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Regression analysis is often used as a tool for causal inference. A typical application of regression analysis for casual inference will fit a model using the outcome as the response variable and the potential cause(s) as the predictor(s). Because of the inevitable confounding factors in a typical social science study, the regression model will inevitably include other predictors to account for the variability associated with different conditions. Including confounding factors in a regression model is often called controlling in social science. It is this controlling that often leads to the misuse of regression analysis. For example, Kanazawa and Vandermassen [2005] suggested that parent’s occupation can predict the likelihood of having boys or girls. Particularly, if the parent’s occupation is “systematizing” (e.g., engineering), she/he tends to have more boys, and if the parent’s occupation is “empathizing” (e.g., nursing), he/she tends to have more girls. The conclusion was reached by using a regression analysis to the University of Chicago’s General Social Survey data. When studying a parent’s likelihood of having boys, the article used a regression model of the form:

Regression analysis is often used as a tool for causal inference. A typical application of...-1

That is, number of boys is predicted by parent’s occupation after controlling the number of girls (opposite sex children), plus other predictors (such as income) (Table 1 of Kanazawa and Vandermassen [2005]). The theory was illustrated using the model because the slope for engineer is positive and statistically different from 0. In a letter to editor, Gelman [2007] pointed out that this result may be a statistical artifact, and proposed a simulation.

The simulation creates two groups of families (nurses and engineers) of families, each having one or two children. Collectively, child sex ratios of the two groups of families are both one boy to one girl. The difference between a nurse family and an engineer family is how they decide the number of children: nurses will stop at having one child if the first born is a boy, and two children otherwise; engineers will stop at one child with probability 30% and continue on to a second child with probability 70%, regardless of the sex of the first child. In this simulated data, the probability of a boy is exactly 50% for all births; thus the true effect, the difference in sex ratios between engineer and nurse families, is actually zero. Under this simulated model, nurses will have the following distribution of family types: 50% boy, 25% girl-boy, 25% girl-girl. Engineers will have the distribution: 15% boy, 15% girl, 17.5% boy-boy, 17.5% boy-girl, 17.5% girl-boy, 17.5% girl-girl. Use the following scripts to generate 800 families of engineers and 800 families of nurses and fit the regression model:

Regression analysis is often used as a tool for causal inference. A typical application of...-2

Is the model result in conflict with the data? Any thoughts on why this would happen (hint: think about the meaning of the slope of engineer)?

 
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You are the owner of the Basket of Rye Corner Market, a small local convenience store that specializes in fresh bread but that also sells various sundries, including cigarettes and alcohol. You hire Tanisha, a clerk, to cashier and Ryan, a manager to run the store when you are not there. You are almost never in the store. You have The law in your State states: “Every person attempting to purchase beer or alcohol must be of the legal age of majority, 21, and must show government-issued Identification, unless they are personally known to the seller. It is unlawful to sell beer or alcohol to a minor.” The fine for violating the law is $5,000 per occurrence. To adhere to the law, you instruct Ryan to post signs in the store alerting customers to the rule. One evening, some teenagers visit the store. One of the teenagers, Sarah, is the younger sister of your clerk, Tanisha. The teens send Sarah to the counter to buy the beer. She is able to convince Tanisha to make the sale. None of the teens show identification. An undercover police saw the entire transaction, and see the teenagers leave the store with the beer. They confiscate the beer and question the Tanisha. You are contacted. Discuss if the law was violated and who, if anyone, may be held responsible for the violation? What is your BEST argument that your business should not be fined.

 
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  1. Write an email message delivering good news for a situation that you might encounter in your current work or your future career.
  2. Write an email message delivering bad news for a situation that you might encounter in your current work or your future career.

For both messages, consider guidelines from the text for strengthening effectiveness and maintaining a positive tone.

Include both messages in one Word document.

Dear associate accountant,

I know that this past tax season we have been busy with a lot of work. I have also noticed how you have picked up the other work of some of your co-workers while they where out sick. While also passing the cpa exam, which is why I wanted to inform you that you will be receiving a promotion to senior accountant and a pay bump in your salary. Speak with HR and payroll on Monday.

Congratulations,

Jeb Josephs

Dear Associate Accountant,

I know that you have been trying your best during this past tax season. But talking with your senior manager and hearing about how your communication has been a bit sharp and not responding to messages. Here at our accounting firm we value teamwork and even though you got all of your work finished. We have decided to part ways and not renew your contract. Security will help you gather your things at your work station, good luck with another accounting firm.

Jeb Josephs

improve these or another message

 
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The steel industry is differentiated into three segments: A, B, and C. In March 2008, Nucor in the market segment of A cut its product price by 20%. Which of the following statement best describes the potential impact of Nucor’s price cutting? Nucor’s price cutting is likely to create stronger competition pressure on firms in Segment A than on firms in Segment B and C. Nucor’s price cutting is likely to create stronger competition pressure on firms in Segment A than on firms in Segment B but not those in Segment C. Nucor’s price cutting is likely to create equal competition pressure on firms in Segment A, B, and C. Nucor’s price cutting is likely to create stronger competition pressure on firms in Segment B and C than on firms in A.
 
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