Linear Regression Model for Political Science Y=a+ß1X1+ß2X2+ß3X3+ß4X4+ß5X5+ß6X6+

Question

Linear Regression Model for Political Science

Y=a+ß1X1+ß2X2+ß3X3+ß4X4+ß5X5+ß6X6+ß7X7+ß8X8+e

where Y = % of Gallup respondents naming candidate as the debate winner; x1 = % of electorate strongly identifying with candidate's party; x2 = % of electorate weakly identifying with candidate's party; x3 = % of electorate identifying as independents leaning toward candidate's party; x4 = candidate's level of support in pre-debate “horse race” poll; x5 = candidate's level of support in pre-debate “horse race” poll * incumbency; x6 = candidate's lead over (or percentage behind) his opponent in pre-debate “horse race” poll; x7 = candidate's lead over (or percentage behind) his opponent in pre-debate “horse race” poll * incumbency; x8 = pre-debate net Gallup presidential approval rating (negative sign for challenger); ß1 to ß8 = parameters to be estimated; e = error.

Assuming i can acquire the information for X1 --> X8, how would i fill out this model for any given candidate?

Table 1. Model predicting proportion of voters naming candidate debate winner. Std. error Coefficient 438 001 1.411 939 Variable Strong party ID Weak party ID Independent "Leaners" Raw poll number Raw poll number (incumbent) Lead in polls Lead in polls (incumbent) Presidential approval Constant 498 468 530** 555* 062 300** 242** 058 29.201 091 646 578 091 26.287 52 52 p .05 (one-tailed) p05 (two-tailed)

Explanation / Answer

Assuming i can acquire the information for X1 --> X8, how would i fill out this model for any given candidate?

The model is

% of Gallup respondents naming candidate as the debate winner = -26.287 + 0.438 * strong party ID + 0.001 * weak party ID + 1.411 * Independent leaners + 0.939 * raw poll number - 0.091 * Raw Poll number (incumbent) - 0.646 * Lead in polls + 0.578 * Lead in polls (incumbent) + 0.091 * presidential approval

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