Price-Demand: p = D(x) 984 410 2140 220 2940 |143 4200 55 5125 45 Price-Supply p = S(x) 984 21 2140 77 2940 108 4200 |150 5125 190 Find a quadratic regression model for the price-demand data and a linear regressio price-supply data. Use these models to answer the following questions.
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- Hello, I am trying to find the equations on my calculator for the price-demand and price supply equations. The data is in the attached image. I think I am doing something wrong, but not sure what. I found the quadratic regression model for the first set of data using my calculator, but I used the p=D(x) as list one, and x, as list two. I came up with 0.028x^2-23x +5743 is this right? or do I need the reverse the order? For the price-supply data I but the p=S(x) as list 1 and x as list 2 and I got the linear regression function: 2 5.1x+342 Can you please let me know if I am on the right track?Suppose that an economist has been able to gather data on the relationship between demand and price for a particular product. After analyzing scatterplots and using economic theory, the economist decides to estimate an equation of the form Q= aPb, where Q is quantity demanded and P is price. An appropriate regression analysis is then performed, and the estimated parameters turn out to be a = 1000 and b = - 1.3. Now consider two scenarios: (1) the price increases from $10 to $12.50; (2) the price increases from $20 to $25. a. Do you predict the percentage decrease in demand to be the same in scenario 1 as in scenario 2? Why or why not? b. What is the predicted percentage decrease in demand in scenario 1? What about scenario 2? Be as exact as possible.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
- Consider the following data regarding students' college GPAs and high school GPAs. The estimated regression equation is Estimated College GPA=1.85+0.4743(High School GPA).Estimated College GPA=1.85+0.4743(High School GPA). GPAs College GPA High School GPA 3.843.84 2.562.56 3.573.57 3.903.90 2.072.07 3.143.14 4.004.00 3.223.22 3.873.87 2.882.88 2.212.21 2.082.08 Copy Data Step 1 of 3 : Compute the sum of squared errors (SSE) for the model. Round your answer to four decimal places.Imagine you are trying to explain the effect of square footage on home sale prices in the United States. You collect a random sample of 100,000 homes that recently sold. a) Homes can be one of three types: single-family houses, townhomes, or condos. How would you control for a home’s type in a regression model? b) Write down a regression model that includes controls for home type, square footage, and number of bedrooms. c) How would you interpret the es3mated coefficients for each of the variables from part b? Be specific.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
- What is a linear regression model? What is measured by the coefficients ofa linear regression model? What is the ordinary least squares estimator?An economic research centre has published data on GDP and Demand for refrigerators as given below:Year 2011 2012 2013 2014 2015 2016 2017GDP (billion) 20 22 25 27 30 33 35Refrigerator 50 60 80 80 90 100 120(a) Estimate regression equation R= a+by, where R= No of refrigerator sold and Y= GDP.Forecast demand for refrigerator in the year 2018 and 2019. The research centre has projected GDP for 2018 and 2019 at Rs. 38 billion and Rs. 40 billion respectively.Below table contains a data sample where X is the independent, and Y the dependent variable. Using the data, please conduct a regression analysis. Determine first the regression equation with the help of below graph and table, then answer all the questions. 1. The value of cell a is 2. The value of cell b is 3: The value of cell c is 4: The value of cell d is 5: The value of cell e is 6: The value of cell f is 7: The value of cell g is 8. The value of cell h is 9: The value of cell i is 10: The value of cell j is 11: The value of cell k is 12: The value of cell l is 13: The value of cell m is 14: The value of cell n is 15: The value of cell o is 16: The value of cell p is 17: The value of cell q is 18: The value of cell r is 19: The value of cell s is 20: The value of cell t is 21: The value of cell u is 22: The value of cell v is 23: The value of cell w is 24: The value of cell x is 25: The value of cell y is 26: The value of cell z is 27: The value of cell aa is 28: The value of cell…
- General Cereals is using a regression model to estimate the demand for Tweetie Sweeties, a whistle-shaped, sugar-coated breakfast cereal for children. The following (multiplicative exponential) demand function is being used: QD=6,280 P(−1.85)A2.05N2.70QD=6,280 P−1.85A2.05N2.70 where QDQD = quantity demanded, in 10-oz boxes PP = price per box, in dollars AA = advertising expenditures on daytime television, in dollars NN = proportion of the population under 12 years old, in percent What is the point price elasticity of demand for Tweetie Sweeties? 2.05 2.70 -0.90 -1.85 What is the advertising elasticity of demand? 0.76 -1.85 2.70 2.05A manufacturer is developing a facility plan to provide production capacity for its factory. The amount of capacity required in the future depends on the number of products demanded by its customers. The data below reflect past sales of its products: Year Annual Sales (number of products) Year Annual Sales (number of products) 1 490 5 461 2 487 6 475 3 492 7 472 4 478 8 458 Use simple linear regression to forecast annual demand for the products for each of the next three (3) years, by using the tabular method to: derive the values for the intercept and slope derive the linear equation plot the linear regression line develop a forecast for the firm’s annual sales for each of the next three yearsThe 2008 sales and profits of seven companies were given as follows Firm Sales ($ Billions) Profit ($ Billions) Fiat 5.7 0.27 Honda 6.7 0.12 BP 0.2 0.01 Toyota 0.6 0.04 Apple 3.8 0.05 IBM 12.5 0.46 Phillips 0.5 0.02 The estimated value for the company’s Profit can be estimated using the equation; Y ̂i = α ̂ + β ̂Xi……………………………………………………………………Eqn.1 Where; Y = Companies Profit X = Companies Sales α ̂ and β ̂ are estimated parameters in the model Calculate the sample regression line, where profit is the dependent variable (Y) and sales is the independent variable (X)