...value 1.8957 we on average make mistake about 0.4624 When we calculate our coefficient 3 at value 35.9569 we on average make mistake about 2.881 E) Check precision of estimate of S and L Si Standard Error Si= | |* 100 = | | I Coefficient 1.350 Ss= * 100 = 10.543 % 12.8052 2.881 SL= * 100 = 8.012 % 35.9569 F) Interpret and comment R^2 _ K (no of independent ) 2 2 R = R - * (1- R2) N –(K+1) (no of observation) _ 3 2 R = 0.964 - * (1- 0.964) = 0.959 => 95.9 % 25-3-1 R2 = 0.964 0.964 > 0.7 is good model It means 96% of dependent variable is explained by independent variable and is good model R2 > 0.7 is good model R2 < 0.5 is bad model 0.5 < R2 < 0.7 is in average G) Test significance of L,D and of constant term H0: 1=0 if parameters 1 is equal to zero variable j is not significant in the...
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...to filter the data to get more accurate results. Graph 1 contains the data between the years 1974 and 1992. Graph 2 Contains the data between the years 1992 and 2012. Graph 1 Filtered This scatter plot shows the amount of beer sold per capita between the years 1975 and 1992. The Independent variable “Year” is graphed on the x-axis and represents the predictor. The scatter plot has a negative, non-linear relationship. It has a strong relationship, with constant scatter. There seems to be a trend of beer sales declining per capita at a rate of 1.23 litres per year. Graph 2 Filtered: This scatter plot shows the amount of beer sold per capita between the years 1992 and 2012. The Independent Variable “Year” represents the predictor and is graphed on the x-axis. The Dependent Variable “Beer Sales Per Capita” is graphed on the y-axis and represents the response. The scatter plot has a non-linear trend, with a negative association that shows as one variable gets smaller, so does the other. It has a weak relationship with constant scatter. This graph shows that there is a trend of Beer Sales Per Capita declining at a rate of 0.31 litres per year. Part 2: The independent variable is “Year” because it does not rely on any other data and the dependent variable is the population aged 20-24 as a percent because it relies on the year. The relationship shown in this scatter plot is a strong, linear correlation...
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...QMS 442- Research Article The Starbucks effect on the Toronto Housing Market Chandini Algoo 500571436 March 31, 2015 Abstract: This study is looking at the relationship between the sales price in Downtown Toronto and the locations of Starbucks. The models being used in this study is first, second and third order models. The main findings of this study conclude that there is strong correlation between the two variables. However, sales price do depend on the independent variables of size, number of rooms and parking space. Introduction: The Starbucks effect is known as a phenomenon that has changed the face of coffee in the recent decade (Vishwanath & Harding, 2000). The idea is to rebrand the coffee by selling it at a premium level to not only promote a beverage, but also a lifestyle(Vishwanath & Harding, 2000). Starbucks has rebranded their company to model a high-class lifestyle. With the Toronto housing market on a rise, this analysis is to compute the relationship between Toronto condos in relation to the distance of a Starbucks location. The types of models being used are the hypothesized regression models that take the dependent variable and comparing them to our four independent variables. Data Collection: The data collected was used from the REMAX.CA website, this is because it was resourceful in finding precise sizes, locations and market prices of the condominiums in 2015. The limitations of collecting this information was the amount...
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...Conjoint Analysis 1. Explain the interpretation of the following tables -> (a) Utilities table (highest and lowest utility for each attribute) The preference of Package B* is highest (1.867 Utility value) among all other options for the Package and it is lowest in the case of A* (-2.233 utility value). The preference for K2 brand is highest (0.367 Utility value) and the Glory brand has the lowest value (-0.350 Utility value). The preference for the Price level of $1.19 is the highest (-1.108 Utility value) among all other price options and the price level 1.59 has the lowest value (-3.325 utility value). The preference of Seal to yes is more (4.0 utility value) and the Preference for money as yes is more (2.5 utility value). (b) Importance Values It gives us the relative importance of the factors in percentage. Like for Package the importance value is 35.6%, and is the most important factor. Similarly, Price has the importance of 29.5%, Brand has the importance of 15%, Seal has the importance of 11.7% and money is the least important factor with importance of 8.8% only. (Values are the average importance factor) (c) Coefficients The Linear Regression B Coefficient estimate for Price comes out to be -1.108, Seal comes out to be 2.000 and money comes out to be 1.250, when there coefficients are multiplied with value associated with different options of factors this gives us the utility for the same. When factor value for seal as yes (2) is multiplied...
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...Institute of Management, Christ University Kengeri Campus, Bangalore. Review On Cluster Analysis Submitted for the partial fulfillment of process related to Continuous Internal Assessment 1 In Marketing Analytics [MBA - III Trimester (MBA 322)] Submitted By P.Akhilesh 1420419 Cluster analysis? The Cluster Analysis is an explorative analysis that tries to identify structures within the data. Cluster analysis is also called segmentation analysis .It is used to identify groups of cases if the alignment is not previously known. Because it is explorative it does make any distinction between dependent and independent variables. The different cluster analysis methods that SPSS offers can handle binary, nominal, ordinal, and scale (interval or ratio) data. Different types of cluster analysis include: * Hierarchical Analysis * Two step Cluster * K-means cluster From the given data set: * We have both continuous and categorical variables * Continuous variables include: Attitude towards Nike, Awareness towards Nike, Preference for Nike Loyalty for Nike and purchase intention for Nike * Categorical variables: Sex, Hierarchical Cluster Analysis: Ward method: * Here it is clearly mentioned that from cluster one 15 respondents are available , from cluster two 15 respondents are available and from cluster three 10 respondents are available * We have 5 missing respondents who had not respondent or the...
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... Other factors such as position, method of loading, the material of which the beam is made will also influence the deflection. If we wish to find the relationship between y and one of the possible variables it is necessary to keep all the other possible variables constant throughout the experiment. 1. Length calculation In this experiment the same beam is used throughout and the centrally applied point load is kept constant. Thus keeping all possible variables other than the deflection y and the span L constant we may investigate the relationship between y and L. Let y[pic]Ln where n is to be found Then y = k•Ln where k is a constant Taking logarithms: log y = n log L + log k which is in the straight line form (y = mx + C). Thus plotting logy against log L will give a straight-line graph of slope “n” and “k” may be determined. 2. Width calculation In this experiment beams of the same material but of different width are used. The span and loading are kept the same for each beam. Hence keeping all possible variables other than width and deflection constant the relationship between y and b is determined. Let y[pic]bn where n is to be found. Then y = k•bn where k is a constant. Taking logarithms, log y = n log b + log k which is in the straight line from : (y = mx + C). Thus plotting logy against log b...
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...choose the variable “% of Firms expressing that a typical Firm reports less than 100 % of sales” that may act as a proxy for this inclination as the dependent variable. Several other variables are chosen to explain this dependent variable. These explanatory variables are: the firm’s age, ownership (domestic/private), exporter (whether it exports its product or not), % of firms using the web to communicate with clients/suppliers, % of firms using email to communicate with clients/suppliers, average number of unskilled production workers, average number of temporary workers, % of firms using technology licensed from foreign companies, % of firms expected to give gifts to secure a government contract, and the size of the firm. In addition, a constant is considered in the regression. After regressing the dependent variable on the above independent variables the following results have been attained: (will insert equation and table of coefficients) When the firms’ age is considered, a negative relationship can be deducted between it and the dependent variable illustrated by the negative coefficient -0.070. As the firm ages, it is expected to report its sales more accurately. The ages of the firms selected ranged from 2 years to 146 while the average age of these firms was 27.61. This may seem unsurprising as firms with a long history are not expected to underreport their output while new upcoming firms may be more willing to do that. Exporting firms (firms who are expected to...
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...LAB 3.1 Variable Name | Problem (Y or N) | If Yes, what’s wrong? | Declare Real creditsTaken | No | | Declare Int creditsLeft | Yes | The variable should be declared as Real so it can indicate decimal values | Declare Real studentName | Yes | The variable should be a String to store text | Constant Real creditsNeeded = 90 | No | | Step 2: The calculation should be “creditsLeft = creditsNeeded – creditsTaken” Step 3: “The student’s name is Nolan Owens” Step 4: “The Network Systems Administration degree is awarded after 90 credits and Nolan Owens has 70 left to take before graduation.” Step 5: 1. //Provide documentation on line 2 of what this program does 2. //This program calculates how many credits a student still needs to graduate the NSA program 3. //Declare variables on lines 4, 5, 6, and 7 4. Declare Real creditsTaken = 0 5. Declare Real creditsLeft = 0 6. Declare String studentName = “NO VALUE” 7. Declare Constant Real creditsNeeded = 90 8. //Ask for user input of studentName and creditsTaken on line 9 - 12. 9. Display “What is the student’s name?” 10. User input = studentName 11. Display “How many credits does the student have?” 12. User input = creditsTaken 13. //Calculate remaining credits on line 14 14. creditsLeft = creditsNeeded – creditsTaken 15. //Display student name and credits left on line 16 and 17 16. Display “The student’s name is “, studentName 17. Display “They require “, creditsLeft...
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...上海大学 20 4 ～ 2015 学年 秋季学期研究生课程考试 课程名称： Econometrics 论文题目（Title）: 研究生姓名(Name): Hoang Thi Lan Huong 学号(ID): 14760006 研究生班级： 留学生班 成 绩: 任课教师: 叶明确 评阅日期: 1. Introduction (Background and Purpose) The data use for this paper was constructed in the following way. The data are drawn from the OPE Campus Safety and Security Statistics website database to which crime statistics (as of the 2010 data collection) are submitted annually, via a web-based data collection, by all postsecondary institutions that receive Title IV funding (i.e., those that participate in federal student aid programs). This data collection is required by the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Higher Education Opportunity Act. The outcome of this process was that the enrolments is not influenced significantly by the price or living conditions of the campus. When reading some articles I was personally very surprised about this since normally the living conditions should be influent more than the other factors in the enrolments. Based on this, I was very interested which other factors influence to students’ decisions. That is why I chose this dataset to analyze for the final term paper in the course of econometrics. 2. Multiple regressions 2.1 Data source The numerous data used in this paper (reference see below), consists of observations on six variables. The variables are: • enroll ...
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...Week 3 Activity—Calculate Overtime Pay ------------------------------------------------- TCO 3—Given a simple problem, design and desk-check a solution algorithm requiring a modular design that is expressed in terms of pseudocode or program notes, input-process-output (IPO) analysis, and flow chart. ------------------------------------------------- TCO 4—Given a simple problem that requires one or more decisions, create a working solution that uses decisions with logical and relational expressions. ------------------------------------------------- TCO 8—Given a more complex problem, develop a complete solution that includes a comprehensive statement of the problem, complete program design, and program documentation. ------------------------------------------------- Assignment You will need to design an application that it will prompt a user for his or her hourly pay rate, his or her hours worked, and whether he or she is single, married, divorced, or widowed. It will then calculate his or her gross and net pay. If the user works more than 40 hours, overtime is calculated at 1 ½ times the regular rate and displayed separately. If he or she is married, use a flat tax rate of 15%. If he or she is single, use 22%; if he or she is divorced, use 23%; and if he or she is widowed, use 13% (I realize that these are not very realistic). Below is a sample of what the program should do when run. (No overtime example) Enter hourly pay rate: 10.00 Enter hours worked: 35 ...
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...Question 6: Why might we normally expect (in a market with no market functional differences between the brands) that the duplications down each column are approximately the same? Table 3 shows average duplication of each brand, duplication coefficient, expected duplication and the difference of expected duplication and average duplication. In a market, it is expected the duplications across each brands are approximately the same due to constant duplication coefficient. Expected duplication is the value of penetration of each brand multiply by the duplication coefficient of overall market. For example, expected duplication for Kettle Fries is 37%, 33% for BB Corn Chips and 28% for BB Twisties. The average duplication for Kettle Fries is 37%, 33% for BB Corn Chips and 30% for BB Twisties. This proved that the expected duplication is most likely to be the same as average duplication for each brand. Marketers used expected duplication to forecast the average duplication pattern of a product. However, BB Standard is a special case. The expected duplication is not consistent as compare to other brands. The expected duplication of BB Standard is 112%. This might be involvement of partitioning on the brand. Partitioning means clustering of brands compete more intensely with others (Dawes 2006). This is supported by Winchester, Thiel, Arding and Lees (n.d.), “the Duplication of Purchase Law states that the competitive brands which consumers purchase will be similar in the aggregate...
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...EFFECT OF COUNTRY OF ORIGIN ON BRAND EQUITY OF CARS IN INDIA: A STUDY Faculty Guide: Prof. Sandeep Puri Authors: Chandana Mandal, Mohit Jain, Ambika Paliwal, Rohit Iyer, Sahil Gupta INSTITUTE OF MANAGEMENT TECHNOLOGY, GHAZIABAD ABSTRACT Marketers and consumer behaviour researchers generally accept that a product’s or brand’s country-of-origin is an important influencing factor in consumer decision-making. Most of the previous studies suggest that country-of- origin information which is indicated by the “Made in ...” label which serves several purposes in consumer decision-making. It acts as a salient attribute in consumer product evaluation. That is, the image that consumers have about a certain country will influence their perceptions of products from that country. Since consumers’ perception of a particular country-of-origin influences their evaluation of products from that country, this will influence their preference, purchase intention and choice of a particular brand and hence has implications on the brand’s equity. In this study the effects of brand’s country-of-origin image on the formation of brand equity in two clusters i.e India and foreign market(African and French market) is studied. Here a high involvement category of Cars has been selected. Introduction In the arena of global marketing, building strong brands has become a top priority for marketers. Beyond, the marketing mix variables (advertising, distribution, price and product quality), the consumer’s...
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...Term IV Pricing Submitted by Group 9 Deepak V - PGP/17/144 Girish Ganapath J – PGP/17/207 R Gayathri – PGP/17/228 S Akshaya - PGP/17/233 Shraddha S - PGP/17/240 Sonakshi J - PGP/17/358 Term IV Pricing Submitted by Group 9 Deepak V - PGP/17/144 Girish Ganapath J – PGP/17/207 R Gayathri – PGP/17/228 S Akshaya - PGP/17/233 Shraddha S - PGP/17/240 Sonakshi J - PGP/17/358 Determining Willingness to Pay at Pizza Chains Determining Willingness to Pay at Pizza Chains ACKNOWLEDGEMENT We would like to extend our thanks to Prof. Kausik Gangopadhyay, Prof. Rajesh Srinivas Upadhyayula and Prof. Sumit Mitra, coordinators of the “Pricing” course at Indian Institute of Management Kozhikode, for giving us the opportunity to work on this project. We also express our gratitude to the students of PGP-17 and PGP-18 who volunteered to participate in our survey. This helped us to get a good sample size to analyze the data. TABLE OF CONTENTS ACKNOWLEDGEMENT 1 ABSTRACT 3 INTRODUCTION 4 OBJECTIVE 5 METHODOLOGY 5 ANALYSIS AND RESULTS 6 RECOMMENDATIONS 8 LIMITATIONS 9 APPENDIX 10 ABSTRACT Our study identifies the most important and influencing factors for consumer preferences to eat at a pizza outlet like pizza hut, dominoes and pizza corner. With the help of factor analysis and regression we are trying to find out the willingness of a customer to pay at a particular outlet and hence what should be the pricing strategy of the pizza chain. The main...
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...of the equation is an expression, which is to the left of the equal sign. The right side of the equation is an expression, which is to the right of the equal sign. In our case the left side of the equation is 5x - 8 + 2x - 2, while the right side is 7x - 1 - 3x - 3. Terms containing variable x are called variable terms; terms containing the numbers only are called constant terms, or simply constants. The equation under consideration is called a linear equation, because its both sides are linear polynomials. The solution of an equation is such a value of the variable x that turns the equation into a valid equality when this value is substituted to both sides. I am explaining below how to solve this linear equation, in other words, how to find the unknown value of the variable x. The first step you should do is to simplify both sides of the equation by collecting the common terms containing variable x and the common constant terms separately at each side of the equation. Let us do it. By collecting common terms with the variable x at the left side, you will get 5x + 2x = 7x. By collecting common constant terms at the left side you will get -8 - 2 = -10. Thus, now the left side is 7x - 10. Making similar calculations at the right side of the equation, you will get the right side 4x - 4. After these simplifications your equation has the form 7x - 10 = 4x - 4. The first step is...
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...http://hwcampus.com/shop/econ-1002-tutorial-4-assignment/ ECON 1002 Tutorial 4 Assignment You are given data on food expenditure, income and demographic characteristics of 150 married couples, where the husband is a manual worker and is aged 30-50. All survey interviews take place in the same month. You decide to estimate an Engel curve and regress the ratio of food expenditure to income (wfoy) on the logarithm of net current income (ncy), the number of children aged 6-10 (kids2), the number of children aged 11-17 (kids3) and an indicator for London (london = 1 if the couple lives in Greater London, and 0 otherwise). Your estimates are: OLS Estimation, Dependent variable is wfoy, 150 observations Variable Coefficient Standard error constant 0.877 0.065 ncy -0.155 0.014 kids2 0.026 0.006 kids3 0.138 0.032 london 0.047 0.018 R2 0.496 Mean of wfoy 0.189 Mean of ncy 4.473 (a) On the basis of the above estimates, establish if food is a necessity or a luxury. (b) Establish the statistical significance of the coefficient on london. (c) Further, compute the income elasticity for a hypothetical couple living outside London, with mean log income and no children. (d) It has been suggested that share of budget spent on food is a good indicator of household living standard. Households spending a lower share of their budget on food are to be regarded as better off. If number of young children in a household were to increase by one, by how much would income need to rise to preserve...
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