ECMT Submission 3

Note:- Do not provide handwritten solution. Maintain accuracy and quality in your answer. Take care of plagiarism. Answer completely. You will get up vote for sure.

Please don't copy others answer it's wrong . Give me correct answer i will upvote you

Literacy rate reflects the educational facilities and quality of education available in a country, and mass communication plays a large part in the educational process. To relate the literacy rate of a country to various mass communication outlets, a demographer has proposed to relate literacy rate to the following variables: News = number of daily newspaper copies (per 1000 population) Radio = number of radios (per 1000 population) TV = number of TV sets (per 1000 population). The regression model to estimate the literacy rate (?̂ ) is below: ?̂= 0.5149 + 0.0005*News - 0.0003* TV + 0.002* Radio Answer the following questions: 4.1) Interpret the coefficient value of the TV variable in the model. 4.2) Predict the literacy rate for a country that has 200 daily newspaper copies (per 1000 in the population), 800 radios (per 1000 in the population), and 250 TV sets (per 1000 in the population). Show a detailed solution to support your answer.

Suppose a national survey of women was conducted in the years 1972, 1974, 1976, 1978, 1980, 1982, and 1984. Suppose the survey data from each year is pooled to create a pooled cross-sectional data set consisting of 13,000 observations. A researcher wants to use these data to estimate a multiple linear regression model using OLS to explain the number of children born to a woman during this time period. To do this, she regressed number of kids born to a woman on education, age, ethnicity, and dummy variables for the years 1974, 1976, 1978, 1980, 1982, and 1984. For example, if an observation comes from the year 1974, y74 would equal 1, while the remaining dummy variables for other years would equal 0. The regression output is provided below with corresponding standard errors in parentheses: kids = -7.731+ 0.19y74 - 0.09y76 - 0.06y78 – 0.08y80 – 0.42y82 – 0.65y84 – 0.135educ + 0.277age - 0.0055age? +0.88black (3.101) (0.171) (0.171) (0.182) (0.188) (0.175) (0.173) (0.20) (0.111) (0.001)…

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.].

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…

A guidance counselor wants to determine if there is a relationship between a student's number of absences, x, and their grade point average (GPA), y. The data that were collected are displayed in the scatterplot and the least-squares regression line was calculated. One student with 2 absences has a GPA of 1.8. This point is circled on the graph. GPA and Absences 4.8 4.4 4.0 3.6 3.2 2.8 2.4 2.0 1.6 4 6 8 10 12 14 16 Absences (Days) What effect does the circled point have on the standard deviation of the residuals? This point will increase the value of the standard deviation of the residuals because it has a large positive residual. This point will increase the value of the standard deviation of the residuals because it has a large negative residual. This point will not affect the value of the standard deviation of the residuals because it has a large positive residual. This point will decrease the value of the standard deviation of the residuals because it has a large negative residual.…

Consider the following single variate model (1) fare Bo + B₁dist + u =

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?

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 fast

Mr. John operates a medium size business that sells tires. He buys most of his tires from a company that is located in South America. Mr. John believes that he is stocking too much tires so he decided to look into the situation. He wants to use the Economic Order Quantity (EOQ) model to manage his stock of tires. In order to use this model, he must first of all forecast the annual demand for his tires. Using a numerical example, demonstrate to Mr. John how he can use the manual trend projection method of forecasting to forecast demand for the next two years.

We are studying the factors that contribute to unemployment at an individual level. UE, unemployment, is our dependent variable and it is a binary variable that takes the value 1 if an individual is unemployed and 0 if they are employed. We have a random sample and we estimate the following model: UE^= 0.508−0.051educ−0.023urban+0.005age(0.122)  (0.012)          (0.005)            (0.002)n=6214,  R2=0.474 where educ = an individual’s years of education urban = a dummy equal to 1 if the individual lives in an urban area and 0 otherwise age = an individual’s age in years What is the correct interpretation of the estimated coefficient on education?

Sherwin-Williams Company is attempting to develop a demand model for its line of exterior house paints. The company’s chief economist feels that the most important variable affecting paint sales (Q) (measured in gallons) is the Selling price (P) (measured in Ghana cedis per gallon). The chief economist decides to collect data on the variables in a sample of 10 company sales regions that are roughly equal in population. Data on paint sales, and selling prices were obtained from the company’s marketing department. The data are shown in the table below:     Sherwin-Williams Company Data Sales Region Sales (Q) Selling Price (P) (GHS/Gallon) 1 160 15       2 220 13.5 3 140 16.5 4 190 14.5 5 130 17 6 160 16 7 200 13 8 150 18 9 210 12 10 190 15.5                                       Specify the linear demand model for Sherwin-William’s paint.                          Estimate the demand…

PLEASE ANSWER CORRECTLY FULLY  MAKE SURE THE ANSWER IS 100% RIGHT  NO MISTAKES PLEASE

E3

(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…

Q.3. A random sample of ten families had the following income and food expenditure Families A B C D E F G H I J Income 18 28 31 38 15 13 24 36 33 40 Food Expenditure 7 10 8 10 6 4 7 10 9 10 Estimate the regression line of food expenditure on income.

Using a sample from a population of adults, to estimate the effects of education on health, we run the following regression: hypertension, = a + Beduc; + YX¡ + Ei where hypertension is a dummy variable equals one if a person suffers from hypertension and zero otherwise, educ is years of schooling, and X is a vector of demographic variables such as age, gender, and ethnicity. (a) Show that educ in the regression above is likely to be endogenous and discuss the consequences of this on the OLS estimators. (b) Evaluate whether a government policy that requires children to complete twelve years of schooling is a good instrumental variable for educ.

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. What is the most precide interpretation of the estimated coefficient for sweetener?

46) The following model is a simplified version of the multiple regression model used by Biddle and Hamermesh (1990) to study the trade-off between time spent sleeping and working and to look at other factors affecting sleep: sleep = Bo + B₁totwrk + ß₂educ + ß3age +u, where sleep and totwrk (total work) are measured in minutes per week and educ and age are measured in years. (i) If adults trade off sleep for work, what is the sign of f₁? (ii) What signs do you think ₂ and 3 will have? (iii) Using the data in SLEEP75, the estimated equation is sleep = 6,241.15 + 0.211totwrk + 9.22educ + 1.67age n = 211, R² = 0.981 If someone works five more hours per week, by how many minutes is sleep predicted to fall? Is this a large tradeoff? (iv) Discuss the sign and magnitude of the estimated coefficient on educ. (v) Would you say totwrk, educ, and age explain much of the variation in sleep? What other factors might affect the time spent sleeping? Are these likely to be correlated with totwrk?

determine the regression line equation plot the line on a graph and summarize the results( reject or do not) is there enough evidence?

QUESTION 4 In order to estimate the effect on wage of years of schooling, researcher B analysed the dataset that researcher A collected in Question 3. Researcher B considers the following regression equation log (income); = Bo+B₁educ; + • + Ui where educ; is years of schooling for individual and includes additional terms associated with other characteristics in the dataset. When research B was carefully examining omitted factors in the error term U¡, she realised that unobserved factors such as motivation, and work ethic; would determine the income level and be correlated with the variable of interest, educ¡. If researcher B's conjecture regarding the unobserved factors is correct, then: Hint: see page 1 to page 13 of Lecture Note 4. a. The OLS estimator B₁ would not be computed. b. The OLS estimator B₁ would be biased. C. The OLS estimator B₁ would suffer imperfect multicollinearity. d. The OLS estimator B₁ would have a large standard error.

Below is a generic graph that epitomizes one of the three basic natural (or quasi) experiment methods we discussed. Which method is this graph illustrating? 2 4 8 10 time difference-in-difference (diff-in-diff) randomized experiment Regression discontinuity design (RDD) Instrumental variables (IV) 1.4 1.5 1.7 1.8 6

5

In a study of housing demand, the county assessor develops the following regression model to estimate the market value (i.e., selling price) of residential property within her jurisdiction. The assessor suspects that important variables affecting selling price (Y, measured in thousands of dollars) are the size of a house (X1, measured in hundreds of square feet), the total number of rooms (X2), age (X3), and whether or not the house has an attached garage (X₁ No = 0, Yes = 1). Y = a+B₁X₁+ẞ₁₂X₂+ß₂X3+ß«X₁+ε Now suppose that the estimate of the model produces following results: a = 166.048, b₁ = 3.459, b₂ = 8.015, b = -0.319, b₁ = 1.186. 8b1 = 1.079, 862 = 5.288. 863=0.789, 864 = 12.252, R² = 0.838. F-statistic = 12.919, and se= 13.702. Note that the sample consists of 15 randomly selected observations. According to the estimated model, holding all else constant, an additional room means the market value by approximately Which of the independent variables (if any) appears to be…

States (and provinces) that have control over taxation sometimes reduce taxes in an attempt to spur economic growth. Suppose that you are hired by a state to estimate the effect of corporate tax rates on, say, the growth in per capita gross state product (GSP).(i) What kind of data would you need to collect to undertake a statistical analysis?(ii) Is it feasible to do a controlled experiment? What would be required?(iii) Is a correlation analysis between GSP growth and tax rates likely to be convincing? Explain.

How is imperfect collinearity of regressors different from perfect collinearity?Compare the solutions for these two concerns with multiple regressionestimation.

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) Name two irrelevant explanatory variables that should not be included in the regression. Note that these two variables currently should NOT feature in the regression.

help please answer in text form with proper workings and explanation for each and every part and steps with concept and introduction no AI no copy paste remember answer must be in proper format with all working