The most recent FASB-IASB convergence projects include:

a. Leases, Research and Development, Revenue Recognition, and Fair Value Measurement.

b. Leases, Revenue Recognition, Fair Value Measurement, and Joint Ventures.

c. Insurance Contracts, Post-Employment Benefits, Income Taxes and Impairment

d. Insurance Contracts, Income Taxes, Leases, and Revenue Recognition.

e. Revenue Recognition, Leases, Insurance Contracts, and Income Taxes.

The total revenue​ (in hundreds of​ dollars) from the sale of x spas and y solar heaters is approximated by

R(x,y) = 11+196x+218y−8x^2−7y^2−4xy.

Find the number of each that should be sold to produce maximum revenue. Find the maximum revenue.

Find the derivatives Rxx​, Ryy​, and Rxy.

Rxx=​____

Ryy=​____

Rxy=____

Selling spas _____ and _____solar heaters gives the maximum revenue of $_____.

Using out two-team model, graphically demonstrate that the marginal revenue (MR) from hiring talent is equal across large and small revenue markets in equilibrium. In the same graph, show and clearly label revenue imbalance, competitive imbalance and payroll imbalance.

Hint: You should use a two-team model (one large revenue team and one small revenue team and assume that their winning percentages sum to 1.00)

MR_L = 100 – 120 W_L

MR_S = 60 – 80 W_S

Solve for both winning percents and the MR in equilibrium. Show this on your graph.

The following data includes the year, make, model, mileage (in thousands of miles) and asking price (in US dollars) for each of 13 used Honda Odyssey minivans. The data was collected from the Web site of the Seattle P-I on April 25, 2005.

Compute the correlation between age (in years) and mileage for these minivans. (Assume the correlation conditions have been satisfied and round your answer to the nearest 0.001.)

Dependent Variable: BVPS_FSC                                          

Method: Least Squares                                               

Date: 07/25/18   Time: 12:06                                     

Sample (adjusted): 4/01/1998 4/01/2013                                            

Included observations: 15 after adjustments                                       

Variable           Coefficient       Std. Error        t-Statistic         Prob.

C                     3.316771        5.621129         0.590054         0.5714

CET_FSC       0.013773         0.021733         0.633729         0.5439

CR_FSC         0.489317         3.034456         0.161254         0.8759

CTR_FSC       0.008914         0.008949         0.996106         0.3484

ROA_FSC      -2.286163        1.433001         -1.595368        0.1493

ROE_FSC       0.759472         0.474621         1.600166         0.1482

ROI_FSC        0.261457         0.198688         1.315919         0.2247

R-squared                     0.360769            Mean dependent var           6.687333

Adjusted R-squared    -0.118654           S.D. dependent var             1.987921

S.E. of regression        2.102553            Akaike info criterion           4.628907

Sum squared resid       35.36584            Schwarz criterion                4.959330

Log likelihood           -27.71680             Hannan-Quinn criter.          4.625387

F-statistic                     0.752506             Durbin-Watson stat             0.637955

Prob(F-statistic)          0.625229                                 

1. discuss in detail the above data

Alternative-Fueled Vehicles The table shows the numbers (in thousands) of alternative-fueled

vehicles A in use in the United States from 1995 to 2011. (Source: U.S. Energy Information Administration)

(a) Use a graphing utility to plot the data. Let t represent the year, with t = 5 corresponding to 1995. (b) A model for the data is

4615.36t − 8726.7

1 + 15.01t − 0.542t2, 5 ≤ t ≤ 21

where t = 5 corresponds to 1995. Use the model to estimate the numbers of alternative-fueled vehicles in 1996, 2006, and 2011. How do your answers compare to the original data?

(f ) Use the model to predict the numbers of alternative-fueled vehicles in 2016 and 2017

* Need help to understand F . Should I be using a particular formula

PART IV - ANALYSIS AND INTERPRETATION OF EPIDEMIOLOGIC RESULTS

The following food exposure information was collected through the cohort study. On January 19, the information was tabulated by epidemiologists from the Argentine MOH. (Table 2)

Table 2. Foods eaten by ill and well bus drivers at the home at the terminal bus stop, January 3-7, 1998. (N=21)

*Matambre is a traditional meat roll in Argentina.

**Mate is green tea.

Question 12: Calculate the appropriate measures of association for these exposures.

Question 13: Interpret the results. What further data analysis/information might help?

For this submission, you will be given a series of scenarios and small collections of data. You should plot the data or calculate probabilities using excel. Then, you will create your own real or hypothetical scenario to graph and explain.

Answer the following:

The mean temperature for the month of July in Boston, Massachusetts is 73 degrees Fahrenheit. Plot the following data, which represent the observed mean temperature in Boston over the last 20 years:

Is this a normal distribution? Explain your reasoning.

What is an outlier? Are there any outliers in this distribution? Explain your reasoning fully.

Using the above data, what is the probability that the mean will be over 76 in any given July?

Using the above data, what is the probability that the mean will be over 80 in any given July?

The table below contains real data for the first two decades of AIDS reporting.

Graph "year" vs. "# AIDS deaths." Do not include pre-1981. Label both axes with words. Scale both axes. Calculate the following. (Round your answers to the nearest whole number. Round the correlation coefficient r to four decimal places.)

a=

b=

r=

n=