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 = 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 |
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…
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…
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…
$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…
Characteristics………………………………………………………..5 2.0 The Conceptual model applied for analysis………………………………………...6 3.4 The Conceptual Model……………………………………………………......6 3.5 Justification for the selection of variables………………………………......7 3.0 Regression Analysis…………………………………………………………………....8 4.6 Regression Analysis for LIDL and ALDI……………………………............8 3.1.0 Model interpretation for LIDL and ALDI………………………….....9 3.1.1 Regression Analysis Model…………………………………………….9 3.1.2 Goodness of fit - Sector…………………………………………
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…
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…