If the omitted variable is negatively correlated with the treatment (x1,x2)<0 and has a positive impact on the outcome(b2>0), then the effect of b1 is overestimated or underestimated?
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- Researchers are studying pomegranate’s antioxidant properties to see if it might have any beneficial effects in the treatment of cancer. One such study investigated whether pomegranate fruit extract (PFE) was effective in slowing the growth of prostate cancer tumors. In this study, 24 mice were injected with cancer cells, then the mice were randomly assigned to one of three treatment groups. The data on y = average tumor volume (in mm3) and x = number of days after injection of cancer cells for the mice that received plain drinking water is shown in picture below: a. Find (to three decimal places) zy, and zxzy for the pair (23,580): zy = zxzy = b. Compute Pearson’s sample correlation coefficient for the given data to four decimal places. c. Compute the slope, b, for the least-squares regression line to two decimal places d. Use b to calculate a and write the equation for the least-squares regression line a= y= e. Predict the average tumor volume (y) for a mouse 20 days…Hormone replacement therapy (HRT) is thought to increase the risk of breast cancer. The accompanying data on x = percent of women using HRT and y = breast cancer incidence (cases per 100,000 women) for a region in Germany for 5 years appeared in the paper "Decline in Breast Cancer Incidence after Decrease in Utilization of Hormone Replacement Therapy." The authors of the paper used a simple linear regression model to describe the relationship between HRT use and breast cancer incidence. + ŷ = HRT Use Breast Cancer Incidence 46.30 40.60 39.50 36.60 30.00 USE SALT 103.30 105.00 100.00 93.80 83.50 (a) What is the equation of the estimated regression line? (Round your numerical values to four decimal places.) (b) What is the estimated average change in breast cancer incidence (in cases per 100,000 women) associated with a 1 percentage point increase in HRT use? (Round your answer to four decimal places.) cases per 100,000 women (c) What breast cancer incidence (in cases per 100,000 women)…A study reports data on the effects of the drug tamoxifen on change in the level of cortisol-binding globulin (CBG) of patients during treatment. With age = x and ACBG = y, summary values are n = 26, Ex; = 1620, 2(x₁ - x)² = 3756.96, y₁ = 281.9, (y₁ - y)² = 465.34, and Ex,y; = 16,733. (a) Compute a 90% CI for the true correlation coefficient p. (Round your answers to four decimal places.) (b) Test Ho: p = -0.5 versus H₂: P < -0.5 at level 0.05. Calculate the test statistic and determine the P-value. (Round your test statistic to two decimal places and your P-value to four decimal places.) P-value = State the conclusion in the problem context. O Reject Ho. There is no evidence that p < -0.5. O Fail to reject Ho. There is evidence that p < -0.5. O Reject Ho. There is evidence that p < -0.5. O Fail to reject Ho. There is no evidence that p < -0.5. (c) In a regression analysis of y on x, what proportion of variation in change of cortisol-binding globulin level could be explained by…
- Look at the following regression table where the dependent variable is the demand for illegal massage services in a city in the United States. Specifically, the dependent variable is the number of customers per hour (Models 1 and 2) or per day (Models 3 and 4). (a) Explain why the coefficient for Population/1,000 in Model 2 is very different from the one in Model 4?(b) Can you reject H0 in Model 1 if H0 : βP opulation/1,000 = 0.01, H1 : βPopulation/1,000 6= 0.01, and α = 0.01?Tom has been gathering data concerning the cost of a spa treatment, y', during the before Valentine's Day. The only independent variable that he has considered is the number of minutes, "x," in the treatment. Suppose Tom collects data on the relationship between the number of minutes in a treatment and the resulting cost of we the treatment. Tom finds that the correlation between cost and number of minutes is strong and positive. Therefore, he has performed a linear regression analysis on his data. His results are that the constant "a" is 35, and the coefficient "b1" for the independent variable is 1.3. Which of the following is the correct linear regression equation that would allow Tom to predict the cost of a spa treatment given the number of minutes? Oy = 78x + 35 Oy' = 1.3x + 35 %3D Oy = 78x - 1.3 %3D OY = -1.3x - 35The coefficients in a distributed lag regression of Y on X and its lags can be interpreted as the dynamic causal effects when the time path of X is determined randomly and independently of other factors that influence Y. Explain How?
- You are interested in understanding the impact of Student Teacher Ratios (STR) on Test Scores (i.e., STR is your variable of interest and Test Scores are your outcome variable). Let’s pretend you have 2 models and you must only pick 1. The models along with mock results are in the table below. The coefficients are presented with standard errors in parenthesis. Further assume that the STR coefficient in Model 1 is less biased than the STR coefficient in Model 2. But as you can see, the STR coefficient in Model 2 is more precisely estimated (it’s statistically significant) than the coefficient in Model 1. Which model (Model 1 or Model 2) do you prefer and why. X1 and X2 are different control variables. You do not need to know what they are in order to answer this question – you only need to know that they are different. Y = Test Scores Model 1 Model 2 STR -1.4 (0.9) -3.7** (0.9) X1 5.6** (2.2) X2 4.6** (2.1) Constant 680*** (45)…Suppose that I want to estimate the effect of x₁ on y. Consider the univariate regression line: how to calculate a and b₁ using OLS? y = a + b₁x₁Consider a simple regression Y = B1 + B2 X + u. Suppose we found out that the variance of error term is changing with larger values of X (heteroscedasticity). Show how you overcome the problem of heteroscedasticity by using White’s heteroscedasticity consistent variances (only for variance of the slope estimate). Show and explain.
- I want to solve this. Help me plz.We are researching the charity donation behaviour of Australians. We have the following model: (E1) Donate = β0 + β1Income +β2Avg_Gift + β3Edu + u Where: Donate is a dummy variable equal to 1 if the individual makes a donation in response to a social media campaign by our charity organisation, and 0 otherwise Income is the annual household income Avg_Gift is the average value of past donations made by the individual to our charity Educ is the individual's level of education (in years) Our data is a random sample of the population and we have 1,614 observations. You should assume E [u | Income, Avg_Gift, Educ] = 0. Using the information above, please answer the following 3 questions. [i] Referring to Model (E1) above, interpret the coefficient β1. [ii] You know that this model will suffer from heteroskedasticity. Why is this the case? Explain your reasoning. [iii] In your own words, what is heteroskedasticity?
