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Submitted By rerzen

Words 1758

Pages 8

Words 1758

Pages 8

41100-81

Christian Hansen

Winter 2015

“I pledge my honor that I have not violated the Honor Code during this assignment.”

Kataras, Peter

Foltyn, Tom

Erzen, Robert

Scholl, Katie

In order to begin we first had to gain a high level understanding of the 6000 observations that we were given. We ran descriptive statistics on all of the original variables after transforming the variable Color into a dummy variable called White (White Wine=1, Red wine=0). Descriptive Statistics | | N | Minimum | Maximum | Mean | Std. Deviation | quality | 6000 | 2.5000 | 9.5000 | 5.825317 | .9206965 | fixed_acidity | 6000 | 3.8000 | 15.9000 | 7.221233 | 1.3094165 | volatile_acidity | 6000 | .0800 | 1.5800 | .340727 | .1653986 | citric_acid | 6000 | .0000 | 1.6600 | .318008 | .1455540 | residual_sugar | 6000 | .6000 | 65.8000 | 5.425650 | 4.7411670 | chlorides | 6000 | .0100 | .6100 | .056483 | .0344872 | free_sulfur_dioxide | 6000 | 1.0 | 289.0 | 30.482 | 17.7550 | total_sulfur_dioxide | 6000 | 6.0 | 440.0 | 115.576 | 56.5940 | density | 6000 | .99 | 1.04 | .9949 | .00504 | pH | 6000 | 2.74 | 4.01 | 3.2195 | .16022 | sulphates | 6000 | .2200 | 2.0000 | .532073 | .1487300 | alcohol | 6000 | 8.0000 | 14.9000 | 10.491008 | 1.1901957 | White | 6000 | 0 | 1 | .75 | .433 |

Some of our variables in the dataset have very tight ranges, for example density has a min of .99 and a max of 1.04. On the other hand, total sulfur dioxide has a range of 6 to 440 and a standard deviation of 56.6, with the max value being over 5 standard deviations from the mean.

In order to get a sense of the overall dataset, we ran a regression of all of the original variables with the dependent variable of quantity. Below are the outputs of the regression (Potential Model 1):

Potential Model 1 Summary | R Square | Adjusted R Square |…...

...WINE BY THE GLASS 150 ml CHAMPAGNE Laurent-Perrier Brut SPARKLING WINE 2005 Yarrabank Cuvee Guillaume WHITE WINE Riesling 2008 Freycinet Sauvignon Blanc 2009 Summerhouse 2007 Pascal et Nicolas Reverdy Auxerrois 2004 Mount Langi Ghiran Pinot Gris 2008 Paul Kubler Chenin Blanc 2008 Pichot Chardonnay 2008 Hoddles Creek 2007 Laurent Tribut 2009 Dominique Portet ROSE Fontaine RED WINE Pinot Noir 2008 Escarpment Grenache Shiraz Mourvedre 2006 Massena Shiraz 2006 Balnaves Merlot 2007 Irvine Cabernet Sauvignon Merlot 2007 Victory Point 2005 Chateau Saint-Christoly The Malee Root Cru Bourgeois DESSERT WINE 90ml 2007 De Bortoli 2006 Chateau Roumieu-Lacoste 2005 Grande Maison 2004 Alain Brumont 2006 Les Clos de Paulilles Noble One Botrytis Semillon Cuvee Classique Cuvee des Anges Les Larmes Celestes Riverina Sauternes, Bordeaux Monbazillac Pacherenc Banyuls, Roussillon 18 18 15 15 16 The Moonlight Run Martinborough Barossa Coonawarra Barossa Margaret River Haut Medoc 21 15 17 17 15 22 'K' Coteau de la Biche East Coast Tasmania Marlborough Sancerre, Loire Valley Grampians Alsace Vouvray, Loire Valley Yarra Valley Chablis Yarra Valley 17 16 19 12 17 16 15 22 14 Yarra Valley 19 Tours-sur-Marne 29 Terre de Maimbray 1 HALF BOTTLES 375ml Champagne Pol Roger Krug Champagne Rose Billecart-Salmon White Wine Sauvignon Blanc Semillon 2009 Fermoy Estate 2007 Cullen Sauvignon Blanc 2006 Lucien Crochet Chardonnay 2008 Tyrrell’s 2006 Mount Mary Red Wine Pinot Noir 2007 Paringa Estate 2007......

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...Regression Models Student Name Grantham University BA/520 – Quantitative Analysis Instructor Name April 6, 2013 Abstract This paper will refer to regression models and the benefits that variables provide when developing and examining such models. Also, it will discuss the reason why scatter diagrams are used and will describe the simple linear regression model and will refer to multiple regression analysis as well as the potential uses for this type of model. Regression Models Regression models are a statistical measure that attempts to determine the strength of the relationship between one dependent variable (usually denoted by Y) and a series of other changing variables (known as independent variables). Regression models provide the scientist with a powerful tool, allowing predictions about past, present, or future events to be made with information about past or present events. Inference based on such models is known as regression analysis. The main purpose of regression analysis is to predict the value of a dependent or response variable based on values of the independent or explanatory variables. According to Render, Stair, and Hanna (2011) they are two reasons for which regression analyses are used: one is to understand the relation between various variables and the second is to predict the variable's value based on the value of the other. Variables provide many advantages when creating models. One of the......

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...of the previous seasons, followed by feasible recommendations in order to boost the sales of our restaurant in the fore season. FINDINGS: The overall sales performance of each type of wine is analyzed. From the data, French red wine is the most popular wine being sold on average. From November, 2011 to March, 2012, the sales performance of French Red wine is the best. However, from April to June of the same year, sales of French red dropped, having an overall 8.48%, being overtaken by sparkling wine having an overall 11.51% of sales. From July to August, sales of French red regained and overtook that of sparkling wine, having an overall 12.01% of total sales. Due to the high demand of French red wine, this essay would mainly focus on French red wine. Also recommendation on some of the red wines would also be given. Fig 1. Comparison between the sales performance of French Red wine and the sparkling wine I. FRENCH RED WINE: High Sales Wine:Hauts de Carras From the data ,Hauts de Carras has a constantly high sales over the year: Oct-Dec,2011 | Jan-Mar, 2012 | Apr-Jun, 2012 | Jul-Aug,2012 | 3.66% (43 bottles) | 3.06%(35 bottles) | 2.13%(24 bottles) | 0.66%(4 bottles) | Description: Hauts de Carras is a medium body wine from Paulliac region. It is blended wine containing 45% Merlot, 50% Cabernet sauvignon, and 5% Cabernet Franc. It has the taste of woody nose with hints of vanilla and red fruit. Also, it has firm but not too strong tannins, which......

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...Regression Analysis: Basic Concepts Allin Cottrell∗ 1 The simple linear model Suppose we reckon that some variable of interest, y, is ‘driven by’ some other variable x. We then call y the dependent variable and x the independent variable. In addition, suppose that the relationship between y and x is basically linear, but is inexact: besides its determination by x, y has a random component, u, which we call the ‘disturbance’ or ‘error’. Let i index the observations on the data pairs (x, y). The simple linear model formalizes the ideas just stated: yi = β0 + β1 xi + ui The parameters β0 and β1 represent the y-intercept and the slope of the relationship, respectively. In order to work with this model we need to make some assumptions about the behavior of the error term. For now we’ll assume three things: E(ui ) = 0 2 2 E(ui ) = σu E(ui u j ) = 0, i = j u has a mean of zero for all i it has the same variance for all i no correlation across observations We’ll see later how to check whether these assumptions are met, and also what resources we have for dealing with a situation where they’re not met. We have just made a bunch of assumptions about what is ‘really going on’ between y and x, but we’d like to put numbers on the parameters βo and β1 . Well, suppose we’re able to gather a sample of data on x and y. The task ˆ of estimation is then to come up with coefﬁcients—numbers that we can calculate from the data, call them β0 and ˆ1 —which serve as estimates of the unknown......

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...The first one is the business and productive model implemented by the NW, which corresponds to a mass production of varietal wines, with quality consistency, obtained by large industrial enterprises with significant economies of scale. By contrast and as previously mentioned, the productive and business model implemented by the OW was characterized by a business network of cooperatives and enterprises (mostly familiar and of small and medium size). The second one is the commercial and marketing strategy implemented by the NW, leading to better consumer information of the product characteristics; i.e. wines are sold under brands with strong investments in marketing and advertising campaigns. By contrast, the commercial tradition of the OW involved an intricate system of denominations of origin, varietal and geographic areas that were difficult to understand by a novice consumer. The last potential reason is the emergence of strong governmental support in the NW during the '90s and the beginning of this century. This support, known in the specialized literature as the "National Brand Plans", had the primary aim of improving the wine export performance in the long Term. Australia was the pioneer in the development of such national scope plans in June1996, launching its celebrated plan called "Strategy 2025". Its aim was to reach an export turnover of 4.5 billion Australian dollars by the year 2025. The resounding success of the plan meant that in 2005, 20......

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...Q1: All the regressions were performed. Output can be made available if needed. See outputs for Q2 in appendix. Q2: Select the model you are going to keep for each brand and explain WHY. Report the corresponding output in an appendix attached to your report (hence, 1 output per brand) We use Adjusted R Squared to compare the Linear or Semilog Regression. R^2 is a statistic that will give some information about the goodness of fit of a model. In regression, the Adjusted R^2 coefficient of determination is a statistical measure of how well the regression line approximates the real data points. An R2 of 1 indicates that the regression line perfectly fits the data. Brand1: Linear Regression R^2 | 0.594 | SemiLog Regression R^2 | 0.563 | We use the Linear Regression Model since R-squared is higher. Brand 2: Linear Regression R^2 | 0.758 | SemiLog Regression R^2 | 0.588 | We use the Linear Regression Model since R-squared is higher Brand 3: Linear Regression R^2 | 0.352 | SemiLog Regression R^2 | 0.571 | We use the Semilog Regression Model since R-squared is higher Brand 4: Linear Regression R^2 | 0.864 | SemiLog Regression R^2 | 0.603 | We use the Linear Regression Model since R-squared is higher Q3: Here we compute the cross-price elasticity. Depending on whether we use linear or semi-log model, Linear Model Linear Model Semi-Log Model Semi-Log Model ` ...

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...relationships between the variables. The relationships can either be negative or positive. This is told by whether the graph increases or decreases. Benefits and Intrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.069642247 R Square 0.004850043 Adjusted R Square -0.00471871 Standard Error 0.893876875 Observations 106 ANOVA df SS MS F Significance F Regression 1 0.404991362 0.404991 0.50686 0.478094147 Residual 104 83.09765015 0.799016 Total 105 83.50264151 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 5.506191723 0.363736853 15.13784 4.8E-28 4.784887893 6.2274956 4.7848879 6.22749555 Benefits -0.05716561 0.080295211 -0.711943 0.47809 -0.21639402 0.1020628 -0.216394 0.10206281 Y=5.5062+-0.0572x Graph Benefits and Extrinsic Job Satisfaction Regression output from Excel SUMMARY OUTPUT Regression Statistics Multiple R 0.161906 R Square 0.026214 Adjusted R Square 0.01685 Standard Error 1.001305 Observations 106 ANOVA df SS MS F Significance F Regression 1 2.806919 2.806919 2.799606 0.097293 Residual 104 104.2717 1.002612 Total 105 107.0786 Coefficients Standard Error t Stat P-value Lower 95% Upper......

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...November 20, 2013 Wine The creation of wine dates back prior to written history, earliest known production tools found in the ancient Middle East about 6000BC. Wine has influenced royalty, religion and wars throughout the ancient world, playing an important role in each culture. Today, wine has managed to maintain its luster in society, being produced and consumed in large amounts worldwide. The production of wine has proved lucrative business for many years, and for many reasons. From grape farming to creating different varieties to pricing the bottles, there is a good amount involved in the wine production process. For the world of “winos” (people addicted to wine), there could never be enough of this sweet nectar. While the wine making process was developed about 6000BC, many improvements and innovations have taken place over the past 8000 years. Today, the production of wine is worldwide, manufacturing nearly 6.5 billion gallons per year. According to the International Wine Guild, the United States consumes 762 million gallons per year, ranking third overall in wine consumption across the world. The top three innovations in wine production include Biodynamic Viticulture, micro-oxygentation, and fuel creation from the waste. Biodynamic Viticulture involves the process of using the farm and surrounding land’s eco-system to best determine ways to get the most yields along with controlling pests. Micro-oxygentation is used to improve the flavor of a wine by......

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...STATISTICS FOR ENGINEERS (EQT 373) TUTORIAL CHAPTER 3 – INTRODUCTORY LINEAR REGRESSION 1) Given 5 observations for two variables, x and y. | 3 | 12 | 6 | 20 | 14 | | 55 | 40 | 55 | 10 | 15 | a. Develop a scatter diagram for these data. b. What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? c. Develop the estimated regression equation by computing the values and. d. Use the estimated regression equation to predict the value of y when x=10. e. Compute the coefficient of determination. Comment on the goodness of fit. f. Compute the sample correlation coefficient (r) and explain the result. 2) The Tenaga Elektik MN Company is studying the relationship between kilowatt-hours (thousands) used and the number of room in a private single-family residence. A random sample of 10 homes yielded the following. Number of rooms | Kilowatt-Hours (thousands) | 12 9 14 6 10 8 10 10 5 7 | 9 7 10 5 8 6 8 10 4 7 | a. Identify the independent and dependent variable. b. Compute the coefficient of correlation and explain. c. Compute the coefficient of determination and explain. d. Test whether there is a positive correlation between both variables. Use α=0.05. e. Determine the regression equation (used Least Square method) f. Determine the value of kilowatt-hours used if number of rooms is 11. g. Can you use the model in (f.) to predict the kilowatt-hours if number of......

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...MULTIPLE REGRESSION After completing this chapter, you should be able to: understand model building using multiple regression analysis apply multiple regression analysis to business decision-making situations analyze and interpret the computer output for a multiple regression model test the significance of the independent variables in a multiple regression model use variable transformations to model nonlinear relationships recognize potential problems in multiple regression analysis and take the steps to correct the problems. incorporate qualitative variables into the regression model by using dummy variables. Multiple Regression Assumptions The errors are normally distributed The mean of the errors is zero Errors have a constant variance The model errors are independent Model Specification Decide what you want to do and select the dependent variable Determine the potential independent variables for your model Gather sample data (observations) for all variables The Correlation Matrix Correlation between the dependent variable and selected independent variables can be found using Excel: Tools / Data Analysis… / Correlation Can check for statistical significance of correlation with a t test Example A distributor of frozen desert pies wants to evaluate factors thought to influence demand Dependent variable: Pie sales (units per......

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...of wine | Red, dry, still | Character of wine | Intense ruby colour. Intensive aroma of wild berries, eucalyptus and red cherries The palate exhibits earthy aromas with a full-bodied structure combining supple and silky tannins. This wine is soft and fruity. The grapes are picked in November by hand.Humagne Rouge wines have great aging potential and can keep for 4 to 6 years. | Food harmony 2 dishes | Roast Haunch Of Venison Without Bone And Pepper Sauce Full bodied wine is perfectly match to venison, the texture of the meet and the sauce flavour matches with the wine’s palate and will create a smooth characteristic in the mouth. Rabbit fricassee with carrots The strong flavour of this meat will be perfectly reflected by this wine, because of its intensity and aroma. Carrots work well with earthy elements of this wine | Commercial argumentation | Humagne Rouge Hospices De Salquenen Adrian Mathier 2010 will be perfect for people who want taste the wine which reveal the character of Valais region. This wine will reflect you the delicate aromas of this Swiss canton. Moreover, Humagne Rouge wine grapes is the highest quality, and one of the most interesting of the local red varieties of the Swiss Valais region. This wine , made of rare red grape, has fruity and delicate basis which match with plenty of local dishes. | Cost&sales price | Cost price: 15, 71- CHF(www.vinopedia.com/store/Kaufmann+Wine+%26+Drinks/?wineid=21400178)Sales price: 47,13- CHFThis wine......

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...Hallgarten Wines Case 4 Introduction Peter Hallgarten is grandson of Arthur Hallgarten, the establisher of Hallgarten Wines Limited. Peter currently runs the wine importer and is continuously occupied with securing his purchases of foreign wines. As a wine importer it is not hard to imagine that the investments are subject to a lot of exchange rate risk, something that has to be dealt with. In this paper the operational financial strategy of Hallgarten Wines Limited is revised and recommendations are made. Case questions Comment on Peter Hallgarten´s approach to managing the exchange risks and other international financial risks encountered by Hallgarten Wines. Since Hallgarten Wines is a wine importer and has no production of wines of itself, it is highly dependent on foreign suppliers. This explains the high exposure to exchange rate risk that Hallgarten Wines endures. Peter has dealt with this problem by diversifying away from German wines (which composed the major share of Hallgarten´s sales) into Portuguese, Israeli and English wines. Another problem that Hallgarten Wines faces is that it cannot pass down unexpected price changes onto its customers. Its customers or trade buyers, like restaurant owners, fix their prices according to their expected wholesale costs. So if Hallgarten pays more for the wines than expected, it has to make up for this difference in price by itself. Not selling this wine is not an option because goodwill needs to......

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... Wine In the story Wine the author Hayashi Mariko subtly sketches a modern Japanese career woman as a developing round character who is constantly evaluating her relationships with men. While in Quebec for a business trip Ms. Sone is being taken on a tour along with others through different areas within Quebec. They are they taken to a very exclusive and important wine cellar of Canada. “Oh, wine. Just hearing the word makes me tense.” As the reader can tell wine must not be a very strong subject to Ms. Sone. “But wine is my weak area,” she says. Knowing very little of the subject she offers to buy a bottle to take back as a souvenir of the trip. Picking one of extremely lavish quality just to splurge on the occasion Ms. Sone picks one thinking it is costing her forty-five dollars in reality ending up to have a final cost of one hundred forty-five dollars. In complete shock of the price she decides it is of too much importance to drink in a regular occasion with regular people and decides to save it for something or someone special to share it with. The wine now became of some importance to Ms. Sone due to the price tag which came with it. Like those who she mocked she guarded the bottle as it were a real life baby While thinking of those to share it with she immediately crosses out her boyfriend Kunihiko because she thought he had no appreciation for it. After saying “ Lets drink that expensive wine together to celebrate your......

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...A) Estimated regression equation – First Order: y = β0 + β1x1 + β2x2 + ε Output of 1st Model | | | | | | | | | | | | | | Regression Statistics | | | | | | Multiple R | 0.763064634 | | | | | | R Square | 0.582267636 | SSR/SST | | ̂̂̂ | | | Adjusted R Square | 0.512645575 | | | | | | Standard Error | 547.737482 | | | | | | Observations | 15 | | | | | | | | | | | | | ANOVA | | | | | | | | df | SS | MS | F | Significance F | | Regression | 2 | 5018231.543 | 2509115.772 | 8.363263464 | 0.005313599 | | Residual | 12 | 3600196.19 | 300016.3492 | | | | Total | 14 | 8618427.733 | | | | | | | | | | | | | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Intercept | -20.35201243 | 652.7453202 | -0.031179101 | 0.975639286 | -1442.561891 | 1401.857866 | Age (x1) | 13.35044655 | 7.671676501 | 1.740225432 | 0.107375657 | -3.364700634 | 30.06559374 | Hours (x2) | 243.7144645 | 63.51173661 | 3.837313819 | 0.002363965 | 105.334278 | 382.0946511 | B) equation | ŷ= -20.3520124320994 + 13.3504465516772 x̂1 + 243.714464532425 x̂2 | C) Interpretation of β β̂1 = 13.35044655, If number of hours worked (x2) held fixed, we can estimate that every one-year increase in age (x1) the mean of annual earnings will increase by 13.35044655. β̂2 = 243.7144645, If age (X1) held fixed, we can estimate that every one hour (x2) of work increase, the mean of......

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...the majority of your wine purchases fall? (per 750ml bottle) a. less than $10 b. between $10 and $20 c. between $20 and $30 d. greater than $30 NOTES: 5. The majority of the time you consume wine with: a. by itself b. with tobacco products c. with finger foods (cheese, crackers, chocolate, fruits etc.) d. with large meal (lunch, dinner) e. other NOTES: 6. Where do you usually drink wine? a. home b. restaurant c. bar/lounge d. club e. other NOTES: 7. When heading into a wine store you already know which: a. brand and type you want b. type and region you want c. region you want (Bordeaux, Napa Valley, Chile etc.) d. type you want (Merlot, Cabernet Sauvignon, Pinot Noire etc.) e. look to see what catches your attention f. ask a store clerk for suggestions NOTES: If your answer is E, please describe what catches your attention, is it the label, is it a sale, maybe the position in the store? 8. Do you have a favorite type of red wine? a. Merlot b. Bordeaux blend c. Pinot Noir d. Cabernet Sauvignon e. Syrah f. Zinfandel g. Malbec h. Sangiovese i. Cabernet Franc j. Chianti k. Other ________________ NOTES: When would you normally consume champagne? Anytime During a regular meal Special occasions Where do you normally purchase champagne? Grocery store/supermarket Liquor store Specialty wine shop In your opinion, is there a difference between sparkling wine and champagne? ......

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