6) Suppose you have the following data on the price of orange and the quantity sold:Price per Pound (in QuantitySold (inDollars)Pounds)0.500.751.001.251.5010769952Assume that the quantity sold (Y) is a linear function of the price (X), i.e.Y₁ =B₁ + B₂X₁ + ε₁Estimate the population regression coefficients. (Do not use Computer)

The price of orange and the quantity sold is represented in the table below:Price per poundQuantity…

6) Suppose you have the following data on the price of orange and the quantity sold: (in Price Dollars) Sold Pound (in Quantity Pounds) 0.50 0.75 1.00 1.25 1.50 per 10 7 6 5 2 Assume that the quantity sold (Y) is a linear function of the price (X), i.e. Y₁ =B₁ + B₂X₁ +8₁ Estimate the population regression coefficients. (Do not use Computer)

6) Suppose you have the following data on the price of orange and the quantity sold: (in Price Dollars) Sold Pound (in Quantity Pounds) 0.50 0.75 1.00 1.25 1.50 per 10 7 6 5 2 Assume that the quantity sold (Y) is a linear function of the price (X), i.e. Y₁ =B₁ + B₂X₁ +8₁ Estimate the population regression coefficients. (Do not use Computer)

Managerial Economics: Applications, Strategies and Tactics (MindTap Course List)

14th Edition

ISBN:9781305506381

Author:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Publisher:James R. McGuigan, R. Charles Moyer, Frederick H.deB. Harris

Chapter4: Estimating Demand

Section: Chapter Questions

Problem 1.1CE

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QUESTION 1 Suppose a researcher collects data on houses that have been sold in a particular neighbourhood over the past year, and obtains the regressions results in the table shown below. This table is used for Questions 1-6. Dependent variable: In(Price) Regressor (1) (2) (3) (4) (5) 0.00042 (0.000038) Size In(Size) 0.57 (2.03) 0.69 0.68 0.69 (0.055) (0.054) (0.087) In(Size)² 0.0078 (0.14) Bedrooms 0.0036 (0.037) Рol 0.082 0.071 0.071 0.071 0.071 (0.032) (0.034) (0.034) (0.036) (0.035) 0.037 0.027 0.026 0.027 0.027 (0.030) View (0.029) (0.028) (0.026) (0.029) Pool x View 0.0022 (0.10) 0.12 (0.035) Condition 0.13 0.12 0.12 (0.035) 0.12 (0.045) (0.035) (0.036) 6.63 (0.53) Intercept 10.97 6.60 7.02 6.60 (0.069) (0.39) (7.50) (0.40) Summary Statistics SER 0.102 0.098 0.099 0.099 0.099 R? 0.72 0.74 0.73 0.73 0.73 Variable definitions: Price = sale price ($); Size = house size (in square feet); Bedrooms = number of bedrooms; Pool = binary variable (1 if house has a swimming pool, 0…
Given the following data X (consumers of teff) or popn 3 6 8 1 13 13 14 Y ( teff consumption) 8 6 10 12 12 14 20 year 2013 2014 2015 2016 2017 2018 2019 Estimate the regression equation, Y= a+bX, Where Y denotes demand for teff while X is consumers of teff (population) By assuming demand for teff is only affected by its consumers, find the amount demand for teff in the year 2022 if the populations (consumers of teff) are about 18 people? (Hint: use the least square method, parameter a and b can be estimated by solving the two linear equations) SY= na+ bSX SXY=aSX +b Where n is number of years. For example, Estimate the sales for 2012, 2015 and fit a linear regression equation and draw a trend line.ar X Sales (Y) XY X2 year X Sales (Y) XY X2 2002 1 22734 22734 1 2003 2 24731 49462 4 2004 3 31489 94467 9 2005 4 44685 178740 16 2006 5 55319…
Setting: U.S. Auto manufacturers are trying to develop a multivariate function with which to estimate the demand for their gas-electric hybrid compact cars. Here is one that Motors General developed for its Jolt: Qj = 65000 – 20Pj + 20Pf + 35Pt – 5Pb + 0.2Tc + 0.05Y + 10Mg + 0.04A Where Qj = the number of Jolts demanded per week. Pj = the price of each new Jolt (in $). Pf = the price of each new Ford gas-electric hybrid (in $). Pt = the price of each new Toyota gas-electric hybrid (in $). Pb = the price of replacement batteries for the Jolt (in $). Tc = the amount of tax credit incentive offered with the purchase of a new hybrid (in $). Y = average weekly disposable income of a typical Jolt purchaser (in $). Mg = the miles per gallon of gas rating of the Jolt (in miles per gallon). A = average weekly Jolt advertising expenditure (in $). 6 If all variables remain unchanged except that the price of the Toyota hybrid (Pt) decreases by $500, then the demand for Jolts will: 7…
The following data relate the sales figures of the bar in Mark Kaltenbach's small bed-and-breakfast inn in portland, to the number of guest registered that week: week guests bar sales 1 16 $330 2 12 $270 3 18 $380 4 14 $315 a) The simple linear regression equation that relates bar sales to number of guests(not to time) is (round your responses to one decimal place): Bar sales = [___]+[___]X guests
Sally Sells Sea Shells by the Sea Shore and collects all sales dataNow she is curious to find out what the elasticity of demand is for her shells Assume they are all the same type and quantity She scatter plots the data and finds there is a linear relationship that looks ripe for a regression estimation of the price response function for her shells The slope of her regression line is 61. Currently, her average daily price is 11.74 and she sells 95 quantity at that priceCalculate the point elasticity of demand for her sea shells
The table to the right contains price-demand and total cost data for the production of projectors, where p is the wholesale price (in dollars) of a projector for an annual demand of x projectors and C is the total cost (in dollars) of producing x projectors. Answer the following questions (A) - (D). (A) Find a quadratic regression equation for the price-demand data, using x as the independent variable. X 270 360 520 780 The fixed costs are $. (Round to the nearest dollar as needed.) ITTI y = (Type an expression using x as the variable. Use integers or decimals for any numbers in the expression. Round to two decimal places as needed.) Use the linear regression equation found in the previous step to estimate the fixed costs and variable costs per projector. The variable costs are $ per projector. (Round to the nearest dollar as needed.) (C) Find the break even points. The break even points are (Type ordered pairs. Use a comma to separate answers as needed. Round to the nearest integer as…
The following table shows worldwide sales of a certain type of cell phone and their average selling prices in 2012 and 2013. Year 2012 2013 Selling Price ($) 395 325 Sales (millions) 741 1,133 (a) Use the data to obtain a linear demand function for this type of cell phone. (Let p be the price, and let q be the demand). q(p) = Use your demand equation to predict sales if the price is lowered to $255. million phones (b) Fill in the blank. For every $1 increase in price, sales of this type of cell phone decrease by million units.
Numerical Answer Only Type Question Enter the numerical value only for the correct answer in the blank box. If a decimal point appears, round it to two decimal places. Assume that the number of visits by a particular customer to a mall located in downtown Toronto is related to the distance from the customer's home. The following regression analysis shows the relationship between the number of times a customer visits(Y)per month and the distance(X, measured in km) from the customer's home to the mall. \[ Y=15-0.5 X \] A customer who lives30 kmaway from the mall will visi______ who lives10 km away. less times than a customer
As the selling price of a product increases, demand decreases. Fewer people will buy a product if it is more expensive. This is a very important concept. The demand equation for new iSmarts is given by 0.025x , where p is the unit p = 100 price (in dollars) of the iSmart and x is the number of units produced. We can determine the total revenue (money earned for selling x iSmarts) by multiplying the number of items we sell, x and the price p. The total revenue obtained by producing and selling x iSmarts is given by = xp 100x – 0.025x². It makes sense that this function is quadratic. The company can price the iSmarts so high that no one will buy them and the revenue will be zero. The can also set the price to $0, but the revenue will still be $0, no matter how many iSmarts they "sell". In between, the graph is a parabola opening downward. Determine prices p that would keep revenue above 6530 dollars. Give your answers to 2 decimal places. The revenue will be above 6530 if price is…
An analyst working for your firm provided an estimated log-linear demand function based on the natural logarithm of the quantity sold, price, and the average income of consumers. Results are summarized in the following table: SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept LN Price LN Income df 0.968 0.937 0.933 0.003 30 SS MS F 2 0.003637484 0.001818742 202.48598 0.000242516 8.98206E-06 27 29 0.00388 Coefficients Standard Error 0.57 0.00 0.13 0.51 -0.08 0.15 t Stat 0.90 -19.50 1.13 P-value 0.37 0.00 0.27 Significance F 5.55598E-17 Lower 95% -0.65 -0.09 -0.12 How would a 4 percent increase in income impact the demand for your product? Demand would increase by 60 percent. Demand would increase by 0.6 percent. Demand would decrease by 60 percent. Demand would decrease by 0.6 percent. Upper 95% 1.68 -0.07 0.41
As an auto insurance risk analyst, it is your job to research risk profiles for various types of drivers. One common area of concern for auto insurance companies is the risk involved when offering policies to younger, less experienced drivers. The U.S. Department of Transportation recently conducted a study in which it analyzed the relationship between 1) the number of fatal accidents per 1000 licenses, and 2) the percentage of licensed drivers under the age of 21 in a sample of 42 cities. Your first step in the analysis is to construct a scatterplot of the data. FIGURE. SCATTERPLOT FOR U.S. DEPARTMENT OF TRANSPORATION PROBLEM U.S. Department of Transportation The Relationship Between Fatal Accident Frequency and Driver Age 4.5 3.5 3 2.5 1.5 1 0.5 6. 10 12 14 16 18 Percentage of drivers under age 21 Upon visual inspection, you determine that the variables do have a linear relationship. After a linear pattern has been established visually, you now proceed with performing linear…
3. To explain what determine the price of air conditioners, B.T. Ratchford, obtained the following regression results based on a sample of 19 air conditioners: R²=0.84 y=-68.236 +0.023x, +19.729x3, +7.653x (0.005) (8.992) (3.082) se= Where y the price, in dollars x2= the BTU rating of air conditioner x3 the energy efficiency ratio x, the number of settings se standard errors a) Interpret the regression results. b) Do the results make economic sense? c) At 5% significance level, test the hypothesis that the BTU rating has no effect on the price of an air conditioner vs. that it has a positive effect. d) Would you accept the null hypothesis that the three explanatory variables explain a substantial variation in the prices of air conditioners? Show clearly all your calculations.