One of the tasks that forensic scientists face is estimating the age of an unidentified crime victim. Prior to 2010, this was usually done by analyzing teeth and bones, and the resulting estimates were not very reliable. In a groundbreaking study, scientists examined the relationship between age and a blood test measure. They recorded age and the blood test measure for 195 people ranging in age from 7 to 80 years. The scientists were interested in predicting age using the blood test measure. The following sample of 15 people will be used in this problem Blood test measure Age -8 19 -6 12 -11 42 -8 22 -13 58 -15 77 -14 70 -6 -12 45 -10 37 -9 35 -7 20 -9 28 -13 65 -12 75 4. Construct a residual plot and comment on any unusual features of the residual plot. (inciuding influential observations and outliers, unusual patterns) 5. Calculate and interpret the value of r² in context of this study. 6. Explain why it would also be informative to look at the value of s, in addition to the value of r? when evaluating the usefulness of the least squares regression line. 7. Calculate and interpret the value of se. 8. Based on your answers to parts 4 to 7, do you think it is appropriate to use the least squares regression line to predict the age of a victim using the blood test measure? Explain. 9. Two unidentified crime victims were found. The values of blood test measure are determined to be -10 for the first victim and -4 for the second victim. Predict (if possible) the age of the victims. Explain why if not.

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Contingency Table

A contingency table can be defined as the visual representation of the relationship between two or more categorical variables that can be evaluated and registered. It is a categorical version of the scatterplot, which is used to investigate the linear relationship between two variables. A contingency table is indeed a type of frequency distribution table that displays two variables at the same time.

Binomial Distribution

Binomial is an algebraic expression of the sum or the difference of two terms. Before knowing about binomial distribution, we must know about the binomial theorem.