A)

Consider the FVC dataset. We have fit a linear model of FVC on Weight from this data. Consider a new 12 year old boy who has a weight of 47.3 kg. We have below a 95% confidence interval and a 95% prediction interval based on this new data. Which of the following statements are true about these two intervals. Confidence Interval: (2.826, 3.006) Prediction Interval: (1.983, 3.849) )We are 95% confident that the true mean FVC for all 12 year old boys with weight of 47.3 kg lies between 2.826 and 3.006. O If this boy had an FVC of 2.835, it is not considered unusual. O If this boy had an FVC of 1.983, it is considered unusual. O For any given value of the weight, this 95% prediction interval is narrower than the 95% confidence interval for the same given value. )We are 95% confident that the weight of a 12 year old boy lies between 1.983 and 3.849.We are interested in testing the effectiveness of 4 alternative cancer treatments. To do this, we set up 5 groups. The first group will be the control group which is exposed to standrad chemotherapy. The other four groups will each be put on one of the four alternative treatments. After the treatment period, the difference in cancer cells from before and after is recorded. What would be an appropriate test/model to see if there is any difference in the cancer cells between the treatments? O one-way ANOVA O Multiple linear regression O Simple linear regression O Pearson chi-squared testWe have t a simple linear regression model to some data. Based on the plot given below, we claim that the assumption of constant spread of residuals is reasonable. Is this statement TRUE or FALSE? N m F Ta = :9 I23 3 CE . '1' 5|] l 15f]I 200 Fitted values plot of chunk plot 0 FALSE 0 TRUE Which of the following are assumptions for a simple linear regression model? You may choose more than one answers. O Unequal spread of the points as we go from left to right in residual vs fitted values plot. O As the predictor increases, the response increases O One observation should give information about the other observations. Linearity There is a relationship between the response and predictor variable