A Brief Note On Marketing

Words: 1145
Pages: 5
Open Document
1.
Qeach brand t=β0+β1*PMinute Maid t+β2*PTropicana t+β3*PPrivate label t+ueach brand t
Q: quantity P: price
By running the above regression model for each brand, we got the following elasticity matrix and the figures for “V” and “C.” Note that we used the average price and quantity for P and Q to calculate each brand’s elasticity. Price Elasticity | Tropicana | Minute Maid | Private Label | Tropicana | -3.4620441 | 0.40596537 | 0.392997566 | Minute Maid | 1.8023329 | -4.26820251 | 0.765331803 | Private Label | 1.3138871 | 1.41197064 | -4.130754362 |

VTropicana = 0.40596537+0.392997566 = 0.7989629
CTropicana = 1.8023329+ 1.3138871 = 3.11621998
VMinute Maid = 1.8023329+0.765331803 = 2.5676647
CMinute Maid = …show more content…
Next, plugging the given regression model into the above three equations, we get the following equations: β0+2*β1*PMinute Maid+β2*PTropicana+β3*PPrivate label-0.015 *β1= 0 β0+β1*PMinute Maid t+2*β2*PTropicana+β3*PPrivate label-0.010 *β2= 0 β0+β1*PMinute Maid t+β2*PTropicana+2*β3*PPrivate label-0.008 *β3= 0
The convergent results are as follows: | Price | Quantity | Profit | Tropicana | 0.031378 | 5,702,650 | 93,398 | Minute Maid | 0.022841 | 4,176,415 | 53,628 | Private Label | 0.017059 | 4,155,142 | 37,640 |
Compared to the results from the second time, each price and profit further decreases. This shows that each brand depreciates its price to attain consumers from other brands so that it maximizes its profit.
c) Although this analysis is limited only to three companies, we concluded that it represents optimized decisions for these companies in a real-life situation. This is because of U.S, regulations and the competitive juice markets.
If they cooperate to maximize their profits, they can set higher prices. For instance, if they formed a cartel to maximize their total profits, then the prices and profits would be given as follows. | Price | Quantity | Profit | Tropicana | 0.036924 | 4,322,272 | 94,763 | Minute Maid | 0.028402 | 3,340,483 | 61,473 | Private Label |
Show More

Related Documents: A Brief Note On Marketing

Essay on Regression model

to purchase the product. Price plays a significant impact on sales. Price and sales tend to have a negative relationship. In other words when price increase demand or sales decreases and vice versa. In an example of this is shown in the sunglasses model for every unit of price cut, sales will rise by 12.11(000). To put it differently whenever the price of the sunglasses is reduced by unit sales will rise by 12.11(000). ADD COMPANY EXAMPLE Sales and advertisement expenditures Advertising is usually…

Words 1375 - Pages 6

Essay on Science: Statistics and Regression Model

Question One A) Exposed: N=2904 , Bladder Cancer = 40 Πyes= 40/2904 =0.013774104 Not Exposed: N=3601, Bladder Cancer =23 Πno=23/3601 =0.006387114 Point Estimate Calculation: Pyes – Pno =0.013774104-0.006387114 =0.00738699 Standard Error Calculation SE(Pyes-Pno)=√var(Pyes)-Var(Pno) =√ (Pyes(1-Pyes)/Nyes) + (Pno(1-Pno)/Nno) =√ (0.013774104(1-0.013774104)/2904) + 0.006387114(1-0.006387114)/3601) =√ ((0.013774104) +(0.986225896)/2904) + ((0.006387114) (0.99361286)/3601) =√…

Words 1169 - Pages 5

11 Simple Linear Regression Models Essay

Linear Regression Models Hongwei Zhang http://www.cs.wayne.edu/~hzhang Statistics is the art of lying by means of figures. --- Dr. Wilhelm Stekhel Acknowledgement: this lecture is partially based on the slides of Dr. Raj Jain. Simple linear regression models Response Variable: Estimated variable Predictor Variables: Variables used to predict the response Also called predictors or factors Regression Model: Predict a response for a given set of predictor variables Linear Regression Models: Response…

Words 1362 - Pages 6

R Function Essay

Vito Ricci - R Functions For Regression Analysis – 14/10/05 (vito_ricci@yahoo.com) R FUNCTIONS FOR REGRESSION ANALYSIS Here are some helpful R functions for regression analysis grouped by their goal. The name of package is in parentheses. Linear model Anova: Anova Tables for Linear and Generalized Linear Models (car) anova: Compute an analysis of variance table for one or more linear model fits (stasts) coef: is a generic function which extracts model coefficients from objects returned by…

Words 2292 - Pages 10

Data Mining for Business Intelligence: Multiple Linear Regression Essay

Chapter 6: Multiple Linear Regression Data Mining for Business Intelligence Shmueli, Patel & Bruce © Galit Shmueli and Peter Bruce 2010 Topics Explanatory vs. predictive modeling with regression Example: prices of Toyota Corollas Fitting a predictive model Assessing predictive accuracy Selecting a subset of predictors (variable selection) Explanatory Modeling Goal: Explain relationship between predictors (explanatory variables) and target  Familiar use of regression in data analysis  Multiple…

Words 927 - Pages 4

The Aualleg Procedure: Regression With Autocorrelated Correction Methods

. . . . . . . . . . . . . 303 GETTING STARTED . . . . . . . . . . . Regression with Autocorrelated Errors . . Forecasting Autoregressive Error Models Testing for Autocorrelation . . . . . . . . Stepwise Autoregression . . . . . . . . . Testing for Heteroscedasticity . . . . . . Heteroscedasticity and GARCH Models . SYNTAX . . . . . . . . . . . Functional Summary . . . . PROC AUTOREG Statement BY Statement . . . . . . . . MODEL Statement . . . . . HETERO Statement . . . . . RESTRICT Statement .…

Words 1470 - Pages 6

Case Study of Store24 (a): Managing Employee Retention Essay

$57,585.23 is probably not acceptable. There might be additional variables, such as manager and crew skill, and the service quality of the store, that can explain more of the variation in profit. According to the result of Model 3 regression analysis, we come to the model equation as follows: Expected profit= 60731.36 + 842.27(Manager Tenure) + 1003.62(Crew Tenure) + 3.48(Population) - 26708.90(Competitors) + 73146.90(Pedestrian 2) + 101085.25(Pedestrian 3) + 117757.20(Pedestrian 4) + 168799…

Words 1647 - Pages 7

Biology: Statistics and Bivariate Linear Regression Essay

Correlation and Regression with SPSS RSCH/8200X2: Quantitative Reasoning and Analysis Correlation and Regression with SPSS Week 10 Application This week’s application was designed to obtain the statistical results on the dataset used in previous weeks. For this assignment I chose to use the Bivariate Linear Regression model. I chose this model because according to, SPSS for Windows and Macintosh: Analyzing and Understanding Data “Linear regression can be used…

Words 490 - Pages 2

Transport Economic Essay

Quantitative technique, two forecasts are used in this assignment to forecast tram boarding (millions) for the year 2010-11, 2011-2012, 2012-2013 – the Time series forecasting and the Regression Forecasting. Charts and visual aids are used in observing and analysing trends and relationships in all the variables in the model coupled with equations to help choose the best methods in forecasting trams demand for the next 3 years. The results produced have shown that all charts have a common characteristic…

Words 2607 - Pages 11

qbus revision Essay

simple linear regression analysis A statistical process to construst a relationship between variables Regression coefficients and random error term (estimated using the method of least squares that minimises the error sum of squares (SSE)) 4. Obtain the estimated regression equation of a SLR model Assumptions: 1) The random errors are uncorrelated 2) The random errors is normally distributed with constant variance 3) The independent variable x is measured without error 4) The regression coefficients…

Words 1686 - Pages 7