Reference no: EM13739739
Problem 1. In multiple regression analysis, explain whythe typical hypothesis that analysts want totest is whether a particular regression coefficient(B) is equal to zero (H0: B = 0) versuswhether that coefficient is not equal to zero(H1: B ≠ 0).
Problem 2. Explain what types of biases arise in the different approaches to understanding consumer demand and behavior.
Follow these instructions for completing the assignment:
1. Place all answers, both numerical and written, in a single excel spreadsheet.
2. Place each problem into a separate tab or sheet in an Excel file.
3. Place labels on spreadsheet inputs and outputs, and use the yellow highlighter on the top menu bar to highlight your final answer.
4. If the question incorporates graphs, you must replicate the graph on your spreadsheet file.
5. Do not submit Word files or multiple files for a single assignment.
Problem 3. Complete the following questions using the spreadsheet, "ECN-601 Extended Problems Data." Begin by entering the data provided on a separate Excel spreadsheet and label the tab "handout." Complete the following steps for this assignment.
1. Use regression to estimate the demand function. Show the results.
2. Write the subsequent demand equation, with Qd as the dependent variable; Price, Advertising, Product Development, and Rel Price as the independent variables.
3. How strong is the relationship between the quantity demanded and the set of independent variables? List and briefly interpret at least two measures of this strength.
4. Which variable is most important in determining quantity demanded? Justify the reasoning?
5. Solve for the price elasticity of demand. Classify the product's demand as elastic or inelastic. Price elasticity of demand can be found by the following equation:
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ep = (coefficient of price variable x average price) / (average Qd)
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Average values for all variables are below.
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Qd
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626,271
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Price
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$ 10.06
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Advertising
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$ 181,000
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Product Development
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$ 125,417
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Rel Price
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$ 10.16
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6. Solve for the cross price elasticity of demand. Classify the relationship between these products as complements or substitutes. The formula for this coefficient is similar to the one for price elasticity:
7. Forecast Qd if:
Price $10.00
Advertising $150,000
Product Development $150,000
Rel Price $10.25
Construct a 95% confidence interval around this forecast.
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Extended Problems Data
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The following sales and marketing data was accumulated by a manufacturer over the past 12 quarters.
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Qd
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Price
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Adv
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Prod Dev
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Rel Price
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561,628
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$9.75
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$150,000
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$115,000
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$9.46
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697,734
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$9.25
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$162,000
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$100,000
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$9.44
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761,217
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$9.40
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$125,000
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$125,000
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$10.14
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667,994
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$10.25
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$250,000
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$100,000
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$10.09
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496,985
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$10.40
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$200,000
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$150,000
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$10.21
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637,148
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$9.90
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$160,000
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$120,000
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$10.16
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569,773
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$10.55
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$250,000
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$100,000
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$10.34
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663,146
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$10.17
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$245,000
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$115,000
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$10.31
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432,424
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$10.30
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$185,000
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$140,000
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$10.52
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535,571
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$10.50
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$140,000
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$150,000
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$10.56
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698,376
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$10.20
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$185,000
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$140,000
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$10.38
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793,260
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$10.00
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$120,000
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$150,000
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$10.32
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Variable Definitions:
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Qd = quantity demanded, in units
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Price = price per unit charged by the firm that quarter
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Adv = dollars spend in advertising, that quarter
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Prod Dev = dollars spent in product development that quarter. Product development should be thought of as
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money spent on developing additional features or properties that make the product more desirable
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in the eyes of customers.
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Rel Price = Price of a related product
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