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For each of the following situations choose the statistical model that you find to be the most appropriate. Justify your choice.
a) We are interested in assessing the effects of temperature (low, medium, and high) and technical configuration on the amount of waste output for a manufacturing plant. Suppose that we have randomly selected 5 technical configurations out of 100 configurations, and we want our conclusions to apply to all 100 configurations.
b) We are evaluating the association between the presence of a very rare adverse event (yes, no) and the treatment received (placebo, new drug) for a group of 100 patients. We want to be able to control for age (in years).
c) We are evaluating the effectiveness of five different diets with respect to weight loss. Fifty women were randomly assigned to each one of the five different diet regimens, and the weight loss during a one year period was recorded.
d) We are interested in evaluating the relationship between the levels of C-reactive protein and the body mass index for a sample of 1000 middle aged African-American women.
e) We are interested in comparing (on average) the housing prices (in thousands of dollars) for five specific locations within the same city. To allow for fair comparisons we will take into account the size of the house (in square feet) and the age of the house (in years).
Analysis of covariance (ANCOVA) It is initially used for an expansion of the analysis of variance which permits to the possible effects of continuous concomitant variables (suc
X 110 120 130 120 140 135 155 160 165 155 Y 12 18 20 15 25 30 35 20 25 10
1. Recognize and explain the opportunities for statistical learning. 2. Describe how the use of statistics supports student learning. 3. Recognize appropriate data displays a
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Pattie-Lynn's utility function for total assets is, in which A represents total assets in thousands of dollars. (a) Graph Pattie-Lynn's utility function. How would y
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As one of the oldest multivariate statistical methods of data reduction, Principal Component Analysis (PCA)simplifies a dataset by producing a small number of derived
Flow Chart for Confidence Interval We can now prepare a flow chart for estimating a confidence interval for μ, the population parameter. Figure
In a management class of 100 childerns' 3 languages are offered as an additional subject viz. Hindi, English and Kannada. There are 28 childrens taking Hindi, 26 taking Hindi and 1
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