Suppose you run the following regression: outcome=alpha0 + alpha1*female + alpha2*married + epsilon. You know that female equals 1 for females and 0 otherwise. You know that married equals 1 if the person is married and 0 otherwise. What is the estimated outcome for married respondents who are not female?

The data for this question is given in the file 1.Q1.xlsx(see image)  and it refers to data for some cities X1 = total overall reported crime rate per 1 million residents X3 = annual police funding in $/resident X7 = % of people 25 years+ with at least 4 years of college   (a) Estimate a regression with X1 as the dependent variable and X3 and X7 as the independent variables. (b) Will additional education help to reduce total overall crime (lead to a statistically significant reduction in crime)? Please explain. (c) Will an increase in funding for the police departments help reduce total overall crime (lead to a statistically significant reduction in total overall crime)? Please explain. (d) If you were asked to recommend a policy to reduce crime, then, based only on the above regression results, would you choose to invest in education (local schools) or in additional funding for the police? Please explain.

Suppose you work for North Dakota DNR Grand Forks office. DNR would like to know whether they should set aside some conservation land, previously slated to be logged, for a potential state park. You are helping do a travel-cost analysis to estimate the benefits of the set-aside. You collected data from 500 visitors who came to a state park in a neighboring state. You ran regression analysis and controlled for these visitors' age, income, education, employment status, and other important factors that might affect the number of visits. With all the information, you have developed the following relationship: (a) (b) Cost to Visit $20 $40 $80 # of Visits Per Person Per Year 8 6 2 Graph the demand curve for the number of visits as a function of the "price" -- the travel cost. Based on demographic information about the people living in the vicinity of the proposed park, you have estimated that 10,000 people will take an average of 4 visits per year. For the average person, calculate: (1) The…

Mita, the manufacturer of copiers, has been spending increasing amounts of money on radio and television advertising in recent years. An analyst employed by Mita wanted to estimate a simple linear regression of the company's annual copier sales versus advertising dollars. Th regression results included SSE = 12593 and SSR = 87663. What is the coefficient of determination for this regression? 0.874 0.935 0.144 0.126

Problem 5. Based on a sample of 21 Monitoring the Future respondents (see table below), we present their racial/ethnic background and the number of school days missed in the past 4 weeks.   White Black Hispanic 4 1 4 5 2 3 3 2 5 4 1 1 4 3 5 4 4 2 6 3 2     Complete the 5-step model for these data, and set alpha at 0.05. If alpha were set at 0.01, would your decision change? Explain.

In the regression equation, what is B0? Group of answer choices the population slope the sample y-intercept the sample slope the population y-intercept

please answer in text form and in proper format answer with must explanation , calculation for each part and steps clearly

1. Consider the following regression model y = x3 + u. (1) Let 3 denote the Ordinary Least Squares (OLS) estimator of B. The so-called Gauss- Markov assumptions are: • MLR.1: The true model in the population is given by (1). • MLR.2: We have a random sample of n observations {(ri, Yi), i = 1, 2, .., n} following the population model in (1). .... • MLR.3: No one explanatory variable can be written as a linear combination of the remaining explanatory variables that is, there is no perfectcollinearity. • MLR.4: In the population, the error u has an expected value of zero given any values of the explanatory variables, that is Elu|x] = 0. • MLR.5: In the population, the error u has the same variance given any values of the explanatory variables, that is Var[u|x] = o? , an unknown finite, positive constant. In the following scenarios, state whether B is an unbiased and consistent estimator of 3, and provide a brief justification for your answer in each case - but no formal mathematical…

Consider the population regression of log earnings [Y;, where Y,= In(Earnings,;)] against two binary variables: whether a worker is married (D₁, where D₁;= 1 if the th person is married) and the worker's gender (D2;, where D₂;= 1 if the th person is female), and the product of the two binary variables Y₁ = Po+B₁D₁+P₂D2i + P3 (D₁¡ × D₂i) + Hi- The interaction term: O A. indicates the effect of being married on log earnings. B. does not make sense since it could be zero for married males. C. allows the population effect on log earnings of being married to depend on gender. D. cannot be estimated without the presence of a continuous variable.

Write first-order model relating E(y) to four quantitative independent variables.