Write down the econometric model that this OLS regression estimates. Interpret the educ coefficient.  At how many average weekly hours worked is the marginal effect of hours on predicted wage equal to zero? What is the exact effect (in percent) of an increase of 10 years of education on predicted wage? What is the predicted monthly wage in US$ for a worker with 10 years of education, an IQ score of 100, who works on average 40 hours per week?

Suppose you ran a regression of AGE predicting INCOME (thousands of dollars). If your estimated line was INCOME = -1.5 + 1.2*AGE + ε, how much more income do you expect to make for every two years you age? (Express your answer in thousands of dollars.)

A patient is classified as having gestational diabetes if their average glucose level is above 140 milligrams per deciliter (mg/dl) one hour after a sugary drink is ingested. Rebecca's doctor is concerned that she may suffer from gestational diabetes. There is variation both in the actual glucose level and in the blood test that measures the level. Rebecca's measured glucose level one hour after ingesting the sugary drink varies according to the Normal distribution with μ=140+5 mg/dl and σ=5+1 mg/dl. Using the Central Limit Theorem, determine the probability of Rebecca being diagnosed with gestational diabetes if her glucose level is measured: Once? n=5+2 times n=5+4 times Comment on the relationship between the probabilities observed in (a), (b), and (c). Explain, using concepts from lecture why this occurs and what it means in context.

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Consider a linear regression model for the decrease in blood pressure (mmHg) over a four-week period with muy=2.8+0.8x and standard deviation chi=3.2. The explanatory variable x is the number of servings fruits and vegetables in a calorie-controlled diet. Explain clearly what this slope says about the change in the mean of y for a change in x.

Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary = 10,815.11 +2563.46 (Education) +897.49(Experience) Suppose two employees at the company have been working there for five years. One has a bachelor's degree (8 years of education) and one has a master's degree ( 10 years of education). How much more money would we expect the employee with a master's degree to make? Answer Tables Keypac Keyboard Shortcu

8. Suppose the following estimated regression equation was determined to predict salary based on years of experience. Estimated Salary = 27,017.66 +2343.69 (Years of Experience) What is the estimated salary for an employee with 26 years of experience? Answer:

Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion).  Use the horizontal axis for the customer age to graph.  Find the linear regression line that models the data by each type of customer.  Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales.  What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y  / Change in x

Suppose you wanted to test whether or not the payoff to an additional year of education was the same for men and women in the STEM majors. How would you set up your regression analysis in this case

Consider the following estimated regression model relating annual salary to years of education and work experience. Estimated Salary = 10,896.07 + 2755.34 (Education) + 773.89 (Experience) Suppose two employees at the company have been working there for five years. One has a bachelor's degree (8.years of education) and one has a master's degree ( 10 years of education). How much more money would we expect the employee with a master's degree to make?