One of the methods forensic investigators use for estimating the age at death of a human, or the age of a living individual (for example for legal reasons or to verify identity) is based on measurement of the biochemical changes in amino acids within the teeth or bones. As bones and teeth age the ratio (R/L) of two amino acids, denoted simply as R and L, increases. The biochemical changes that occur over time in these amino acids follow an exponential law. The age t, in years, can be estimated from (R/L) using the equation t = b0 + b1*LN((1+R/L)/(1-R/L))^(), where LN denotes the natural logarithm (logarithm base e). This Excel file has data on the ratio (R/L) from the teeth of human subjects of known age. Question 1. Use the data to find the least squares intercept b0 and slope b1 in the equation t = b0 + b1*LN((1+R/L)/(1-R/L))^(). intercept b0 slope b1 Question 2. While tending to his flower garden, Sherlock Holmes discovers a body buried in his backyard. Forensic analysis yields the value 0.045 for the ratio (R/L). Use the least squares equation to estimate the age of Sherlock Holmes' surprise garden visitor. years Question 3. Calculate a 95% prediction interval for the age at death of Sherlock Holmes' surprise garden visitor. lower bound upper bound Question 4. Several years ago a man in Sherlock Holmes's neighborhood mysteriously disappeared. At the time of the disappearance the man was 35 years old. Do you think that this is the body of that man? No Yes
Age as a Function of R/L
R/L
LN[(1+(R/L))/(1-(R/L))]
Age(yrs)
0.0253
14
0.026
18
0.0314
21
0.0302
21
0.0343
25
0.0327
25
0.0317
26
0.0312
26
0.0342
26
0.0338
27
0.0325
28
0.0334
28
0.0334
29
0.0333
30
0.0362
33
0.0359
33
0.0401
36
0.0375
36
0.0398
37
0.0424
38
0.0424
38
0.0404
41
0.0489
49
0.0498
53
0.0518
57
0.0526
63
0.0588
69
0.0579
69



Answer :

Obtain the regression equation.

Use MINITAB to analyze the given data.

MINITAB procedure:

Step 1: Choose Stat > Regression > Regression.

Step 2: In Responses, enter the column of Age (yrs).

Step 3: In Predictors, enter the column of LN [ (1 + (R / L)) / (1-(R/L))). Step 4: In Options, enter the value of Prediction interval for new observation as 0.0901.

Step 5: Click OK.

MINITAB output:

Regression Analysis: Age(yrs) versus LN[(1+R/L)}{1-{R/L)}]

The regression equation is Age (yrs) =- 26 \ 809 LN (1+ (R/L))/(1-(R/L))]

Predictor Coef SE Coef

Constant

-26.7252.025P-13.20 31.57 0.000

LNI ( 1 + (R / I) )/ (I - (R / L)) * 1

808.66

25.61

s = 2.41117

R - Sq = 97.54

R - sq(adj) = 97.44

Analysis of Variance

Source MS

5795.7

996.900.000

Regression

15795.7

Residual Error 26 151.2

Total275946.95.8

Predicted Values for New Observations

New Ob s Fit SE Fit95% CI95% PI

146.103 0.565

(44.942, 47.264)

(41.013, 51.193)

Values of Predictors for New Observations

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