Question 1:

Fill in the blank. Two samples are if the sample values are paired. Two samples are E if the sample values are paired. dependent unusual independent disjoint Determine the test statistic. The test statistic is t= . (Round to two decimal places as needed.) Determine the P-value. The P-value is . (Round to three decimal places as needed.) Since the P-value of the linear correlation coefficient is the significance level, there V sufficient evidence to the claim that there is V linear correlation between the subway fare and the price of a pizza slice. Based on these results, does it appear that a slice of pizza can be used to estimate the cost of the subway fare? O A. Yes, because the price of the subway and a slice of pizza appear to be correlated. O B. No, because the price of the subway and a slice of pizza do not appear to be correlated. O C. No, because the price of the subway and a slice of pizza appear to be correlated. O D. Yes, because the price of the subway and a slice of pizza do not appear to be correlated.Which of the following is not equivalent to the other three? Choose the correct answer below. Predictor variable Dependent variable Explanatory variable 0000 Independent variable Use the given data to nd the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. x 11 9 14 6 7 10 13 5 8 4 12 E1 y14.56 13.48 13.54 9.18 10.96 14.20 14.24 7.04 12.40 4.54 14.58 = + x (Round to two decimal places as needed.) Create a scatterplot of the data. Choose the correct graph below. O A. O B. 2 v Q 25 v Q 20 Q 20 Q 1 15 10 I3 10 {E41 5 5 .7, 0 0 0 5 1015 20 25 0 5 1015 20 25 Identify a characteristic of the data that is ignored by the regression line. 0 A. There is no trend in the data. 0 B. The data has a pattern that is not a straight line. 0 C. There is an inuential point that strongly affects the graph of the regression line. 0 D. There is no characteristic of the data that is ignored by the regression line. 25 20 15 10 5 0 OC. 0 510152025 25 20 15 10 5 0 OD. x 0 510152025 '3' 9 PE Listed below are the heights (cm) of winning presidential candidates and their main opponents from several recent presidential elections. Find the regression equation, letting president be the predictor (x) variable. Find the best predicted height of an opponent given that the president had a height of 183 cm. How close is the result to the actual opponent height of 182 cm? President 178 175 185 177 183 188 188 183 191 Q Opponent 180 173 177 183 182 173 175 185 169 The regression equation is = + x. (Round the y-intercept to the nearest integer as needed. Round the slope to three decimal places as needed.) The best predicted height of an opponent given that the president had a height of 183 cm is om. (Round to one decimal place as needed.) How close is the result to the actual opponent height of 182 cm? 0 A. The result is more than 5 cm less than the actual opponent height of 182 cm. 0 B. The result is within 5 cm of the actual opponent height of 182 cm. 0 c. The result is more than 5 cm greater than the actual opponent height of 182 cm. O D. The result is exactly the same as the actual opponent height of 182 cm. Researchers conducted trials to investigate the effects of color on creativity. Subjects with a red background were asked to think of creative uses for a brick; other subjects with a blue background were given the same task. Responses were scored by a panel of judges and results from scores of creativity are given in the accompanying table. Higher scores correspond to more creativity. The researchers make the claim that "blue enhances performance on a creative task." Assume that the two samples are independent simple random samples selected from normally distributed populations, and do not assume that the population standard deviations are equal. Complete parts (a) and (b). Click the icon to view the summary statistics. - X a. Use a 0.01 significance level to test the claim that blue enhances performance on a creative task. Response Summary Statistics What are the null and alternative hypotheses? OA. HO: M1 = H2 OB. HO: H1 2 H2 Background n X S Hy: My * H2 H1: My H2 H1: My "2 474.4 129.1 328.6 94.6 1' "1 ' \"2 1' \"1 "2 401.5 232.6 914.7 100.4 552.8 162.6 Calculate the test statistic. t= (Round to two decimal places as needed.) Find the P-value. P-value = (Round to three decimal places as needed.) Make a conclusion about the null hypothesis and a nal conclusion that addresses the original claim. Use a signicance level of 0.01. V \"0 because the P-value is the signicance level. There V sufcient evidence to warrant V the claim that cars in two queues have a mean waiting time equal to that of cars in a single queue. b. Construct the condence interval suitable for testing the claim in part (a). H2 H1: H1 > H2 O C. HO: M1 = H2 OD. HO: My # H2 H1: My # H2 H1 : H1 > H2 The test statistic is . (Round to two decimal places as needed.) The P-value is . (Round to three decimal places as needed.) State the conclusion for the test. A. Reject the null hypothesis. There is not sufficient evidence to support the claim that subjects with low lead levels have higher IQ scores. O B. Fail to reject the null hypothesis. There is sufficient evidence to support the claim that subjects with low lead levels have higher IQ scores. O C. Reject the null hypothesis. There is sufficient evidence to support the claim that subjects with low lead levels have higher IQ scores. O D. Fail to reject the null hypothesis. There is not sufficient evidence to support the claim that subjects with low lead levels have higher IQ scores. b. Construct a confidence interval appropriate for the hypothesis test in part (a). 1