. This will save overall time and allow for a more precise schedule, and in return save money for the airline. The total cost of flight delays is $585,252.23 per hour on average to a large airline like Dyeus. (George 2012) With Airlines running an average of 17 minutes behind schedule per day according to the FAA. (Garrick 2007) Our goal with the sampling was to identify 30 times with a few of our competitors and determine how much money could be saved based on the 30 samples. After taking 30 samples, we have the data, as well as the descriptive statistics that goes with the data.
- 44. Flight Delay Times in Hours Total Cost 0.25 146313.1 0.36 210690.8 1.654 968007.2 0.012 7023.0 0.0365 21361.7 0.41 239953.4 0.023 13460.8 0.23 134608.0 0.21 122903.0
- 46. 1.63 953961.1 0.26 152165.6 1.95 1141241.8 0.56 327741.2 2.13 1246587.2 1.56 912993.5 0.23 134608.0 0.65 380413.9 2 1170504.5 0.15 87787.8 0.56 327741.2 0.8954 524034.8 Descriptive Analysis of Total Cost Mean 362581.3141 Standard Error 72522.21979 Median 149239.3187
- 47. Mode 134608.0129 Standard Deviation 397220.557 Sample Variance 1.57784E+11 Kurtosis -0.093977551 Skewness 1.175434673 Range 1244831.493 Minimum 1755.75669 Maximum 1246587.25 Sum 10877439.42 Count 30 Largest(1) 1246587.25 Smallest(1) 1755.75669 Confidence Level(95.0%) 148324.5936 The second item that our sample wanted to look at was the total amount of bags on a specific plane and how long it took to unload the plane. Going back to the first variable, it was determined that quickly expediting the loading and unloading of the airplane; there would be a saving of funds that went along with that.
- 48. We wanted to see if a baggage fee made a difference in the time it took to unload and load each plane. It was determined that baggage fee is charging airlines take an average of 6.9 seconds per bag. Our second survey looked at the how long it took to unload each plane using the 6.9-second benchmark. By lowering the total time it takes to unload the plane, we will, in turn, be able to save a lot of money. The first set of data showed the cost of delays with airlines. The second set of data is a two piece showing first the amount of bags and time to unload for the airlines that do charge for a baggage fee. The second part of the data will look at what it took Dyeus airlines to unload ten planes with their no luggage fee and having the baggage handlers expedite the baggage versus the passengers. Flight # Bags Time for Unload 1 652 4498.8 2 486 3353.4 3 695 4795.5 4 589 4064.1 5 745 5140.5 6 658
- 49. 4540.2 7 523 3608.7 8 852 5878.8 9 954 6582.6 10 563 3884.7 Competitors Mean 4634.73 Standard Error 320.3067731 Median 4519.5 Mode #N/A Standard Deviation 1012.898953 Sample Variance 1025964.289 Kurtosis 0.032445637 Skewness 0.755747003 Range 3229.2 Minimum
- 50. 3353.4 Maximum 6582.6 Sum 46347.3 Count 10 Largest(1) 6582.6 Smallest(1) 3353.4 Confidence Level(95.0%) 724.5842611 Dyeus Airline Numbers for baggage time were recorded using our airline's baggage counts. Our data shows that our average bag took 4.2 seconds to unload from our planes. The average we came out with was lower than the time of the baggage fee airlines. The second set of data looks at the numbers from a 4.2 second standard versus the first 6.9 seconds that it took our competitors. Flight # Bags Time for Unload 1 652 2738.4 2 486 2041.2 3 695 2919 4 589 2473.8
- 51. 5 745 3129 6 658 2763.6 7 523 2196.6 8 852 3578.4 9 954 4006.8 10 563 2364.6 Dyeus Airlines Mean 2821.14 Standard Error 194.9693402 Median 2751 Mode #N/A Standard Deviation 616.5471888 Sample Variance 380130.436 Kurtosis 0.032445637
- 52. Skewness 0.755747003 Range 1965.6 Minimum 2041.2 Maximum 4006.8 Sum 28211.4 Count 10 Largest(1) 4006.8 Smallest(1) 2041.2 Confidence Level(95.0%) 441.0512894 In this report, I have been able to look at a few variables and analyze the descriptive statistics involved in our business decision to not charge customers for excess baggage. Our business has a two variable idea of the loading and unloading of a plane will take a shorter time, and in turn saving Dyeus Airlines money towards off schedule expenses. The cause of fewer delays will be majority of the baggage being handled by professional baggage handlers. The effect of fewer delays will be substantial savings and in turn charging our customers less overhead. With lower prices, Dyeus Airlines will be able to get more customers and be able to corner some of the markets with our low prices. References Garrick Blalock, Vrinda Kadiyali, Daniel H. Simon, The Impact
- 53. of Post-9/11 Airport Security Measures on the Demand for Air Travel, The Journal of Law and Economics, 2007, 50, 4, 731 George W. Douglas and James C. Miller III, Quality Competition, Industry Equilibrium, and Efficiency in the Price- Constrained Airline Market The American Economic Review, Vol. 64, No. 4 (Sep., 2012), pp. 657-669 Ying Wang, Francis Kofi Andoh-Baidoo, Jun Sun, Security investment in aviation industry: a longitudinal analysis, Industrial Management & Data Systems, 2014, 114, 2, 276 Business Research Project Christopher Abgande, Lee DeVaughan, Sheri LeBeau, Qiana Reynolds, Andrew Rice QNT/561 12/19/16 Dr. Heidi Carty Running head: BUSINESS RESEARCH PROJECT 1 BUSINESS RESEARCH PROJECT 2 Business Research Project Dyeus Airlines, an international airline, was founded in 2013. Dyeus Airlines is facing challenging competition from other,
- 54. larger airlines around the world. To compete with other airline companies, Dyeus has decided to pilot a system where the airline waives all excess luggage fees. Most airlines charge a fee for excess baggage, as well as overweight luggage, and it will be the strategy of Dyeus Airlines to waive the first two pieces of luggage from most additional fees. Due to weight restrictions of the Federal Aviation Administration, any bag over the weight of 50 pounds will be subject to a $50 fee for special handling guidelines. Hypothesis-The Hypothesisis “Boarding times will be faster when the first two checked bags are free.” The Null Hypothesis, then, is “There is no difference in boarding times when the first two checked bags are free.” The Independent Variable is whether or not the first two checked bags are free. The Dependent Variable is the flight boarding times. Dyeus Airlines independent variable is quite simple. This variable will stand alone and is not affected by any other variable. Dyeus Airlines will not charge extra baggage fees moving forward. By not charging for two bags per ticket, it is Dyeus Airlines’ belief that the boarding and unloading of the plane will be quicker, which will mean more precise timing in connecting flights. By pushing the luggage away from overhead storage, they can get the passengers in and out of the plane faster. Literature Review- “Passengers, airlines, airport management and industry experts all consider flight delays as one of the most important measures of service quality.” (Bubalo & Gaggero, 2015) The article investigates whether low-cost carriers can increase their service quality by improving efficiency. Flight delays are one of the most expensive costs that an airline company may ever have to deal with. In the year 2010, researchers from the University of California reported that domestic flight delays within the US alone between the year 2007 and 2010 cost an approximate thirty-three billion dollars (Guy, 2017). To mitigate these unexpected costs, airline
- 55. companies increase flight costs to have the customers pay for the costs related to the flight delay. One such strategy would be the proposed waiver on customer bags. Such a waiver would reduce the amount of time spent in loading and offloading luggage on the planes, making it easier and faster for customers to settle and reducing delays (Guy, 2017). Increased fees discourages many customers since they have to pay a lot of money for services they could access at lower prices (de Wit & Zuidberg, 2012). Companies that reduce luggage fees may end up enjoying the advantage for a long time. (Barrett, 2004). De-bundling fees has had an enormous impact on the U.S. airline industry, with multiple ancillary fees. One paper predicts that “many network carriers will eliminate ancillary fees, particularly as they begin to recognize how these fees can impact other system performance objectives such as minimizing the number of misconnecting passengers.” (Garrow, Hotle, and Mumbower, 2012) Another study of de-bundling “predicts that an airline’s fares will fall when it introduces a bag-fee, but that the full-trip price (the bag fee plus the new fare) could either rise or fall.” (Brueckner, Lee, Picard, & Singer, 2015) Many airlines charge for extra luggage for two reasons. One is fuel cost and the other is a 7.5% excise tax on tickets. According to, “The real reason airlines charge checked fees…And it’s not what you think,” the 7.5% federal excise tax on domestic tickets applies to airfare and not to ancillary services. “As long as airlines are able to unbundle, they get a portion of the transportation cost out from under that tax.” (Leff, 2015) The airline may be losing revenue when not charging extra baggage fees because they will not be receiving a portion of the transportation fee. When there are charges for the amount of bags that a customer can bring, customers will generally attempt to stuff one bag in order to avoid the excess baggage fees. By looking only for the weight of the bags, we can determine the overall weight of the plane, and adjust accordingly. In 2009, the airline industry saw its worst performing year in recent memory. (Sumit, 2009) The
- 56. cause of poor revenue was due in part to the economic recession that the world felt. In order to make up for lost revenue, various airlines started to charge extra fees for baggage fees. According to the USA Today article, several airlines charge up to $25 for the first bag and $35 for any additional bag. (Buleen, 2014) Fuel hedging is when a company that uses large amounts of fuel or crude oil can purchase the oil at an agreed upon price, and will pay that price throughout the year. Many airlines choose not to utilize fuel hedging, and just pass the extra cost onto the customers. (Carter, 2012) In 2008, airlines de-bundled “free” baggage fees. In the article, “The Market Structure Dynamics Created by Debundling of Airline Bag Fees,” “the ULCCs operating in the United States domestic airline routes, have implemented in their pricing structure a higher carry-on baggage fee than their checked- baggage fees.” (Schumann & Singh, 2014) In 2013, the US Department of Transportation calculated that U.S. carriers had a revenue of 3.35 billion in checked bag fees. (Scotti & Dresner, 2015) Some airline carriers have elected not to charge for checked bags. For example, Southwest does not charge a fee for the first two bags, and Jet Blue allows one checked bag with no charge. When customers are charged extra for checking bags, they generally carry-on bags, especially for a short trip. Checked bags slow airline turnaround time, since it takes longer for the bags to be processed, moved to another plane for connecting flights, or sent to baggage claim. “Although passenger traffic is still the major source of revenue for combination carriers, air cargo carriage has become an increasingly important revenue source for these carriers, with some deriving 40% of their revenue from cargo operations.” (Kasilingam 1996; Zhu, Ludema, and van der Heijden 2000; Zhang and Zhang 2002; Billings, Diener, and Yuen 2003; LaDue 2004). “Cargo delivery for these carriers is therefore heavily influenced by passenger operations, such as flight scheduling,
- 57. routing, and the amount of baggage that each passenger can bring” (Kasilingam 1996; Zhu, Ludema, and van der Heijden 2000; Zhang and Zhang 2002; Billings, Diener, and Yuen 2003; LaDue 2004). The theory behind “Pay as you weigh” is that passengers are charged based on the passenger’s total baggage and body weight. “Most airlines worldwide have been only marginally profitable in recent decades despite continued and rapid growth in demand for air travel” (Duganis, 2006 and 2010; Janofsky, 2008; Vasigh et al , 2008). According to the Economist, “It is a fact that an aircraft of a given make and model has a strict maximum carrying capacity. There are numerous examples about the critical importance of weight and how an airline attempts to reduce weight in a flight. With fuel accounting for a large share of costs for all airlines and the price of which has risen substantially over the last few years, reducing weight can significantly contribute to improving profitability.” (The Economist , 2011) References Barrett, S. D. (2004). How do the demands for airport services differ between full-service carriers and low-cost carriers? Journal of Air Transport Management, 10(1), 33-39. Bhatta, B. P. (2013). Pay-as-you-weigh pricing of an air ticket: Economics and major issues for discussions and investigations. Journal of Revenue and Pricing Management, 12(2), 103-119. doi:dx.doi.org/10.1057/rpm.2012.47 Bhatta, B. P., Ghimire, H. L., & Nesse, J. G. (2014). Pay-as- you-weigh pricing of an air ticket: Message of media and public about the concept from a public policy perspective. Journal of Revenue and Pricing Management, 13(4), 291-308. doi:dx.doi.org/10.1057/rpm.2014.12 Brueckner, J. K., Lee, D. N., Picard, P. M., & Singer, E. (2015).
- 58. Product unbundling in the travel industry: the economics of airline bag fees. Journal of Economics and Management Strategy, 24(3), 457-484. Retrieved from http://search.ebscohost.com/contentproxy.phoenix.edu/login.asp x?direct=true&db=eoh&AN=1535706&site=ehost-live doi:10.1111/jems.12106 Bubalo, B., & Gaggero, A. A. (2015). Low-cost carrier competition and airline service quality in Europe. Transport Policy, 43, 23-31. Retrieved from http://www.elsevier.com/locate/tranpol Buleon, C. (2014, Spring). Checked Bags Around the Airline Business. Retrieved from http://traveltips.usatoday.com/southwest-airlines-checked- baggage-rules-63778.html Carter, D., Roger, D., & Simkins, B. (2012, September). Does Fuel Hedging Make Economic Sense? The Case of the US Airline Industry. Retrieved from http:/dx.doi.org/10.2139/ssrn.325402 de Wit, J. G., & Zuidberg, J. (2012). The growth limits of the low cost carrier model. Journal of Air Transport Management, 21, 17-23. Garrow, L. A., Hotle, S., & Mumbower, S. (2012). Assessment of product debundling trends in the U.S. airline industry: Customer service and public policy implications. Elsevier Transportation Research Part A, 255-268. doi:10.1016/j.tra.2011.09.009 Guy, A. (2017). Flight delays cost $32.9 billion, passengers foot half the bill. Berkeley News. Retrieved 6 January 2017, from http://news.berkeley.edu/2010/10/18/flight_delays/ 8/01409171011055834 Leff, G. (2015, April 21). The real reason airlines charge checked bags fees... and it's not what you think. Retrieved from http://http://viewfromthewing.boardingaa.com/2015/04/21/the- real-reason-airlines-charge-checked-bag-fees-and-its-not-what- you-think/ Schumann, H., & Singh, H. (2014, September). The Market
- 59. Structure Dynamics Created by De-Bundling of Airline Bag Fees. Journal of Economics and Economic Education, 15(3), 186-197. Scotti, D., & Dresner, M. (2015, October). The Impact of Baggage on Passenger Demand on US Air Routes. Transport Policy, 43(1), 4-10. doi:10.1016/j.tranpol.2015.05.017 Sumit, A., Souphala, C., Neale, M., & Johannes, S. (2014, June). Benefit-Cost Analysis Financial Regulation. Journal of Legal Studies, 43(2), 239-252. Wong, W. H., Van Hui, Y., & Leung, L. C. (2009). Optimal baggage-limit policy: Airline passenger and cargo allocation. Transportation Science, 43(3), 355-369. Retrieved from http://search.proquest.com/docview/203522697?accountid=3581 2 Zane, C., & Reyes, P. (2014). Airlines' plight: Where has all the luggage gone? Management Research Review, 33(7), 767- 782. doi:dx.doi.org/10.110