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Economics 4570 Summer 2020 Demand Project Prof. Roach Instructions: In this project, you will use Microsoft Excel to run linear regressions as a way to estimate demand functions. You will need the...

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Economics 4570 Summer 2020 Demand Project Prof. Roach Instructions: In this project, you will use Microsoft Excel to run linear regressions as a way to estimate demand functions. You will need the “Analysis ToolPak” to do this. Note that there are videos within the Chapter 3 module in D2L that show you how to install the ToolPak (if it is not already in your copy of Excel) and how run a regression in Excel. The data you will need is the Excel file “demand_data_summer_2020.xlsx” in the “Demand Project” module on D2L. The data contains information on the quantity of digital cameras sold in a given geographical market, the price in that market, the unemployment rate in that market, and the median household income in that market. Please submit your regression output and plots within an Excel workbook (with your name included in the name of the file). Submit your answers to the rest of the questions as a Word document or PDF (with your name included in the name of the file). Both files can be uploaded to the “Demand Project” Dropbox on the D2L site. 1. Using the Data Analysis features from the ToolPak, run a linear regression with quantity as the dependent variable and price as the explanatory variable. a. Is the price variable statistically significant at the 5% level? How do you know? b. How much of the variation in quantity is explained by price? c. If price is $100, what is our predicted quantity? d. If I raise my price by $5, what would be our predicted change in quantity? e. Create a scatter plot with a linear trendline through the data points. (Hint: the videos on D2L, in particular “Doing a Regression in Microsoft Excel, are helpful here). One way to add a trendline is to select a data point, right-click that point, and then choose “Add Trendline”) Choose the options where the trendline equation and R-squared are displayed. 2. Run a linear regression with quantity as the dependent variable and with price, unemployment rate, and median household income as explanatory variables. a. Which of these explanatory variables are significant at the 5% level? b. Which of these explanatory variables are significant at the 1% level? c. How much of the variation in quantity is explained by the explanatory variables? d. If price rises by $1, what would be our predicted change in quantity? e. If the unemployment rate falls by 1 percentage point, what would be our predicted change in quantity? 3. Which regression model (#1 or #2) do you prefer? Why?
Answered Same Day May 26, 2021

Solution

Komalavalli answered on May 30 2021
148 Votes
1. Regression Mode1:
y = 44323.3 -299.3X1
y – Demand for Quantity of cameras
X1 – Price of cameras
a)     The price variable is statistically significance at 5% level. The p-value of price coefficient is 0 which is less than 5 %.
)     The R square value of this model is 0.65. It means that 65 % of variation in quantity is explained by price.
c)     If price is $ 100 the predicated quantity for cameras is 14394.
y = 44323.3 -299.3(100)
y= 14394.01
If price is $ 100 the predicated quantity demand for cameras is 14394.    
d)     If price is $ 5 the predicated quantity demand for cameras is 42826.8.
y= 44323.3 -299.3(5)
y = 42826.8.
If price is $ 5 the predicated quantity for cameras is 42826.8.
e) Scatter plot
The above plot indicates that there is negative co
elation between price and quantity demand for cameras. That is when price increases the quantity demand for cameras decreases and vice versa.
2. Regression Model 2:
y = 41281.3 -142.9 X1-4074.1 X2+0.1 X3
y – Demand for Quantity of cameras
X1 – Price of cameras
X2 –Percentage of Unemployment rate
X3 – Median Household Income
a)     The price, Unemployment rate and Median house income variables are statistically significance at 5% level. The p-value of these coefficients is 0 which is less than 5 %.
)     The price, Unemployment rate and...
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