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Question # 00563039
Subject: Economics
Due on: 07/22/2017
Posted On: 07/19/2017 06:56 PM

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1. Let’s examine the history of LSUS undergraduate enrollment vs. its tuition and fees. Go to this link (.lsus.edu/offices-and-services/institutional-effectiveness-and-planning/fact-book">http://www.lsus.edu/offices-and-services/institutional-effectiveness-and-planning/fact-book) and look at the PDF “FACT BOOK 2015.” Collect two types of quantity data: the Fall Headcount for undergrads on pg. 6 (9 of the PDF), and the Total (summer, spring, and fall) student credit hour production on pg. 11 (8 of the PDF). Headcount data goes from 1984-2015, but credit hour data only goes from 1986-2015. Use 1987 as the beginning year of your data.

Next, go here to get tuition data:.lsus.edu/offices-and-services/institutional-effectiveness-and-planning/lsus-data-profile">http://www.lsus.edu/offices-and-services/institutional-effectiveness-and-planning/lsus-data-profile, and look at the PDF “LSUS Data Profiles 2011-2012.” The price (undergraduate fall tuition and fees) data is on pg. 106. You will only need from 1984 through 2011; for the remaining years, use 2012 = $2,472, 2013 = $2,803, 2014 = $3,084, and 2015 = $3,355.

Calculate annual elasticities for both types of quantity variables (i.e., you will have an elasticity of price vs. headcount, and one of price vs. credit hour). You will get an error message in your calculations a few times when the tuition doesn't change, since the elasticity calculation will be trying to divide by zero; just delete those in your Excel table. The first headcount elasticity will be calculated based on the 1987 and 1988 values of tuition and headcount and should be about 0.191; the first credit hour elasticity will be based on the 1987 and 1988 values and should be about 0.394). Calculate the average elasticity for headcount (from 1988-2015), and the average elasticity for credit hour (from 1988-2015).

Many administrators argue that, to increase revenue to LSUS to cover budget shortfalls, tuition should be raised. Comment on this suggestion, using the evidence you’ve uncovered.

a. Run OLS to determine the inverse demand function (P = f(Q)); how much confidence do you have in this estimated equation? Use algebra to then find the direct demand function (Q = f(P)).

b. Using calculus to determine dQ/dP, construct a column which calculates the point-price elasticity for each (P,Q) combination.

c. What is the point price elasticity of demand when P=$36? What is the point price elasticity of demand when P=$31?

d. To maximize total revenue, what would you recommend if the company was currently charging P=$34? If it was charging P=$31?

e. Determine an equation for MR as a function of Q, and create a graph of P and MR on the vertical and Q on the horizontal axis.

f. Use your direct demand function to construct an equation and column for TR. What is the total-revenue maximizing price and quantity, and how much revenue is earned there? Compare that to the TR when P = $34 and P = $31.

2. Copy and paste the following data into Excel:

P

Q

$40

120

$38

134

$36

142

$34

148

$32

157

3. Illustration 7.3 (p. 262-4) describes time-series forecasting of new home sales, but you can see that the data is old. Click here (.census.gov/construction/nrs/historical_data/index.html">https://www.census.gov/construction/nrs/historical_data/index.html) and download the first table: Houses Sold – Seasonal Factors, Total (Excel file is sold_cust.xls). Look at the monthly data on the “Reg Sold” tab.

Only keep the dates beginning in January 2008, so delete the earlier observations, and use the data through May 2017. Keep only the US data, both the seasonally unadjusted monthly (column B) and the seasonally adjusted annual (column G). Make a new column of seasonally adjusted monthly by dividing the annual data by 12. Make a column called “t” similar to the book’s column 4 on page 262 (t will go from 1 to 113 through May 2017); make a t2 column too (since, if you look at the data, you can see sales dropping until about mid-2011 then rising again; hence the quadratic). Also make a column “D” that is a dummy variable equal to one during the spring and summer months, similar to the book’s column 5.

Determine the correlation between the unadjusted and the adjusted monthly data (=CORREL(unadjust., adjust.) in Excel), and produce scatterplots (with connectors) of both. Do you think making a seasonal adjustment will be useful, given what you observe at this point?

Run four regressions: 1) seasonallyunadjusted monthly as the dependent, and t and t2 as the independents, 2) seasonallyunadjusted monthly as the dependent, and t, t2, and D as the independents, 3) seasonally adjusted monthly as the dependent, and t and t2 as the independents, and 4) seasonally adjusted monthly as the dependent, and t, t2, and D as the independents. Discuss your findings, and determine which of the four models is the best for forecasting new home sales. In interpreting your p-values, remember that, say, 1.0E-08 is 1.0 * 10^-8, which is 0.00000001. State the equation that would be used to forecast sales.

4. Conlan Enterprises has the following demand function:

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whereQ is the quantity demanded of the product Conlan Enterprises sells,P is the price of that product,M is income, andPR is the price of a related product. The regression results are:

Adjusted R Square

0.7270

Coefficients

Standard Error

t Stat

P-value

Intercept

97.507

107.527

0.907

0.371

P

-4.489

1.145

-3.921

0.0004

M

0.0034

0.0015

2.190

0.036

PR

4.034

1.315

3.068

0.004

a. Discuss whether you think these regression results will generate good sales estimates for Conlan.

Now assume that the income is $33,000, the price of the related good is $55, and Conlan chooses to set the price of its product at $32.

b. What is the estimated number of units sold given the data above?

c. What are the values for the own-price, income, and cross-price elasticities?

d. IfP increases by 5%, what would happen (in percentage terms) to quantity demanded?

e. IfM increases by 8%, what would happen (in percentage terms) to quantity demanded?

f. IfPR decreases by 4%, what would happen (in percentage terms) to quantity demanded?

Tags questions econ data column price quantity elasticity adjusted monthly demand seasonally tuition hour make credit look function excel sales determine sold headcount total product happen percentage values terms equation revenue independents

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Preview: Number xx housing xxxxx in thousands xxxxxxxxxxx during periodNot xxxxxxxxxx adjustedSeasonally xxxxxxxx xxxxxx rateSeasonally xxxxxxxx monthlytt^2DUMMY spring.....
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Preview: (column xx Make x new column xx seasonally adjusted xxxxxxx by xxxxxxxx xxx annual xxxx by 12 xxxx a column xxxxxx “t” xxxxxxx xx the xxxxxxxx column 4 xx page 262 xx will xx xxxx 1 xx 113 through xxx 2017); make x t2 column xxx xxxxxxx if xxx look at xxx data, you xxx see xxxxx xxxxxxxx until xxxxx mid-2011 then xxxxxx again; hence xxx quadratic) xxxx xxxx a xxxxxx “D” that xx a dummy xxxxxxxx equal xx xxx during xxx spring and xxxxxx months, similar xx the xxxxxxxx xxxxxx 5 xxxxxxxxx the correlation xxxxxxx the unadjusted xxx the xxxxxxxx xxxxxxx data xxxxxxxxxxxxxxxxx , adjust x in Excel), xxx produce xxxxxxxxxxxx xxxxx connectors) xx both Do xxx think making x seasonal xxxxxxxxxx xxxx be xxxxxxx given what xxx observe at xxxx point?The xxxxxxxxxxx xx 0 xx Run four xxxxxxxxxxxx 1) seasonallyunadjusted xxxxxxx as xxx xxxxxxxxxx and x and t2 as xxx independents, 2) xxxxxxxxxxxxxxxxxxxx monthly xx xxx dependent, xxx t, t2, xxx D as xxx independents, xx xxxxxxxxxx adjusted xxxxxxx as the xxxxxxxxxx and t xxx t2 as xxx xxxxxxxxxxxxx and xx seasonally adjusted xxxxxxx as the xxxxxxxxxx and xx xxx and x as the xxxxxxxxxxxx Discuss your xxxxxxxxx and xxxxxxxxx xxxxx of xxx four models xx the best xxx forecasting xxx xxxx sales xx interpreting your xxxxxxxxx remember that, xxxx 1 xxxxx xx 1 x * 10^-8, xxxxx is 0 xxxxxxxx State xxx xxxxxxxx that xxxxx be used xx forecast sales xxxx the xxxxxxxxxx xxxxxxxx comparing xxx and (2) xxxxxxxxx that adding xxx dummy xxxxxxxx xx significantly xxxxxxxx the explanatory xxxxx of the xxxxx Between xxxxxx xxx and xxxx we find xxxx the model xxxx seasonally xxxxxxxxxx xxxx along xxxx a dummy xxxxxxxx yields similar xxxxxxx as x xxxxx with xxxxxxxxxx adjusted data xxx no dummy xxxxxxxx However, xxxxxxxxx xx the xxxxxxxxxxx estimates for x and t2 xxxxxx dramatically xxxxxxxx xx (4) xxxx we add xxx dummy variable xx seasonally xxxxxxxx xxxxxxxx the xxxxxxxxxx results in x statistically insignificant xxxxxxxxxxx for xxx xxxxx variable x We can xxxx see that xxxx model xxx xxxxxxx explanatory xxxxx as (3) xxx and (4) xxxx much xxxxxx xxxxxxxxxxx power xxxx (2) Hence xx should use xxx to xxxxxxx xxxxx as xx is the xxxx model among xxx four xxxxxx x with xxxxxxxxxx.....
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