Hp Dell

Dell vs. HP

1. Compare the cost estimation results.

We performed a regression analysis. As the variables we used Net Sales (independent variable) and COGS + Operating Expenses (dependent variable). We want to investigate how the latter depend on the former. COGS + Operating Expenses represent the output or outcome whose variation we want to study. Net Sales work as the inputs and represent causes. We then try to explain the effects that the independent variable has on the dependent variable.

Let’s start with the case of Dell.

Regression Statistics |

Multiple R | 0.998184496 |

R Square | 0.996372289 |

Adjusted R Square | 0.996042497 |

Standard Error | 709.3679481 |

Observations | 13 |

The observations lead to an R² of 99.6%. This means that 99.6% of the COGS + Operating Expenses can be explained by the Net Sales. This is a very high number and indicates that Net Sales are very significant when determining COGS + Operating Expenses. The causality can be questioned however as we only have a very limited number of observations.

  | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% |

Intercept | -1420.807284 | 909.3096169 | -1.562512105 | 0.146460021 | -3422.184257 | 580.5696887 |

X Variable 1 | 0.963957857 | 0.017537482 | 54.96557898 | 8.91322E-15 | 0.925358119 | 1.002557595 |

We can determine the regression equation. In theory, the intercept can serve as the fixed cost and the slope can serve as the variable cost. However, the negative coefficient may seem striking as a negative fixed cost basis does not make sense. It has to be noted here that the intercept is not statistically relevant. Our sample observations range in the high ten-thousands. We cannot draw conclusions for out of the sample observations. It could be the case that out of the sample events do not follow a linear trend. The slope however, is statistically significant and we will compare it with HP.

Therefore, let’s move on to the case of...