Suppose you are interested in estimating how health affects productivity. Specifically, you are interested in the...
Suppose you are interested in estimating how health affects productivity. Specifically, you are interested in the following regression: where Yi is a variable representing the income earned by person i, and Xi is a variable representing the health of person i. Income is a fairly straightforward concept to capture. Health, however, is a more complex concept that can be measured in many different ways. For example, body mass index (BMI - weight in kilograms, divided by height in meters squared)...
Suppose you are interested in estimating how health affects productivity. Specifically, you are interested in the following regression: where Yi is a variable representing the income earned by person i, and Xi is a variable representing the health of person i. Income is a fairly straightforward concept to capture. Health, however, is a more complex concept that can be measured in many different ways. For example, body mass index (BMI - weight in kilograms, divided by height in meters squared)...
Suppose you are interested in estimating how health affects productivity. Specifically, you are interested in the following regression: where Yi is a variable representing the income earned by person i, and Xi is a variable representing the health of person i. Income is a fairly straightforward concept to capture. Health, however, is a more complex concept that can be measured in many different ways. For example, body mass index (BMI - weight in kilograms, divided by height in meters squared)...
Suppose you are interested in estimating how health affects productivity. Specifically, you are interested in the following regression: where Yi is a variable representing the income earned by person i, and Xi is a variable representing the health of person i. Income is a fairly straightforward concept to capture. Health, however, is a more complex concept that can be measured in many different ways. For example, body mass index (BMI - weight in kilograms, divided by height in meters squared)...
PREDICTION (REGRESSION) – Chapter 12 3. A researcher was interested in whether there was a relationship between stress and depression scores obtained from emergency health care providers. The data from 10 emergency workers are below. This is the same data you calculated a correlation on in Question 1. We are trying to use stress scores (IV or Predictor) to predict depression scores (DV or outcome). Using SPSS, calculate a linear regression model: Y = a+b1X1 where Y is Depression and...
1.You are interested in estimating the effects of owning a tablet (such as an iPad) on the grades of high school students (the idea is that owning a tablet might help students take notes and study more effectively). You have data on tablet ownership and GPA for a sample of 1000 students. You wish to estimate this model: GPA=B0+B1tablet+u where GPA is the student's grade point average measured on a scale of 0-4, and tablet is a dummy variable that...
Suppose you are interested in understanding the causal impact of having an MBA (versus just an undergraduate degree in business) on earnings. To this end, you estimate a regression of the following form: EARNINGS = 55679 + 27809(MBA) Write out the causal question/relationship that is implied by the above equation. The estimated coefficient above suggests that individuals with an MBA earn $27,809 more than those with just a business undergraduate, on average. Give an example of how omitted variable bias...
11. Suppose you are interested in estimating the effect of hours spent in an SAT preparation course (hours) on total SAT score (sat). The population is all college-bound high school seniors for a particular year. (i) Suppose you are given a grant to run a controlled experiment. Explain how you would structure the experiment in order to estimate the causal effect of hours on sat. (ii) Consider the more realistic case where students choose how much time to spend in...
1) Suppose that you are interested in the relationship between the return rate on a stock in 2010 compared to the return rate in 2009. You believe that the return rates in both years are positively correlated. A sample of 15 stocks yields the following regression results: b0= 5.3, b1= 1.04, s= 1.79, s = 0.2163, R2 = 0.64, and MSE = 35.4. Calculate the regression sum of squares. What is the correlation coefficient for the stock returns of the...
2. Suppose you are interested in the relationship between weekly wage earnings in dol lars) and age (in years). You run a linear regression model where age is your dependent variable and earn is your independent variable. Answer the following questions about your regression results. earn = 239.16 5.20 × age (20.24) (0.57) Ip = 0.05, SER 287.21 (a) Interpret the coefficient for age. (b) Is the effect of age on earnings economically significant here? (Hint: think about how much...