Suppose that you had data on the amount of pollution in London every year. Write down the regression equation that you would need to estimate to measure the effect of ULEZ on pollution. Describe carefully what the dependent variable, the independent variable, the unit of observation (time or location), and the main coefficient of interest are. What control variables do you think should be included in this regression?
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Suppose that you had data on the amount of pollution in London every year. Write down the regression equation that you would need to estimate to measure the effect of ULEZ on pollution. Describe carefully what the dependent variable, the independent variable, the unit of observation (time or location), and the main coefficient of interest are. What control variables do you think should be included in this regression?
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- 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…(2)What would the consequence be for a regression model if theerrors were not homoscedastic?The Results below show the output of the following model: ?=?0+?1?1+?2?2+? Coefficient St. Error t-ratio Intercept 10.492 0.6655 15.77 ?1 0.0154 0.1889 0.08 ?2 0.1353 0.1889 0.72 Observations 100 ?2 0.985 Correlation matrix: X1 X2 X1 1 X2 0.950 1 Instructions: a. The above results show that the model has the problem of multicollinearity, what are the indicators of multicollinearity that can be identified from these results? b. What are the solutions to rectify multicollinearity?
- 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 shellsIf we run a regression where y (bankruptcy) = f (factors potentially predicting bankruptcy), what is the dependent variable?The table below shows the profit, P(x), in dollars, from selling x items. 1 2 3 6 9 14 P(x) 66.1 82.4 88.9 49.6 -77.9 -486.4 Using your calculator to do a quadratic regression, express the profit as a function of the number of items sold. P(x) = Round all numbers to 1 decimal place. Using your quadratic regression, estimate the profit from selling 5 items? Select an answer v Round to 2 decimal places. Using your quadratic regression, estimate the number of items that need to be sold to make a profit of $-25.6 ? Select an answer
- Expedia wants to use regression analysis to build a model for airfare tickets prices in the states: Ticket prices = 30 + B1*Miles + E where Miles is measured in hundreds Coefficients 169.50 5.90 Intercept Miles (in hundreds) Which of the following is true? Standard Error 1.34 0.09 4 t Stat 126.85 61.28 P-value 0.000 0.002 If Miles increases by 1, then we predict ticket price to go up by $5.9. O If ticket price goes up by $1, then we predict Miles to go up by 590 miles. O If ticket price goes up by $100, then we predict Miles to go up by 590 miles. If Miles increases by 100, then we predict ticket price to go up by $5.9.Question: 1. Please chose a topic you are interested in within social policy-education, health care, mental health etc. and write out a hypothetical regression estimation for this topic. Make sure to identify the ideal outcome variable, explanatory variable, and control variables.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.