QMB 3600 Project: Multiple Regression of Tourist Trips in US Zones

This project undertakes a multiple regression analysis to examine the factors influencing the number of tourist trips attracted to different zones in the US. The study identifies several independent variables, including the number of trips generated, households, workers, adults, jobs, shopping options, and crime rates, to ascertain their impact on tourism. Categorical variables like nature of people generated and season are also considered. The analysis reveals that variables such as number of trips generated, households, jobs in the zone, and crime rate are statistically significant, explaining 67% of the total trips. The model concludes that areas with more job opportunities, a higher number of households, and lower crime rates tend to attract more tourists. The project emphasizes the importance of statistical analysis in making informed business decisions, and the ability to predict tourist influx based on factors within a certain zone. The project also highlights the elimination of outliers to ensure the model's accuracy and discusses the application of the learned statistical methods in future business endeavors.

NUMBER OF TOURIST
TRIPS ATTRACTED TO
DIFFERENT ZONES IN US

Introduction
Tourism is a major factor in human socio-economic
engagements. Apart from the revenue they
generate, they improve learning of other people’s
way of life and also the creation of employment.
Studying the aspect of tourism is important for the
betterment of the society.

Number of tourist attracted statistics
Multiple regression model
Dependent was categorically chosen to analyze the number of tourist trips
attracted to different zone. The independent variables were chosen to
ascertain some of the reasons why tourist are attracted to some zones
compared to others. Some of the variables chosen, were the availability of
jobs, households, number of adults etc.
The researcher confident interval in this study is 95% giving us the alpha of
0.05
The prediction is that tourists are attracted to areas where there are plenty of
jobs, large number of adults who are working to offer services and to areas
where there are low crime rates.

Independent quantitative variables
• Number of trips generated: these are the number of trips generated from a certain zone due to people
moving out. It is important to compare those who move out as some are moving in
• Number of households: number of households can be used to predict the population of an areas, this is
important as this population can offer the services required by the tourists
• Workers in the zone: these are different service providers
• Adult in the zone: this is important to our study because it enable us to predict the labour force when
need be if there is influx of tourists
• Jobs in the zone: apart from just visiting a place for pleasure, some tourist visit to work, availability of
jobs hence serve well.
• Shopping: these are the different category of the goods available for shopping. People who like
shopping can be attracted to varieties of thing to shop.
• Crime rate: some tourist consider the crime rate before they can visit a place

Binary categorical variables
 Nature of people generated: these are people leaving a
certain zone to be either tourist somewhere or people are
attracted and are leaving.
 Season: this is whether the season is summer or winter
Categorical variable with at least 3 variables
Colour: these are zones with majority as Latino, Blacks,
White or Others

Number of trips
attracted
Number of
trips generated
Number of
households
Workers in
zone
Adults in
zone
Jobs in zone Shopping m2 Crime
Rate
Nature
of
people
generat
ed
Season Colour
1063 1144 946 946 1892 3032 125716 20 0 0 1
678 421 310 310 620 3807 67182 21 0 0 2
553 823 337 674 1348 3498 188545 15 0 0 2
1768 1216 904 904 1808 4703 133366 32 1 1 3
1045 1168 838 838 2514 5926 187937 23 0 0 1
1406 1104 538 1076 2152 5935 198630 14 1 1 3
1528 1521 690 1380 2070 4514 127228 17 1 0 2
744 1946 921 1842 3684 5995 86105 15 0 0 4
577 938 875 875 1750 3064 16370 23 1 1 3
718 1023 805 805 1610 3558 179430 39 0 0 2
1084 268 230 230 460 4851 142533 38 0 0 1
892 565 474 474 1422 3142 191692 12 1 1 1
514 1066 710 710 1420 3017 48221 32 1 1 3
106 483 289 289 578 5228 39930 27 1 0 2
22 998 610 610 1220 3221 10322 26 0 0 4
449 257 157 157 471 3308 116098 12 0 1 4
233 800 565 1130 1695 3837 31795 18 1 0 1
35 1295 927 927 2781 5912 11855 21 0 0 2
1463 948 704 1408 2112 5175 95402 22 1 0 1
685 865 695 695 2085 4739 89118 33 1 1 3
Original data

Number of trips
attracted
Number of trips
generated
Number of
households
Adults in
zone
Jobs in zone Shopping m2 Crime
Rate
Nature of
people
generated
Season Colour
1063 1144 946 1892 3032 125716 20 0 0 1
678 421 310 620 3807 67182 21 0 0 2
553 823 337 1348 3498 188545 15 0 0 2
1768 1216 904 1808 4703 133366 32 1 1 3
1045 1168 838 2514 5926 187937 23 0 0 1
1406 1104 538 2152 5935 198630 14 1 1 3
1528 1521 690 2070 4514 127228 17 1 0 2
744 1946 921 3684 5995 86105 15 0 0 4
577 938 875 1750 3064 16370 23 1 1 3
718 1023 805 1610 3558 179430 39 0 0 2
1084 268 230 460 4851 142533 38 0 0 1
892 565 474 1422 3142 191692 12 1 1 1
514 1066 710 1420 3017 48221 32 1 1 3
106 483 289 578 5228 39930 27 1 0 2
22 998 610 1220 3221 10322 26 0 0 4
449 257 157 471 3308 116098 12 0 1 4
233 800 565 1695 3837 31795 18 1 0 1
35 1295 927 2781 5912 11855 21 0 0 2
1463 948 704 2112 5175 95402 22 1 0 1
685 865 695 2085 4739 89118 33 1 1 3
Multicollinearity
Multicollinearity exist where two variables are correlated. From our data above, the number of worker and jobs in zone are
correlated because people who are working will be obviously engaged. We will therefore remove the variable “Worker” as it
may interfere with our analysis.
Once we remove, the multicollinearity, we remain with the following data

Outliers
Outlier is data that differ significantly from the rest. In the
above table, we can see that there are two number of trips
attracted which differ significantly from the rest. Value 22
and 35. The following table has no outliers.

Number of
trips attracted
Number of trips
generated
Number of
households
Adults in
zone
Jobs in zone Shopping m2 Crime
Rate
Nature of
people
generated
Season Colour
1063 1144 946 1892 3032 125716 20 0 0 1
678 421 310 620 3807 67182 21 0 0 2
553 823 337 1348 3498 188545 15 0 0 2
1768 1216 904 1808 4703 133366 32 1 1 3
1045 1168 838 2514 5926 187937 23 0 0 1
1406 1104 538 2152 5935 198630 14 1 1 3
1528 1521 690 2070 4514 127228 17 1 0 2
744 1946 921 3684 5995 86105 15 0 0 4
577 938 875 1750 3064 16370 23 1 1 3
718 1023 805 1610 3558 179430 39 0 0 2
1084 268 230 460 4851 142533 38 0 0 1
892 565 474 1422 3142 191692 12 1 1 1
514 1066 710 1420 3017 48221 32 1 1 3
106 483 289 578 5228 39930 27 1 0 2
449 257 157 471 3308 116098 12 0 1 4
233 800 565 1695 3837 31795 18 1 0 1
1463 948 704 2112 5175 95402 22 1 0 1
685 865 695 2085 4739 89118 33 1 1 3

R Square 0.641413
Adjusted R Square 0.238002
Standard Error 400.6401
Observations 18
ANOVA
df SS MS F Significance F
Regression 9 2296898 255210.9 1.589975 0.262415
Residual 8 1284100 160512.5
Total 17 3580998
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -530.156 728.9124 -0.72733 0.487763 -2211.03 1150.719 -2211.03 1150.719
X Variable 1 0.959304 1.017722 0.942599 0.043476 -1.38757 3.306174 -1.38757 3.306174
X Variable 2 1.079052 1.337963 0.806488 0.043274 -2.0063 4.1644 -2.0063 4.1644
X Variable 3 -0.69156 0.432391 -1.59938 0.148403 -1.68865 0.305537 -1.68865 0.305537
X Variable 4 0.264314 0.153816 1.718373 0.024051 -0.09039 0.619015 -0.09039 0.619015
X Variable 5 0.002896 0.003058 0.946962 0.37138 -0.00416 0.009948 -0.00416 0.009948
X Variable 6 -10.6024 16.75558 -0.63277 0.044545 -49.2408 28.03608 -49.2408 28.03608
X Variable 7 -18.7354 418.8831 -0.04473 0.965421 -984.682 947.2107 -984.682 947.2107
X Variable 8 407.7466 629.155 0.648086 0.035083 -1043.09 1858.581 -1043.09 1858.581
X Variable 9 -176.027 269.5488 -0.65304 0.532044 -797.607 445.5539 -797.607 445.5539
Excel data regression output

Number of
trips
attracted
Number of
trips
generated
Number of
households
Jobs in zone Crime Rate Season
1063 1144 946 3032 20 0
678 421 310 3807 21 0
553 823 337 3498 15 0
1768 1216 904 4703 32 1
1045 1168 838 5926 23 0
1406 1104 538 5935 14 1
1528 1521 690 4514 17 0
744 1946 921 5995 15 0
577 938 875 3064 23 1
718 1023 805 3558 39 0
1084 268 230 4851 38 0
892 565 474 3142 12 1
514 1066 710 3017 32 1
106 483 289 5228 27 0
449 257 157 3308 12 1
233 800 565 3837 18 0
1463 948 704 5175 22 0
685 865 695 4739 33 1
Significant variables
Here we can see that;
Number of trips generated, Number of households, Jobs in the Zone, Crime rate and People engaged have a p-value less than
0.05 which was our level of significant hence they are statistically significant.
The table below shows data with significant variables only

SUMMARY OUTPUT
Regression Statistics
Multiple R 0.519108
R Square 0.694731
Adjusted R Square 0.671305
Standard Error 466.9061
Observations 18
ANOVA
df SS MS F Significance F
Regression 5 964982.9 192996.6 0.8853 0.010134
Residual 12 2616016 218001.3
Total 17 3580998
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept -192.193 612.465 -0.3138 0.759059 -1526.64 1142.254 -1526.64 1142.254
X Variable 1 0.031349 0.601882 0.052085 0.059318 -1.28004 1.342737 -1.28004 1.342737
X Variable 2 0.564186 0.948173 0.595024 0.00288 -1.5017 2.630076 -1.5017 2.630076
X Variable 3 0.152291 0.126457 1.204296 0.025169 -0.12323 0.427817 -0.12323 0.427817
X Variable 4 -1.0636 15.38948 -0.06911 0.046039 -34.5944 32.46721 -34.5944 32.46721
X Variable 5 129.9874 238.2694 0.545548 0.595372 -389.157 649.1318 -389.157 649.1318
Excel regression output