A researcher is interested in estimating the supply function of an online car rental service provider as a function of its average price (Price, measured by the average per kilometre price charged for the month of October, 2017). He has data on the price charged and the number of cars available on the road for rent, (Car), for the 200 cities in the U.S. in which the provider operates. However, given that there is simultaneous causality between Price and Car due to the interactions between demand and supply, the supply function cannot be estimated consistently by an OLS regression of Car on Price. He, therefore, uses Income (measured by the per capita GDP of a given city in the U.S. for 2017) as an instrument which satisfies the two conditions of instrument validity. He uses the two stage least squares (TSLS) estimator of the coefficient on Price which enables him to estimate the supply function. Suppose, the sample covariance between Car and Income, Scar Income is 1.45, and the sample covariance between Price and Income, Sincome Price is 2.45.

2. Baldur is a big fan of salaries. He collects data on wages and the length of schooling the people had from 100 employees in a large company with the aim of creating a model that can predict wages by level of education (measured in the number of years in school after primary school). The employees with the lowest level of education in Baldur's data immediately entered the labor market after compulsory school leaving, while those with the highest level of education have completed 9 years of schooling after leaving compulsory school leaving. Baldur started by drawing the data and saw that the relationship is linear. He also calculated some fish sizes that can be seen below: standard Change average deviation salary 660020 150100 Education (year from primary school diploma) 5.5 2.2 He also calculated the correlation coefficient between salary and level of education (measured in the number of years in school from primary school exam) and obtained r = 0.55. a.) Find the equation regression…

Profitability remains a challenge for banks and thrifts with less than $2 billion of assets. The business problem facing a bank analyst relates to the factors that affect return on assets (ROA), an indicator of how profitable a company is relative to its total assets. Data collected from a sample of 20 community banks include the ROA (%), the efficiency ratio (%), as a measure of bank productivity (the lower the efficiency ratio, the better), and total risk-based capital (%), as a measure of capital adequacy. Complete parts (a) through (g) below. Click the icon to view the data table. a. State the multiple regression equation. Let X₁¡ represent the efficiency ratio (%) and let X₂¡ represent the total risk-based capital (%). Ŷ₁=+X₁₁+Xzi (Round the constant to two decimal places as needed. Round the coefficients to four decimal places as needed.) Distribution Costs ROA (%) 1.04 0.77 0.14 1.44 0.90 1.16 1.07 1.58 1.45 1.15 1.17 3.40 0.86 0.66 0.81 1.14 0.83 0.86 1.14 1.04 TOlal Efficiency…

The Excel file (Forecasting Assignment Data) contains quarterly motorcycle shipments for Harley-Davidson from 2000 through 2006. Forecast Harley-Davidson quarterly motorcycle shipments for 2007 using 1) Holt’s Method  2) Winter’s Method 3) Linear Trend Model  4) Quadratic Trend Model  5) Quadratic Trend with Seasonal Indices Model  In addition, make a recommendation of which forecasting method you think Harley-Davidson should use and why. Year Quarter Period DemandAt 2000 1 1 49,057   2 2 53,329   3 3 48,077   4 4 54,129 2001 1 5 54,154   2 6 60,161   3 7 56,611   4 8 63,535 2002 1 9 64,669   2 10 65,540   3 11 67,474   4 12 65,970 2003 1 13 70,608   2 14 76,025   3 15 67,458   4 16 77,056 2004 1 17 74,090   2 18 82,034   3 19 80,578   4 20 80,587 2005 1 21 76,716   2 22 77,128   3 23 87,585   4 24 87,588 2006 1 25 79,506   2 26 79,796   3 27 97,046   4 28 92,848