What common issue might students face when incorporating examples in their writing?

Why is writing in the third person preferred in academic writing?

Consider the Euclidean vector space  with the dot product. A subspace

When using external examples, which of the following is crucial to include?

How can historical examples enhance an argument in an essay?

What type of audience is typically addressed in academic writing?

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Campaign Expenditure  (part 2) Use the VOTE.DTA data for this question. Consider the following model voteA=β0+β1ln⁡(expendA)+β2ln⁡(expendB)+β3prtystrA+u{“version”:”1.1″,”math”:”voteA = \beta_0 + \beta_1 \ln(expendA) + \beta_2 \ln(expendB) + \beta_3 prtystrA + u”} where voteA is the percentage of the vote received by candidate A, expendA and expendB are the campaign expenditures by candidates A and B respectively, and prtystrA is the percentage of the most recent presidential vote that went to A’s party. In terms of the parameters of the model, the null hypothesis that a 1% increase in candidate A’s expenditure would be exactly offset by a 1% increase in candidate B’s expenditure is as follows: 

Campaign Expenditure (part 3) Use the VOTE.DTA data for this question. Consider the following model voteA=β0+β1ln⁡(expendA)+β2ln⁡(expendB)+β3prtystrA+u{“version”:”1.1″,”math”:”voteA = \beta_0 + \beta_1 \ln(expendA) + \beta_2 \ln(expendB) + \beta_3 prtystrA + u”} where voteA is the percentage of the vote received by candidate A, expendA and expendB are the campaign expenditures by candidates A and B respectively, and prtystrA is the percentage of the most recent presidential vote that went to A’s party. Now re-parameterize the model to test the null hypothesis from the previous question, i.e., a 1% increase in candidate A’s expenditure would be exactly offset by a 1% increase in candidate B’s expenditure. Let θ=β1+β2{“version”:”1.1″,”math”:”θ=β1+β2″} The new regression model will be: 

Campaign Expenditure (part 1) Use the VOTE.DTA data for this question. Consider the following model voteA=β0+β1ln⁡(expendA)+β2ln⁡(expendB)+β3prtystrA+u{“version”:”1.1″,”math”:”voteA = \beta_0 + \beta_1 \ln(expendA) + \beta_2 \ln(expendB) + \beta_3 prtystrA + u”} where voteA is the percentage of the vote received by candidate A, expendA and expendB are the campaign expenditures by candidates A and B respectively, and prtystrA is the percentage of the most recent presidential vote that went to A’s party. Estimate the model and then give the interpretation of β^1{“version”:”1.1″,”math”:”β^1″}and β^2{“version”:”1.1″,”math”:”β^2″}.