Using the data below, what is the slope coefficient from a regression of Quantity on Price? It is probably easiest to use either Grett or Jamovi as in the powerpoint example. (round your answer to the nearest 0.1, and it can be either positive or negative.) Quantity Price 17 24 23 39 55 54 60 76 71 80 93 77 62 64 47 35 38 12 29 2
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Using the data below, what is the slope coefficient from a regression of Quantity on Price? It is probably easiest to use either Gretl or Jamovi as in the powerpoint example. (round your answer to the nearest 0.1, and it can be either positive or negative.)
Quantity 17 24 23 39 55 54 60 76 71 80
Price 93 77 62 64 47 35 38 12 29 2
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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?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 guestsPlease no written by hand and no emage Your company, which specializes in running shoes for men who are growing increasingly follicly-challenged (BalderDash®), has the following demand function: Q = a + bP + cM + dR where Q is the quantity demanded of BalderDash’s most popular shoes, P is the price of that product, M is consumer income, and R is the price of a related product. The regression results are: Adjusted R Square 0.7796 Independent Variables Coefficients Standard Error t Stat P-value Intercept 21,055.04 1428.27 14.74 8.1E-16 P -83.912 19.079 -4.398 0.000 M 0.0266 0.013 2.064 0.047 R -16.6 10.664 -1.556 0.129 Discuss whether you think these regression results will generate good sales estimates for BalderDash. Now assume that the income is $69,100, the price of the related good is $39, and BalderDash chooses to set the price of its product at $54. b. What is the estimated number of units sold given the data above? (round to nearest unit; no decimals) c.…
- 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 2.05Imagine that you work for the maker of a leading brand of low-calorie, frozen microwavable food that estimates the following demand equation for its product using data from 26 supermarkets around the country for the month of April. Note: The following is a regression equation. Standard errors are in parentheses for the demand for widgets. QD = - 5200 - 42P + 20PX + 5.2I + 0.20A + 0.25M (2.002) (17.5) (6.2) (2.5) (0.09) (0.21) R2 = 0.55 n = 26 F = 4.88 Your supervisor has asked you to compute the elasticities for each independent variable. Assume the following values for the independent variables: Q = Quantity demanded of 3-pack units P (in cents) = Price of the product = 500 cents per 3-pack unit PX (in cents) = Price of leading competitor's product = 600 cents per 3-pack unit I (in dollars) = Per capita income of the standard metropolitan statistical area (SMSA) in…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.
- Suppose you run a regression with quantity as your dependent variable and advertising as one of your independent variables. The p-value on advertising is .08. The marketing team is arguing that their advertising efforts are impacting sales, but the finance/economics department is arguing that there isn't evidence that the advertising is impacting sales. What side would you take and why?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.Q. Wilpen Company, a price-setting firm, produces nearly 80 percent of all tennis balls purchased in the United States. Wilpen estimates the U.S. demand for its tennis balls by using the following linear specification: Q = a + bP + cM + dPR. Where Q is the number of cans of tennis balls sold quarterly, P is the wholesale price Wilpen charges for a can of tennis balls, M is the consumers’ average household income, and PR is the average price of tennis rackets. The regression results are as follows: a- Discuss the statistical significance of the parameter estimates a^, b^, c^, and d^ using the p-values. Are the signs of b^, c^, and d^ consistent with the theory of demand? Wilpen plans to charge a wholesale price of $1.65 per can. The average price of a tennis racket is $110, and consumers’ average household income is $24,600. b. What is the estimated number of cans of tennis balls demanded? c) At the values of P, M, and Pr given, what are the estimated values of the price (E^), income…
- Q. Wilpen Company, a price-setting firm, produces nearly 80 percent of all tennis balls purchased in the United States. Wilpen estimates the U.S. demand for its tennis balls by using the following linear specification: Q = a + bP + cM + dPR. Where Q is the number of cans of tennis balls sold quarterly, P is the wholesale price Wilpen charges for a can of tennis balls, M is the consumers’ average household income, and PR is the average price of tennis rackets. The regression results are as follows: a. Discuss the statistical significance of the parameter estimates a^, b^, c^, and d^ using the p-values. Are the signs of b^, c^, and d^ consistent with the theory of demand? Wilpen plans to charge a wholesale price of $1.65 per can. The average price of a tennis racket is $110, and consumers’ average household income is $24,600. b. What is the estimated number of cans of tennis balls demanded? c) At the values of P, M, and Pr given, what are the estimated values of the price (E^), income…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…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.