Is it possible to have more than one unbiased estimator for an unknown parameter? Or is not possible? How so
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Is it possible to have more than one unbiased estimator for an unknown parameter? Or is not possible? How so?
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- Cardiorespiratory fitness is widely recognized as a major component of overall physical well-being. Direct measurement of maximal oxygen uptake (VO2max) is the single best measure of such fitness, but direct measurement is time-consuming and expensive. It is therefore desirable to have a prediction equation for VO2max in terms of easily obtained quantities. A sample is taken and variables measured are age (years), time necessary to walk 1 mile (mins), and heart rate at the end of the walk (bpm) in addition to the VO2 max uptake. The equation from a multiple regression is (V02) = 0.01*(age) - 0.032*(HR) + 0.002*(time) + 5.305. If a person is 34 years old, has a heart rate of 107 bpm, a walking time of 14 minutes, and a maximum oxygen intake of 21.252 L/min, the residual would be 19.003. What is the best interpretation of this residual? Question 25 options: 1) The heart rate is 19.003 bpm larger than what we would expect. 2) The…3. A psychology professor wants to know if stress in his statistics class is in anyway affected by a student's major and gender. Test to see if a students and major affects their stress level in the statistics course. Psychology Nursing Other 12 16 21 16 14 7 17 18 Male 12 15 22 8 15 23 9 17 18 EX? = 11275 16 20 23 10 17 28 7 21 28 Female 13 16 27 2 21 23 8 17 29 Critical Value=F(A) 3.32, F(B)4.17, F(AB) 3.32 Solve for the appropriate test statistic(s). Fill out a completed ANOVA Table on the answer sheetIn economics we are often faced with causal problems where the endogeneity arises because of simultaneity. A classic example is that if you are interested in estimating a demand curve, the issue is what you observe in the data are equli- birum and prices and quantity which is not only a function of demand but also a function of supply. In this excercise we will generalize the problem of simultaneity bias. Consider a situation where we are interested in the effect of Ti on Yi, i.e. obtaining a consistent estimate of α. But Yi also effects Ti. Let Xi be some exogenous covariates affecting both Yi and Ti. Let us imagine you have an exogenous variable Z which only effects Ti but does not directly effect Yi. In particular, consider the following structural equations: Yi = αTi+Xi′β+ui (1) Ti = ρYi+Xi′γ+Ziδ+νi (2) where E(ui | Xi,Zi) = 0 and E(vi | Xi,Zi) = 0 (a) Show why you cannot you use OLS to estimate α consistently in model 1? (b) Solve for the reduced form equation for Ti…
- 1 b) For any data set, approximately 95% of the observations fall in the interval (T − 2s, T+2s). True FalseWhen should the Empirical Rule be used?Book Pages (x) Price (y) A 500 $7.10 B 700 7.60 C 750 9.10 D 590 6.60 E 560 7.60 F 650 7.10 G 475 5.10 (a) Develop a least squares estimated regression line. (Round your numerical values to three decimal places.) (b) Compute the coefficient of determination. (Round your answer to four decimal places.) Explain its meaning. (Give your answer as a percent. Round your answer to two decimal places.) The value of the coefficient of determination tells us that % of the variability in |--?-- ☑ has been explained by the least squares regression equation.
- based on these findings, at least two hypotheses that you are testing in this project. Your hypotheses can have the same dependent variable, but they should have different independent variables. (Happiness and physical healthThis dataset continues our saga of modeling the price of this popular Honda automobile. The dataset has now been cleaned to remove the columns with the dealership where the car was offered for sale and specific trim. (a) write out your model in econometric notation. Be very precise! (b) using the 93 observations in the dataset, estimate a model where price is a function of age, mileage and trim of the car. Be sure to avoid the dummy variable trap!! Fully report the results of your model. In this case, interpretation of the coefficients on the dummy variables is particularly important. (c) test the hypothesis that the specific trim does not affect the price of a Civic. Be sure to do all parts of the hypothesis test. (please fully describe steps if you are using Excel) Price Years Old KM EX EXT SE Sport Touring 6555 9 290363 0 0 0 0 0 9999 9 142258 0 0 0 0 0 10281 6 132644 0 0 0 0 0 12480 5 167125 0 0 0 0 0 12991 7 57398 0 0 0 0 0 12991 6 93046 0 0 0 0 0 12991…A weight-loss program wants to test how well their program is working. The company selects a simple random sample of 51 individual that have been using their program for 15 months. For each individual person, the company records the individual's weight when they started the program 15 months ago as an x-value. The subject's current weight is recorded as a y-value. Therefore, a data point such as (205, 190) would be for a specific person and it would indicate that the individual started the program weighing 205 pounds and currently weighs 190 pounds. In other words, they lost 15 pounds. When the company performed a regression analysis, they found a correlation coefficient of r = 0.707. This clearly shows there is strong correlation, which got the company excited. However, when they showed their data to a statistics professor, the professor pointed out that correlation was not the right tool to show that their program was effective. Correlation will NOT show whether or not there is…
- Please answer as many as your allowed too. Thank you :) A regression was run to determine if there is a relationship between the happiness index (y) and life expectancy in years of a given country (x).The results of the regression were: ˆyy^=a+bxa=-1.68b=0.168 (a) Write the equation of the Least Squares Regression line of the formˆyy^= + x(b) Which is a possible value for the correlation coefficient, rr? -1.417 1.417 0.702 -0.702 (c) If a country increases its life expectancy, the happiness index will increase decrease (d) If the life expectancy is increased by 0.5 years in a certain country, how much will the happiness index change? Round to two decimal places.(e) Use the regression line to predict the happiness index of a country with a life expectancy of 69 years. Round to two decimal places.Suppose a data file from a certain study has information for the following variables: 1. Gender- (X1) 2. Monthly income in US dollars- (Y) 3. Carownership (own a car/does not own a car)-(X2) 4. Age (X3) From such a hypothetical data set, answer the following questions: a) Suggest how you can define dummy variables for the nominal variables gender, and car ownership b) Suggest the null and alternative hypotheses for a global F-test examining the joint significance of coefficients of the independent variables in the model E(Y)=B0 + B1X1 +B2X2 +B3X3 c) Interpret the parameters BO, B1, B2, and B3 for the model in b) aboveWhen two variables are correlated, can the researcher be sure that one variable causes the other? If YES , why? If NO , why? Provide and give examples where necessary Graphs illustrating the problems and solutions are recommended
