Consider a regression in which a coefficient suffers from downward omitted variable bias. If you add a regressor that controls for the omitted variable bias: Group of answer choices
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- Describe the “overall” regression F-statistic & F-statistic when q=1?Suppose you examined blood of 36 patients with the aim to study the relation between sugar level in blood (in mg/dL) and the amount of artificial sweetener (measured in grams). Your regression shows: blood=7.1 + 0.4*sweatener - 0.2*female. a)What is the most precide interpretation of the estimated coefficient for sweetener? b)What is the most precide interpretation of the estimated coefficient for the constant?(Don't accept answers from Chat-GPT)You are estimating the following simple linear regression model: Edui = B0 + B1 MomEdu + ui. Where Edu is the years of schooling of an individual and MomEdu is the years of education of the individual's mother (Note: We might estimate this sort of regression to learn about intergenerational transmission of economic success.) a. Suppose you restrict your sample to individuals with MomEdui = 10 What happens to the OLS estimates? b. Suppose you have two random samples of size 100, both with the same In the first sample, half of the mothers have 12 yearsof education and half have 14 years of education. In the second sample, one quarter of of the mothers have each of 10, 12, 14. and 16 years of education. Does the variance of the OLS estimator differ between the two samples? Explain why or why not. C. Suppose you estimate the above regression using a random sample of 100 observations. Then you find another random sample of 100 with the same as the…
- Investigate what factors determine the number of times a person logs into Facebook per week. It is argued that these four factors are important: number of friends, age in years, whether the person is employed, and whether the student has a Twitter account. That is: FACEBOOK LOGIN=f(FRIENDS,AGE,EMPLOYED,TWITTER) Do you think other relevant explanatory variables should also be included? Name any two such variables and explain why they should be included in the regression.Consider the IV regression model Yi = β0 + β1Xi + β2Wi + ui, where Xi is correlated with ui and Zi is an instrument. Suppose that the first three assumptions in Key Concept (The IV Regression Assumptions) are satisfied. Which IV assumption is not satisfied whena) Zi is independent of (Yi, Xi, Wi)?b) Zi=Wi?c) Wi is1 for all i?d) Zi=Xi?Explain Distribution of Regression Statistics with Normal Errors?
- In multiple OLS regressions, if you are using power terms to fit for nonlinearity, how do you interpret the coefficients? For example: Yi=B1+B2X+B3X^2+Ui and B2 and B3 are both significant.The following regression was run using a sample of 587 women living in Kenya. The variable goats is the number of goats the woman owns and grant is whether the woman received $ 500 in cash (in local currency equivalent) two years ago from the charity GiveDirectly, or not. The charity gives grants randomly to poor people in Kenya, of $ 500. goats = 6.42 +0.80 grant R2=0.35 1. What is the estimated coefficient for grant? What does it mean, in words? (Be specific, referring to numbers.) 2. Suppose one woman in the sample, Wangari, did not receive a grant. What is her predicted number of goats? Please do fast ASAP fastA specialist in hospital administration stated that no. of employees in a hospital can be estimated by counting the no. of beds in the hospital .A health care researcher decided to develop a regression model in an attempt to predict the number of employees of a hospital by the no. of beds . 12 hospitals were surveyed and obtained following data. No of Beds. Employees 23 69 29 95 29 102 35 118 42 126 46 125 50 138 54 178 64 156 66 184 76 176 78 225 After obtaining the regression model. Interpret it accordingly based on your understanding .
- Explain Conditional Mean Independence by considering a regression with two regressors?Consider the regression model Yi = b0 + b1X1i + b2X2i + ui. Use approach 2from Section 7.3 to transform the regression so that you can use a t-statistic to testa. b1 = b2.b. b1 + 2b2 = 0.c. b1 + b2 = 1. (Hint: You must redefine the dependent variable in theregression.)We know that discrimination exists. It influences wages, but also many other dimensions over the life cycle which affect wages indirectly. I run OLS regression with variables wage, age, female and degree. The dependent variable is log(wage) and we replace the variables female and degree with the interaction term. However, discrimination is not included among the observed regressors. Given that omitting confounding variables from regression model can bias the coefficient estimates, omitting discrimination would lead to biased results. Could you please help me provide an example of how unobserved gender discrimination can affect my OLS estimates. [Hint: think about ways in which discrimination can invalidate OLS assumptions.].