Under the assumptions MLR.1-4 our parameter estimates from multiple OLS regression are...     Unbiased, and our standard inference statistics are all correct.     Unbiased, and our standard inference statistics need not be correct.     Biased, but our standard inference statistics are all correct.     Biased, and our standard inference statistics need not be correct

You may need to use the appropriate technology to answer this question. The following data are from a completely randomized design. In the following calculations, use a = 0.05. Treatment Treatment Treatment 3 64 82 69 47 71 53 54 87 60 35 64 46 50 76 57 148.67 108.67 96.67 (a) Use analysis of variance to test for a significant difference among the means of the three treatments.

Part A of question one asks for the variance (sigma squared), not the equation of regression line. Is the variance 2.79 as I listed on my answer sheet? Maybe my image didn't go through, but I already had the answer to subparts B and C to the question, which you list as #2 and #3. But I need help with parts D, E, and F. I have the following answers for D.  d. Y hat (fitted value = 426.46), with residual of -1.62. e. r squared value = 1.00 g. 99% confidence interval = (-11.63, -1.05) Thanks thanks

Scenario: If 20 participants completed an attention task twice — once after consuming a sugar pill and once after consuming a pill that contained 25 mg nicotine, which test would be most appropriate? Assume data meets all assumptions for a parametric test, and that participant exposure to the sugar pill vs. nicotine pill were counterbalanced. Question: Which of the following best describes the research design? Descriptive Predictive Quasi-experimental Experimental

The following data are from a completely randomized design. In the following calculations, use a = 0.05. Treatment Treatment Treatment 2 3 64 82 69 47 71 53 54 87 60 35 64 46 50 76 57 2 148.67 108.67 96.67

An administrator wants to investigate the relationship between the numbers of unauthorized days that employees are absent per year and the distance (miles) between home and work for the employees.  A sample of 10 employees was chosen, and the following data were collected. Distance to Work (miles) 2 4 6 6 9 8 10 12 12 15 Number of Days Absent 8 5 8 6 6 4 6 3 5 3 Develop the least square regression line to predict the number of days absent based on the distance to work. Enter the regression coefficients in xx.xxx format.  Round the value to three decimals and use leading and trailing zero to exactly match the format.  Include the negative sign (minus sign) if the coefficient is negative. Do not include plus sign in your response.  For example, if the coefficient is +6.1563 then enter 06.156 as your answer, if the coefficient is -0.54765 then enter -00.548 and if the answer is -1.6435 then enter -01.644 Ŷ=b0+b1X Constant/intercept…