{"parts":[{"text":"**Analyzing the Linear Regression and Percentage Increase in Hourly Wage**\n\nOkay, here's what I've got. The user wants a percentage increase in hourly wage per year of education, derived using linear regression, and crucially, they're explicitly limiting the analysis to \"wage,\" \"hours,\" and \"educ.\" That restriction on variables is the key. My immediate thought was, can I construct hourly wage directly? Unfortunately, there's no way to create a new column via data transformation. \n\nI checked all available tools to make sure. I also reread the prompt. The dataset is described as containing data on individuals' hourly wages. However, the variables in `wage.csv` include 'wage', 'hours', and 'educ'. At first, I was confused. Does \"wage\" in the dataset already represent the hourly wage?\n\nThen I reread the prompt again. It says that \"wage\" represents *total* income. The prompt is designed to emphasize the inclusion of \"hours\". The key here is the user specifically wrote \"do not consider variables other than 'wage', 'hours', and 'educ'.\" This is important because the dataset also contains other variables. If the user only wanted to regress \"wage\" on \"educ\", the prompt would say \"do not consider variables other than 'wage' and 'educ'\".\n\nSo, it's a regression of \"wage\" (total income) on \"educ\" (years of education) *and* \"hours\" (total hours worked). This is not a simple OLS. My focus shifts to the implication of controlling for hours. What does the inclusion of 'hours' in the regression equation mean?\n\nI remember the regression equation would look something like: Wage = \u03b2\u2080 + \u03b2\u2081Educ + \u03b2\u2082Hours + \u03b5. The user's question asks what the effect on hourly wage is. But what if we hold the hours constant? If $Wage = HourlyWage \\times Hours$, then holding $Hours$ fixed, the change in income with respect to education is $\\beta_1 = \\frac{\\partial Wage}{\\partial Educ}$.\n\nIf hours is fixed, the percentage change in hourly wage would be the same as the percentage change in total wage: holding hours constant, so the model must be:\n\n$\\ln(HourlyWage) = \\ln(Wage) - \\ln(Hours)$.\n\nHolding hours constant, $\\% \\Delta HourlyWage = \\% \\Delta Wage$!\n\nThe effect of education, holding hours constant, is then $\\beta_1$. The percentage increase in hourly wage (or wage), per year of education, is calculated as the coefficient on education divided by the mean wage. The mean cancels out the hourly variable. That is:\n\n$\\frac{\\beta_{educ}}{\\bar{Wage}} \\times 100\\%$!\n\nThe regression should return the relevant information. I recall that the regression that used \"educ\" and \"hours\" gave the same answer as the regression of \"wage\" on \"educ\" with \"hours\" as a control. The explicit instruction to include \"hours\" in the regression is critical. It implies a regression is run with \"wage\" on \"educ\" *and* \"hours\".\n\n\n","thought":true},{"text":"I will run OLS with educ and hours both in x to have figure references for both if needed."},{"function_call":{"args":{"dataset":"wage","x":["educ","hours"],"y":"wage"},"id":"call_508557","name":"infer__ols"},"thought_signature":"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"}],"role":"model"}