The index of exposure to radioactive waste and the cancer mortality rates (deaths per 100,000)were recorded for nine different geographic regions. Dependent variable is: Cancer Mortality Rate R-squared = 85.8% S = 14.00993 with 9 - 2 = 7 degrees of freedom \[\begin{array} { l l l l l } \text { Variable } & \text { Coefficient } & \text { SE(Coeff) } & \text { t-ratio } & \text { P-value } \\ \text { Constant } & 114.7156 & 8.045663 & 14.25807 & 0.00000198 \\ \text { Index of Exp } & 9.231456 & 1.418787 & 6.506584 & 0.000332 \end{array}\]

A) Cancer Mortality Rate = 114.7156 + 9.231456 × Index of Exp; The cancer mortality rates in these counties increase by about 9.231456 deaths per 100,000 for each 1 point increase in the index of exposure to radioactive waste. B) Cancer Mortality Rate = 114.7156 + 9.231456 × Index of Exp; The cancer mortality rates in these counties increase by about 114.7156 deaths per 100,000 for each 1 point increase in the index of exposure to radioactive waste. C) Cancer Mortality Rate = 114.7156 × Index of Exp + 9.231456; The cancer mortality rates in these counties increase by about 114.7156 deaths per 100,000 for each 1 point increase in the index of exposure to radioactive waste. D) Cancer Mortality Rate = 114.7156 × Index of Exp + 9.231456; The cancer mortality rates in these counties increase by about 9.231456 deaths per 100,000 for each 1 point increase in the index of exposure to radioactive waste. E) Index of Exp = 114.7156 + 9.231456 × Cancer Mortality Rate; The index of exposure to radioactive waste in these counties increases by about 9.231456 points for every 1 death per 100,000 increase in the cancer mortality rate. A) Cancer Mortality Rate = 114.7156 + 9.231456 × Index of Exp; The cancer mortality rates in these counties increase by about 9.231456 deaths per 100,000 for each 1 point increase in the index of exposure to radioactive waste. B) Cancer Mortality Rate = 114.7156 + 9.231456 × Index of Exp; The cancer mortality rates in these counties increase by about 114.7156 deaths per 100,000 for each 1 point increase in the index of exposure to radioactive waste. C) Cancer Mortality Rate = 114.7156 × Index of Exp + 9.231456; The cancer mortality rates in these counties increase by about 114.7156 deaths per 100,000 for each 1 point increase in the index of exposure to radioactive waste. D) Cancer Mortality Rate = 114.7156 × Index of Exp + 9.231456; The cancer mortality rates in these counties increase by about 9.231456 deaths per 100,000 for each 1 point increase in the index of exposure to radioactive waste. E) Index of Exp = 114.7156 + 9.231456 × Cancer Mortality Rate; The index of exposure to radioactive waste in these counties increases by about 9.231456 points for every 1 death per 100,000 increase in the cancer mortality rate.

Consider a logistic regression model for the probability of a "success",given a quantitative variable X. \[\begin{array} { | l | c | } \hline \text { Term } & \text { Estimate } \\ \hline \text { Intercept } & - 4.00 \\ \hline \mathrm { X } & 0.10 \\ \hline \end{array}\] Use the output above to estimate the probability of a failure when X = 25.

A) 0.1824 B) 0.8176 C) 0.2019 D) 0.7981 E) 0.2231 A) 0.1824 B) 0.8176 C) 0.2019 D) 0.7981 E) 0.2231

Applicants for a particular job,which involves extensive travel in Spanish-speaking countries,must take a proficiency test in Spanish.The sample data were obtained in a study of the relationship between the numbers of years applicants have studied Spanish and their score on the test.Use the regression analysis and summary statistics given below to find a 95% prediction interval for the score of an applicant who has studied Spanish for 2.2 years. \[\begin{array} { l l l l c } \text { Variable } & \text { Count } & \text { Mean } & \text { StdDev } & \text { Range } \\ \text { Years } & 10 & 3.5 & 0.341565 & 3 \\ \text { Score } & 10 & 69.7 & 4.088058 & 41 \end{array}\] Dependent variable is: Score R-squared = 83.0% S = 5.65138 with 10 - 2 = 8 degrees of freedom \[\begin{array} { l l l l l } \text { Variable } & \text { Coefficient } & \text { SE(Coeff) } & \text { t-ratio } & \text { P-value } \\ \text { Constant } & 31.53333 & 6.360418 & 4.957745 & 0.00111 \\ \text { Number of years } & 10.90476 & 1.744054 & 6.252538 & 0.000245 \end{array}\]

A) 43.7 to 67.3 B) 40.9 to 70.2 C) 43.1 to 70.9 D) 51.5 to 59.5 E) 48.9 to 62.2 A) 43.7 to 67.3 B) 40.9 to 70.2 C) 43.1 to 70.9 D) 51.5 to 59.5 E) 48.9 to 62.2

Applicants for a particular job,which involves extensive travel in Spanish-speaking countries,must take a proficiency test in Spanish.The sample data below were obtained in a study of the relationship between the numbers of years applicants have studied Spanish and their score on the test.A 95% confidence interval for the mean score of all applicants who have studied Spanish for 2.5 years was determined to be (53.0,64.6).Give an interpretation of this interval. \[\begin{array} { l l l c c } \text { Variable } & \text { Count } & \text { Mean } & \text { StdDev } & \text { Range } \\ \text { Years } & 10 & 3.5 & 0.341565 & 3 \\ \text { Score } & 10 & 69.7 & 4.088058 & 41 \end{array}\] Dependent variable is: Score R-squared = 83.0% S = 5.65138 with 10 - 2 = 8 degrees of freedom \[\begin{array} { l l l l l } \text { Variable } & \text { Coefficient } & \text { SE(Coeff) } & \text { t-ratio } & \text { P-value } \\ \text { Constant } & 31.53333 & 6.360418 & 4.957745 & 0.00111 \\ \text { Number of years } & 10.90476 & 1.744054 & 6.252538 & 0.000245 \end{array}\]

A) Based on this regression,we are 95% confident that applicants will score between 53.0 and 64.6 points higher on the proficiency test for each additional year spent studying Spanish. B) Based on this regression,we are 95% confident that all applicants who have studied Spanish for 2.5 years will average between 53.0 and 64.6 points on the proficiency test. C) Based on this regression,we are 95% confident an applicant who has studied Spanish for 2.5 years will score between 53.0 and 64.6 points on the proficiency test. D) Based on this regression,95% of all random samples will have an average score between 53.0 and 64.6 points on the proficiency test. E) Based on this regression,we are 95% confident that a randomly selected applicant who has studied Spanish for 2.5 years will score between 53.0 and 64.6 points on the proficiency test. A) Based on this regression,we are 95% confident that applicants will score between 53.0 and 64.6 points higher on the proficiency test for each additional year spent studying Spanish. B) Based on this regression,we are 95% confident that all applicants who have studied Spanish for 2.5 years will average between 53.0 and 64.6 points on the proficiency test. C) Based on this regression,we are 95% confident an applicant who has studied Spanish for 2.5 years will score between 53.0 and 64.6 points on the proficiency test. D) Based on this regression,95% of all random samples will have an average score between 53.0 and 64.6 points on the proficiency test. E) Based on this regression,we are 95% confident that a randomly selected applicant who has studied Spanish for 2.5 years will score between 53.0 and 64.6 points on the proficiency test.

The index of exposure to radioactive waste and the cancer mortality rates (deaths per 100,000)were recorded for nine different geographic regions.Use the regression analysis and summary statistics provided below to determine a 95% prediction interval for the cancer mortality rate of a county whose index of exposure is 5.93. \[\begin{array} { l c l l c } \text { Variable } & \text { Count } & \text { Mean } & \text { StdDev } & \text { Range } \\ \text { Index of Exp } & 9 & 4.617778 & 1.163731 & 10.39 \\ \text { Cancer Mortality Rate } & 9 & 157.3444 & 11.59712 & 96.8 \end{array}\] Dependent variable is: Cancer Mortality Rate R-squared = 85.8% S = 14.00993 with 9 - 2 = 7 degrees of freedom \[\begin{array} { l l l l l } \text { Variable } & \text { Coefficient } & \text { SE(Coeff) } & \text { t-ratio } & \text { P-value } \\ \text { Constant } & 114.7156 & 8.045663 & 14.25807 & 0.00000198 \\ \text { Index of Exp } & 9.231456 & 1.418787 & 6.506584 & 0.000332 \end{array}\]

A) 134.2 to 204.7 deaths per 100,000 B) 135.7 to 203.2 deaths per 100,000 C) 166.1 to 172.8 deaths per 100,000 D) 167.9 to 185.9 deaths per 100,000 E) 157.5 to 181.4 deaths per 100,000 A) 134.2 to 204.7 deaths per 100,000 B) 135.7 to 203.2 deaths per 100,000 C) 166.1 to 172.8 deaths per 100,000 D) 167.9 to 185.9 deaths per 100,000 E) 157.5 to 181.4 deaths per 100,000

The typing speeds (in words per minute)and reading speeds (in words per minute)of nine randomly selected secretaries were recorded.Use the regression analysis and summary statistics provided below to determine a 90% prediction interval for the reading speed of a secretary whose typing speed is 50. \[\begin{array} { l c l l c } \text { Variable } & \text { Count } & \text { Mean } & \text { StdDev } & \text { Range } \\ \text { Typing speed } & 9 & 60.44444 & 3.3587 & 35 \\ \text { Reading speed } & 9 & 501.8889 & 33.42298 & 297 \end{array}\] Dependent variable is: Reading speed R-squared = 12.385% S = 100.3348 with 9 - 2 = 7 degrees of freedom \[\begin{array} { l l l l l } \text { Variable } & \text { Coefficient } & \text { SE(Coeff) } & \text { t-ratio } & \text { P-value } \\ \text { Constant } & 290.2093 & 215.4116 & 1.347231 & 0.219884 \\ \text { Typing speed } & 3.502052 & 3.520579 & 0.994737 & 0.352998 \end{array}\]

A) 371.1 to 559.5 words per minute B) 253.1 to 677.5 words per minute C) 405.9 to 450.9 words per minute D) 257.1 to 673.6 words per minute E) 458.6 to 472.0 words per minute A) 371.1 to 559.5 words per minute B) 253.1 to 677.5 words per minute C) 405.9 to 450.9 words per minute D) 257.1 to 673.6 words per minute E) 458.6 to 472.0 words per minute

Applicants for a particular job,which involves extensive travel in Spanish-speaking countries,must take a proficiency test in Spanish.The sample data below were obtained in a study of the relationship between the numbers of years applicants have studied Spanish and their score on the test.A 95% prediction interval for the score of an applicant who has studied Spanish for 2.6 years was determined to be (45.7,74.0). \[\begin{array} { l l l c c } \text { Variable } & \text { Count } & \text { Mean } & \text { StdDev } & \text { Range } \\ \text { Years } & 10 & 3.5 & 0.341565 & 3 \\ \text { Score } & 10 & 69.7 & 4.088058 & 41 \end{array}\] Dependent variable is: Score R-squared = 83.0% S = 5.65138 with 10 - 2 = 8 degrees of freedom \[\begin{array} { l l l l l } \text { Variable } & \text { Coefficient } & \text { SE(Coeff) } & \text { t-ratio } & \text { P-value } \\ \text { Constant } & 31.53333 & 6.360418 & 4.957745 & 0.00111 \\ \text { Number of years } & 10.90476 & 1.744054 & 6.252538 & 0.000245 \end{array}\]

A) Based on this regression,we are 95% confident that the mean test score for applicants who have studied Spanish for 2.6 years will be between 45.7 and 74.0. B) Based on this regression,we are 95% confident that the mean number of years spent studying Spanish will increase between 45.7 and 74.0 years for each additional point earned on the test. C) Based on this regression,we are 95% confident that the mean test score will increase between 45.7 and 74.0 points for each additional year spent studying Spanish. D) Based on this regression,an applicant who has studied Spanish for 2.6 years will score between 45.7 and 74.0 on the proficiency test,with 95% confidence. E) Based on this regression,we know that 95% of all random samples will have a mean test score between 45.7 and 74.0 points. A) Based on this regression,we are 95% confident that the mean test score for applicants who have studied Spanish for 2.6 years will be between 45.7 and 74.0. B) Based on this regression,we are 95% confident that the mean number of years spent studying Spanish will increase between 45.7 and 74.0 years for each additional point earned on the test. C) Based on this regression,we are 95% confident that the mean test score will increase between 45.7 and 74.0 points for each additional year spent studying Spanish. D) Based on this regression,an applicant who has studied Spanish for 2.6 years will score between 45.7 and 74.0 on the proficiency test,with 95% confidence. E) Based on this regression,we know that 95% of all random samples will have a mean test score between 45.7 and 74.0 points.

The reading speeds (in tens of words per minute)and typing speeds (in words per minute)of nine randomly selected secretaries were recorded. Dependent variable is: Reading speed R-squared = 97.9521% S = 2.094847 with 9 - 2 = 7 degrees of freedom \[\begin{array} { l c l l l } \text { Variable } & \text { Coefficient } & \text { SE(Coeff) } & \text { t-ratio } & \text { P-value } \\ \text { Constant } & - 40.738 & 5.820749 & - 6.99876 & 0.000212 \\ \text { Typing Speed } & 2.260433 & 0.123535 & 18.29785 & 3.61 \times 10 ^ { - 7 } \end{array}\]

A) Reading speed = -40.738 + 2.260433 × Typing speed; We estimate that the reading speeds for these secretaries increase about 2.260433 words per minute for each 10 word per minute increase in their typing speed. B) Typing speed = -40.738 × Reading speed + 2.260433; We estimate that the typing speeds for these secretaries increase about 2.260433 words per minute for each 10 word per minute increase in their reading speed. C) Reading speed = -40.738 + 2.260433 × Typing speed; We estimate that the reading speeds for these secretaries increase about 22.60433 words per minute for each 1 word per minute increase in their typing speed. D) Typing speed = -40.738 + 2.260433 × Reading speed; We estimate that the typing speeds for these secretaries decrease about 40.738 words per minute for each 10 word per minute increase in their reading speed. E) Typing speed = -40.738 × Reading speed + 2.260433; We estimate that the typing speeds for these secretaries decrease about 40.738 words per minute for each 10 word per minute increase in their reading speed. A) Reading speed = -40.738 + 2.260433 × Typing speed; We estimate that the reading speeds for these secretaries increase about 2.260433 words per minute for each 10 word per minute increase in their typing speed. B) Typing speed = -40.738 × Reading speed + 2.260433; We estimate that the typing speeds for these secretaries increase about 2.260433 words per minute for each 10 word per minute increase in their reading speed. C) Reading speed = -40.738 + 2.260433 × Typing speed; We estimate that the reading speeds for these secretaries increase about 22.60433 words per minute for each 1 word per minute increase in their typing speed. D) Typing speed = -40.738 + 2.260433 × Reading speed; We estimate that the typing speeds for these secretaries decrease about 40.738 words per minute for each 10 word per minute increase in their reading speed. E) Typing speed = -40.738 × Reading speed + 2.260433; We estimate that the typing speeds for these secretaries decrease about 40.738 words per minute for each 10 word per minute increase in their reading speed.