Econ

econ

Alexis Flint

Memo 1

Economics for Managers

September 21, 2015

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Regression Statistics | |

Multiple R | 0.8857 | |

R Square | 0.7844 | |

Adjusted R Square | 0.7730 | |

Standard Error | 0.1793 | |

Observations | 21.0000 | |

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After looking at the summary output, we can determine that the overall fit of the regression is good because of the high R-square and Adjusted R square. R-square is 77.3% so that tells us that price explains 77.3% of the variation of the demand. The F-statistic provides a measure of the total variation explained by the regression. With a high f-statistic of 69.1221 it also shows us that it is a good fit. The higher the f-statistic means more of the regression line goes through the data.

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  | Coefficients |

Intercept | 1.9711 |

Price | -0.1075 |

We can also determine that the service is elastic at the current price. The price over number of subscribers multiplied by the coefficient will give us the elasticity of demand. For the current STARZ we have -.1075 * (10.50/842). After the data is plugged into the price elasticity of demand equation, the elasticity it -1.3248. The absolute value of the elasticity is greater than one which makes it elastic. This means the service can vary significantly due to the change in price. With this being said we know that a decrease in price will increase revenues. The revenue is maximized when the good is considered unitary elastic. If we set the price elasticity of demand equal to 1, we would get P= $9.20 and Q= 989. So (9.20/.989)* -.1075 = -1 and the absolute value would be one making it unitary elastic.

In conclusion, the decrease in price will increase the revenue. By dropping the price to $9.20 for the promotional event the number of subscribers would increase by 137 increasing the amount of revenues by $152.80. The maximum revenue made in a month would be $9,098.80.