There are about 1816 different species of birds, 3500 species of orchids and 3532 species of butterflies that can be seen in the Amazon Rainforest in Peru.
If you read my previous blog you’ll see this month I’ll be travelling half way across the world to meet a special person in Peru for a month. Of course. such a travel requires adequate planning and sufficient resources to make the experience as enjoyable as possible.
If I have missed anything, or you’ll like to add other items the list, please comment below 🙂
Without further a-do, this is an approximation of my check-list:
First Aid :
I will be taking a first aid travel kit which includes: (Fairly affordable on Amazon/eBay)
I will be travelling to Peru in July. If you are a traveler, or are planning on travelling, these quotes will be of inspiration.
50. Kilometers are shorter than miles. Save gas, take your next trip in kilometers.” – George Carlin
49. “Every perfect traveler always creates the country where he travels.” – Nikos Kazantzakis
48. “Our Nature lies in movement; complete calm is death.” – Blaise Pascal
47. “It is a strange thing to come home. While yet on the journey, you cannot at all realize how strange it will be.” – Selma LagerlÃ¶f
46. “Remember that happiness is a way of travel – not a destination.” – Roy M. Goodman
45. “Clay lies still, but blood’s a rover / Breath’s aware that will not keep. / Up, lad: when the journey’s over there’ll be time enough to sleep.” – A. E. Housman
44. “As the traveler who has once been from home is wiser than he who has never left his own doorstep, so a knowledge of one other culture should sharpen our ability to scrutinize more steadily, to appreciate more lovingly, our own.” – Margaret Mead
43. “Too often. . .I would hear men boast of the miles covered that day, rarely of what they had seen.” – Louis L’Amour
42. “Stop worrying about the potholes in the road and celebrate the journey.” –Fitzhugh Mullan
41. “One main factor in the upward trend of animal life has been the power of wandering.” – Alfred North Whitehead
40. “The open road is a beckoning, a strangeness, a place where a man can lose himself.” – William Least Heat Moon
39. “Travel only with thy equals or thy betters; if there are none, travel alone.” –The Dhammapada
38. “Our deeds still travel with us from afar, and what we have been makes us what we are.” – George Eliot
36. “An involuntary return to the point of departure is, without doubt, the most disturbing of all journeys.” – Iain Sinclair
35. “Traveling is like flirting with life. It’s like saying, ‘I would stay and love you, but I have to go; this is my station.’” – Lisa St. Aubin de Teran
34. “Once in a while it really hits people that they don’t have to experience the world in the way they have been told to.” – Alan Keightley
33. “Half the fun of the travel is the aesthetic of lostness.” – Ray Bradbury
32. “Bizarre travel plans are dancing lessons from God.” – Kurt Vonnegut
31. “We wander for distraction, but we travel for fulfillment.” – Hilaire Belloc
30. “I haven’t been everywhere, but it’s on my list.” – Susan Sontag
29. “I should like to spend the whole of my life in traveling abroad, if I could anywhere borrow another life to spend afterwards at home.” – William Hazlitt
27. “A child on a farm sees a plane fly overhead and dreams of a faraway place. A traveler on the plane sees the farmhouse… and thinks of home.” – Carl Burns.
28. “I love to travel, but hate to arrive.” – Albert Einstein
26. “Don’t tell me how educated you are, tell me how much you traveled.” –Mohammed
25. “One always begins to forgive a place as soon as it’s left behind.” – Charles Dickens
24. “When one realizes that his life is worthless he either commits suicide or travels.” – Edward Dahlberg
23. “Without new experiences, something inside of us sleeps. The sleeper must awaken.” – Frank Herbert
22. “Arriving at each new city, the traveler finds again a past of his that he did now know he had: the foreignness of what you no longer are or no longer possess lies in wait for you in foreign, unpossessed places.” – Italo Calvino
21. “He who has seen one cathedral ten times has seen something; he who has seen ten cathedrals once has seen but little; and he who has spent half an hour in each of a hundred cathedrals has seen nothing at all.” – Sinclair Lewis, onsightseeing.
20. “A journey of a thousand miles begins with a cash advance.” – Bumper sticker
19. “Travel at its truest is thus an ironic experience, and the best travelers… seem to be those able to hold two or three inconsistent ideas in their minds at the same time, or able to regard themselves as at once serious persons and clowns.” – Paul Fussell
18. “Most of my treasured memories of travel are recollections of sitting.” –Robert Thomas Allen
17. “I am not the same having seen the moon shine on the other side of the world.” – Mary Anne Radmacher Hershey
16. “Only by going alone in silence, without baggage, can one truly get into the heart of the wilderness. All other travel is mere dust and hotels and baggage and chatter.” – John Muir
15. “When you’re traveling, ask the traveler for advice / not someone whose lameness keeps him in one place.” – Rumi
14. “There are only two emotions in a plane: boredom and terror.” – Orson Welles
13. “To be on a quest is nothing more or less than to become an asker of questions.” – Sam Keen
12. “The traveler sees what he sees, the tourist sees what he has come to see.” –G. K. Chesterton
11. “When you are everywhere, you are nowhere / When you are somewhere, you are everywhere.” – Rumi
10. “When preparing to travel, lay out all your clothes and all your money. Then take half the clothes and twice the money.” – Susan Heller
9. “The autumn leaves are falling like rain / Although my neighbors are all barbarians / And you, you are a thousand miles away / There are always two cups at my table.” – T’ang dynasty poem
8. “It is not down in any map; true places never are.” – Herman Melville
7. “People don’t take trips – trips take people.” – John Steinbeck
6. “We are all travelers in the wilderness of this world, and the best we can find in our travels is an honest friend.” – Robert Louis Stevenson
5. “It’s a battered old suitcase and a hotel someplace and a wound that will never heal.” – Tom Waits
4. “The map is not the territory.” – Alfred Korzybski
3. “It is solved by walking.” – Algerian proverb
2. “He who would travel happily must travel light.” – Antoine de Saint Exupéry
What is this all about?
So this is the start to my travel blog on my journey and 4 weeks in Lima, Peru
The reason for such a journey? To meet a very special person who I am very fond of (enough said!)
I plan to write many updates, pictures, videos etc over the next few months on my preparation, experiences and encounters on my voyage to the western South American country.
I hope these updates to be informative, educational and even be of entertainment / interesting!
On July the 21st this year (2015), I plan to travel (solo) over 8,500 miles (13,680 km) on 3 planes from Stoke-on-Trent, England to the Peruvian capital city Lima.
My journey will begin with a 154 mile (248 km) car ride from my house in Fenton, Stoke-on-Trent to Heathrow Airport making sure I check-in at least 3-hours prior to take-off.
I will then be boarding the 19:45 Boeing 777 Jet across the Atlantic ocean to Logan International Airport in Boston. The plane is the world’s largest twinjet and has a typical seating capacity from 314 to 451 passengers! The flight will take around 7 hours 5-15 minutes. Travelling 3265 miles (5255 km). Which would make it 03:00 in England but 10:00 upon arrival (EDT = BST-5hours) in Boston. (And yes, meals are inclusive).
I will then be enduring another 9 hours 55 minutes wait before my next flight which correspondingly means I plan to visit /use every service, facility, shop and restaurant at least 20 times over. (I’m sure I’ll provide a new post during the time to tell what I get up to). (Ohh and to drink a tonne of coffee).
At 07:45 (EDT) I should be ready for the second take-off. Compared to the other flights, this one is relatively short (but not actually short by any measure): a 1258 mile (2024 km) ride on the Boeing 757 Jet, the largest single-aisle passenger aircraft, to Miami International Airport. All being well, arrival is expected to be at 11:25 (2hrs 30 min).
I’m glad of course that I only have an 1hr 30 minute wait until the next take off to J Chavez International Airport in Lima. However, there is of course the chance that any delays with the previous flight could delay my journey significantly. The last plane, a Boeing 767 Jet, will travel 2608 miles (4197 km) for a duration of 5 hours and 37 minutes. Arrival is expected at 17:32 (PET = EDT- 1 hour).
At this point, I imagine to be feeling considerable Jet lag or/and a few symptoms of confusion, dehydration, headache, irritability, nausea, sweating, coordination problems, dizziness, memory loss….. (Probably not). (You’ll know if my blog post is delayed after arrival!)
From here, I will get a 20 minute taxi from the airport to Bellavista district of the Constitutional Province of Callao in Peru, where I have been told to pay attention to rip-off/bogus taxi-drivers etc. But first, I imagine to be purchasing coffee. LOTS of it!
My past experience of flying
Hardly any! I have only been on a few group flights. I have never flown solo, and some find it quite ambitious that I choose my first to be a 3-plane connected flight. I hope many of you reading the blog can take insight into what I find. If you are also new, my future blogs could be highly beneficial as we collaboratively progress on the traveling learning curve. If you happen to be a veteran flyer / traveler, tips will be highly appreciated (to help me and readers) . If you are in a similar situation to me, I hope you find this blog insightful.
Whats to come?
I hope to update my readers the usual tips I find prior, during and after the trip. Further, I will be posting stories, pictures, videos and experiences of my trip, during and after. I’m aiming to keep future posts much shorter than this initial one, and much more illustrative!
This research paper looks into the specific factors that has had an effect on consumer’s decisions when they gave their sentiment over the quality of wine. The report gives an overview of the methods used to conduct the analysis, the results of the analysis and their interpretation. Finally, the report ends with a recommended conclusion of which factors should be considered significant for influencing consumer’s opinions over the quality of wine.
The principal objective was to determine which factors were considered to be of most importance, and also of least importance, when predicting the wine’s quality. To investigate this, thirty eight tasters were asked to give their opinions of the new wine by giving 6 different ratings after tasting the wine. Multiple linear regression analysis was used to develop a model for predicting the average wine quality rating given the rating of five other factors. These five factors were: Clarity, Aroma, Body, Flavour and Oakiness. For analysis, the ratings from the five factors were treated as the explanatory variables: the variables that are used to explain/predict the response variable; the wine quality rating. The multiple regression analysis essentially gave information determining the comparative influence of each of the 5 explanatory variables to the total variation in wine quality ratings. The findings from the analysis provided useful evidence of what factors should be considered of most/least importance to help importing decisions. The aim of this analysis was to give a parsimonious model that may have helped the importer to predict the average quality rating of wine, given the ratings of the five factors were known.
Using the statistical software package IBM SPSS Statistics 21, six variables were created:
1) Quality, which was the rating given for quality of the wine;
2) X1_Clarity, the rating given for the wine’s clarity;
3) X2_Aroma, the rating tasters gave for the Aroma (Smell) of the wine;
4) X3_Body, the rating given for body which generally refers to the sense of alcohol in the wine and the sense of feeling in the mouth;
5) X4_Flavour, the rating given for the Flavour of the wine;
6) Oakiness, rating’s given for how well they perceived the effects of oak from the wine.
Ratings for all these factors were taken from all 38 tasters. After the data was inputted, a multiple linear regression test, as opposed to a simple linear regression test which only has single explanatory variable, was performed on the software: which provided useful information that could be interpreted and reported. A regression test was made for all possible models: from a null model, which included no explanatory variables, to a full model that included all 5 explanatory variables. The results of interest from each test was the model summary table, ANOVA table and coefficients table. The model summary table gave values for, adjusted and standard error of the estimate which are measurements that show how well a model fits the data. is a measurement that shows the strength of the relationship between the response variables and the explanatory variables. Squaring gives the proportion of explained variation from the explanatory variables as is called the coefficient of determination. However, as we had multiple explanatory variables in our model (five), we were unaware which of the variables contributed most significantly to this value. Nonetheless, the SPSS multiple regression analysis also gave the coefficients table to find each explanatory’s level of significance, this is explained below. The ANOVA (Analysis of Variance) table shows how much of the variability in our explanatory variables has attributed to the variation of the response variable (quality of wine rating). It separates the total variability within a model into two parts. These are regression: the variance that can be explained by the explanatory values and residual: the variance which is not explained by the explanatory values, also known as the error part. The table also shows the sum of squares, degrees of freedom, mean square (MSR), F-Ratio and p-value. The F-Ratio indicates whether the model used provides a good overall fit for our data and shows how significantly the explanatory variables predict the response variables. The higher the ratio the more significant. The final table of interest, the coefficients table, gives values for unstandardized coefficients for each explanatory variable. Unstandardized coefficients signify how much the response variable; quality of wine rating, varies with one of the five explanatory variables when the other four explanatory variables are held constant. The first coefficient in this table represents a constant, denoted, this is the predicted rating of wine quality when all explanatory factors are held at zero. The unstandardized coefficients form a regression equation where is the y-intercept and the variable coefficients are the weights of the explanatory variables. The equation is then used to predict the response variable, in this case the rating of wine quality, from the explanatory variables: the factor ratings. The results from all tests were then used to summarise the MSR values and in two simple tables against the number of explanatory factors in the model. Further, the tables were used to create scatter graphs which helped give visual clues of the optimal number of factors that should be considered. The table was also used to find the best model suggested by backward elimination and forward selection. Using these methods, the most effective model that minimized the number of factors (explanatory variables) needed to be considered, therefore giving the wine important a bigger pool of wine’s to choose from. Simultaneously, the model also needed to have enough factors wine quality rating could be predicted (response variable) as accurately as was possible.
Multiple regression analysis was performed on all possible models (combinations of explanatory variables), using the methods above. The mean square error value (MSR) and coefficient of determination were obtained from these tests and summarized according to each model, as shown in the table below. Note: X1, X2, X3, X4, X5 explanatory variables relate to the Clarity, Aroma, Body, Flavour and Oakiness factors respectively.
|Variables Included||MSR||Variables Included||MSR|
|None||4.183459||0||X1, X2, X3||2.089265||0.541|
|X1||4.296194||0.001||X1, X2, X4||1.534382||0.663|
|X2||2.152917||0.499||X1, X2, X5||2.111765||0.536|
|X4||1.615917||0.624||X1, X3, X5||2.823412||0.38|
|X5||4.290167||0.002||X1, X4, X5||1.456294||0.68|
|X1, X2||2.213543||0.499||X2, X3, X4||1.550265||0.6659|
|X1, X3||2.900086||0.344||X2, X3, X5||1.927412||0.577|
|X1, X4||1.621286||0.633||X2, X4, X5||1.348441||0.704|
|X1, X5||4.406457||0.004||X3, X4, X5||1.527059||0.665|
|X2, X3||2.051629||0.536||X1, X2, X3, X4||1.569303||0.665|
|X2, X4||1.508257||0.659||X1, X2, X3, X5||1.921788||0.59|
|X2, X5||2.053114||0.536||X1, X3, X4, X5||1.439030||0.693|
|X3, X4||1.651229||0.627||X1, X2, X4, X5||1.337485||0.715|
|X3, X5||3.013914||0.319||X2, X3, X4, X5||1.385091||0.705|
|X4, X5||1.498743||0.661||X1, X2, X3, X4, X5||1.271824||0.721|
To spot trends between the number of explanatory variables and the mean square error value in each model, the data was represented visually using a scatter diagram as shown below. The x-axis gives the number of explanatory variables and the y-axis gives the MSR values.
The first insight found with the relationship is that, clearly, an increase in explanatory variables suggested a decrease in MSR. This plot showed that the lowest MSR value for three explanatory variables was almost the same as when four of five explanatory variables were used in a model. This indicated that we could ignore at most 2 explanatory variables without leading to a significant increase in the MSR value. Inspection of the table showed that this MSR value is 1.34844 (the lowest MSR) when variables X2, X4 and X5 were included in the model. This suggested a suitable parsimonious model (simplest plausible model with the fewest possible number of variables) might have been
A scatter diagram was also used to show the relation between the number of explanatory variables and the coefficient of determination. Again, the number of explanatory variables were plotted along the x-axis but now against the values along the y-axis.
The trend suggested an increase in the number of explanatory variables tended to led to an increase in R². Signifying that a larger proportion of the variation in wine quality ratings could be explained if we included more explanatory factors, i.e. the Clarity Rating. The plot also showed that including three, four or five variables produced an R² value at around the same value. Thus, the plot implies a suitable parsimonious model would again include three explanatory variables: which confirmed the previous suggestion. The highest R² value, with three explanatory variables was 0.704 and included the variables X2, X4 and X5, as before. The SPSS regression test for these three explanatory variables gave the tables:
|Model||R||R Square||Adjusted R Square||Std. Error of the Estimate|
|a. Predictors: (Constant), X5_Oakiness, X4_Flavour, X2_Aroma|
|Model||Sum of Squares||df||Mean Square||F||Sig.|
|a. Dependent Variable: Quality|
|b. Predictors: (Constant), X5_Oakiness, X4_Flavour, X2_Aroma|
|Model||Unstandardized Coefficients||Standardized Coefficients||t||Sig.|
|a. Dependent Variable: Quality|
For this model, the model summary gave a value of 0.839. This indicated a good level of prediction of the response variable. The R² was 0.704 which meant that the explanatory variables, X2, X4 and X5, explained 70.4% of the variability of the response variable. Interpreting this for analysis, this statistic showed that producers could have significantly influenced the quality rating of the wine by focusing on the ratings for Aroma, Flavour and Oakiness when importing. The ANOVA table above also gives the F-Statistic F(3,34)=26.93 and p<0.0005. This showed the regression model was an excellent fit of the data. Finally, the coefficients table gave the regression equation for the model. So, the general form of the equation to predict the response variable (y) was: y=6.461+0.576X2 +1.203X4 -0.6X5 This explanatory variable, which had the largest influence on wine quality rating, was therefore the flavour rating X4. This meant that a 1.203 wine quality rating increase was predicted for each time the wine’s flavour rating had increased by 1. On the other hand, a 1 rating increase in the wine’s Oakiness, according to the regression, is predicted to decrease the wine’s quality rating by 0.6.
Another approach used to select a suitable parsimonious model was the Backward Selection method. The technique initially involved including all explanatory variables in our model and then removing variables sequentially from the model, if any, that improved the model. The deletion of each explanatory variable was tested using its F-statistic and the comparison criterion of 4. If the F-statics were calculated to be less than 4, the explanatory variables were eliminated from the model and so on. This technique eliminated X3 from the full model and gave the prediction equation: y=4.972 + 1.802X1 + 0.527X2+1.267X3-0.657X5
Further, an alternative to the backward elimination technique was forward selection: the complete reversal of backward elimination. This involved starting with a regression model, which included no explanatory variables, then including sequentially variables to the model that had an F-statistic larger than the comparison criterion of 4. This technique selected just X4 (Flavour rating) to be included in the model and gave the prediction equation: y=4.941+1.572X4
The results above provided three contrasted results. The first model included 3 explanatory variables, the second 4 explanatory variables and the third only 1 explanatory variable. The second model, as suggested by backward elimination, predicts that the wine quality rating was significantly influenced by 4 of the explanatory factors (Clarity, Aroma, Flavour and Oakiness ratings) and supports that, to increase the overall wine quality rating, the wine importer should import a wine that has high ratings on Clarity, Aroma, Flavour but a lower rating on Oakiness. However, finding a wine that has all these factors can be difficult and also costly to produce. Therefore, a simpler model may be more appropriate. The third model, as suggested by forward selection, predicts that the wine quality rating was significantly influenced only by the Flavour rating. This suggests that the wine importer should, to increase wine quality ratings, only find wines that have high Flavour ratings. Conversely, this model may have been too simplistic and may not include the full model which impacts the overall wine quality rating. The first model, as suggested by inspection from the scatter plots, predicts that wine quality rating was actually significantly influenced by 3 of the explanatory factors which were Aroma, Flavour and Oakiness. This regression equation for this model shows that, in order for the wine importer to import the highest quality wine (as rated by consumers), the importer must select wine that has high ratings for Aroma and Flavour but a lower rating of Oakiness. In this case, the most important factor effect on the wine’s quality rating is the Flavour so this factor should be the most significant when considering the selection of wine. If the importer wanted to find a wine that had a lower average quality rating (due to lowering the selling the price of the for example) the opposite of these factor ratings apply.