Following are measurements of soil concentrations (in mg /kg) of chromium (Cr) and nickel (Ni) at 20 sites in the area of Cleveland, Ohio. These data are taken from the article "Variation in North American Regulatory Guidance for Heavy Metal Surface Soil Contamination at Commercial and Industrial Sites" (A. Jennings and J. Ma, J. Environment Eng, 2007:587-609). Cr: 260 19 36 247 263 319 317 277 319 264 23 29 61 119 33 281 21 35 64 30 Ni: 435 377 359 53 38 38 54 188 397 33 92 490 28 35 799 347 321 32 74 508 (d) Use these to construct comparative boxplots for the two sets of concentrations. (e) Using the boxplots, what differences can be seen between the two sets of concentrations?

1. A study compared the number of tree species in unlogged forest plots to similar plots logged 8 years earlier. Independent random samples of both logged and unlogged plots were analyzed and the number of tree species was recorded. Tables 1 and 2 present the Shapiro-Wilk normality test for both types of forest plots and the Independent Samples t-test results. Use the information presented in these tables to answer the questions. Table 1: Tests of Normality Tests of Normality Kolmogorov-Smirnov Shapiro-Wilk Forest Plot Statistic df Sig. Statistic df Sig. Tree_Species Logged .181 12 .200 .936 12 .444 Unlogged .110 14 .200 .945 14 .480 *. This is a lower bound of the true significance. a. Lilliefors Significance Correction Table 2: Independent Samples Test Independent Samples Test Levene's Test for Equality of Variances t-test for Equality of Means 95% Confidence Interval of the Difference Mean Std. Error F Sig. t df Sig. (2-tailed) Difference Difference Lower Upper Equal variances…

1. Model 1: OLS, using observations 1-706 Dependent variable: RST   Coefficient Std. Error t-ratio p-value   const 3586.38 38.9124 92.17 <0.0001 *** TOTWRK −0.150746 0.0167403 −9.005 <0.0001 ***   Mean dependent var 3266.356 S.D. dependent var 444.4134 Sum squared resid 1.25e+08 S.E. of regression 421.1357 R-squared 0.103287 Adjusted R-squared 0.102014 F(1, 704) 81.08987 P-value(F) 1.99e-1810538.19 Log-likelihood −5267.096 Akaike criterion 10538.19 Schwarz criterion 10547.31 Hannan-Quinn 10541.71 RSTi =3586.38−0.150746 x TOTWRKi , R2=0.103287,SER=421.1357                                     (38.9124) (0.0167403)   Question? A- The researcher claims that the model lacks a fundamental principle, namely, the impact of the worker’s gender on their efficiency. The researcher further claims that Men on average take more resting time than women. To clarify the researcher’s claim, add the binary variable MALE into your model, and write down the estimated results…

In the above plot, a rejection of the null hypothesis that the population regression slope is equal to 0 implies:    a) the correlation coefficient in the population is likely equal to 0 b) the correlation coefficient in the population is likely unequal to 0 c) the standard deviation of height is likely very large d) the standard deviation of weightis likely very small e) the correlation coefficient must be close to 1.0 f) a and c g) b and e h) b, d and e