WEEK 11 & 12 ACTIVITY 8

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University of the Cumberlands *

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Economics

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Feb 20, 2024

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WEEK 11 & 12 ACTIVITY 8 Name: Akul Patel UC ID: 005011924 1. Based on the data provided, write out an expression for the data-generating process for subscriptions per 1,000 local residents. The data-generating process can be represented by the following linear regression model: Subs_1000 = β0 + β1 * Price + β2 * Income + β3 * Labor_Cost + ε Where: β0, β1, β2, and β3 are the coefficients (parameters) to be estimated, ε is the error term, representing the part of Subs_1000 that cannot be explained by the model. From the regression coefficients are β0 = 220.5905974 β1 = 5.567126569 β2 = 0.980212339 β3 = -7.600456097 R-value on regression [Labor cost affecting Subscriptions per 1000] is 0.06277 which is very small thus we can say that labor cost does not affect subscriptions per 1000 value. R- value on regression [Labor cost affecting price ] is 0.22699 which shows labor costs does effect more on price than subscriptions per 1000 value. 2. Estimate the effect of basic package price on subscriptions per 1,000 local residents using OLS. Why might you distrust this result as being a causal effect? R-SQUARE value on running a regression for price affecting subscriptions per 1000 is 0.4737 which shows it affects up to some level. However, there are a number of reasons why we could doubt that this outcome is a causal effect. - Bias due to omitted variables: The model may not take into account some

variables that may have an impact on both the cost of the entry-level package and the proportion of subscribers per 1,000 residents. Examples of significant characteristics include the caliber of the cable service, the accessibility of other entertainment options, or local cable TV preferences. If these elements influence the number of subscribers and relate to price, our estimate of the price's impact on subscriptions may be inaccurate.

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