Statistics students in Oxnard College sampled 9 textbooks in the Condor bookstore and recorded the number of pages in each textbook and its cost. The bivariate data are shown below: Number of Pages (xx) Cost(yy) 364 44.12 364 50.12 648 68.84 432 57.56 387 44.96 300 35 562 65.96 675 69 218 29.44

Listed below are annual data for various years. The data are weights (metric tons) of imported lemons and car crash fatality rates per 100,000 population. Construct a scatterplot, find the value of the line= correlation coefficient r, and find the P-value using a = 0.05, Is there sufficient evidence to conclude that there is a linear correlation between lemon imports and crash fatality rates? Do the results suggest imported lemons cause car fatalities? Lemon Imports Crash Fatality Rate 228 15.9 265 357 483 531 15.6 15.4 15.2 14.9 200 400 600 200 400 600 200 400 600 200 400 600 The linear correlation coefficient is r= (Round to three decimal places as needed.) The test statistic is t= (Round to three decimal places as needed.) The P-value is (Round to three decimal places as needed.) Because the P-value is than the significance level 0.05, there sufficient evidence to support the claim that there is a linear correlation between lemon imports and crash fatality rates for a

The data shown below consists of the price (in dollars) of 7 events at a local venue and the number of people who attended. Determine if there is significant  linear correlation between ticket price and number of attendees. Use a significance level of 0.01 and round all values to 4 decimal places.   Ticket Price Attendence 6 176 10 111 14 187 18 102 22 190 26 188 30 110   Ho: ρ = 0Ha: ρ ≠ 0   Find the p-value=

A state's park system statistical report for the 2014/2015 fiscal year gave the accompanying data on x = amount of money collected in user fees (in thousands of dollars) and y = operating cost (in thousands of dollars) for nine state parks in a certain district. User Fees (thousands of dollars) Operating Costs (thousands of dollars) 17 99 74 363 811 3,618 380 1,377 33 241 427 768 500 1,034 734 2,205 760 1,620 (a) Construct a scatterplot of the data. A scatterplot has 9 points. The horizontal axis is labeled "x" and ranges from approximately 0 to 900. The vertical axis is labeled "y" and ranges from 0 to 4,000. The points are plotted from left to right in a horizontal band, starting from the middle left of the diagram. The points are somewhat scattered, becoming more scattered from left to right, and are between the approximate horizontal axis values of 10 and 820 and between the approximate vertical axis values of 100 and 2,000. A scatterplot has 9 points. The horizontal axis is…

Report the correlations between the three independent variables (age, educ and Protestant) and your dependent variable (childs). Which category had the correlation that was the weakest?

The Minitab output shown below was obtained by using paired data consisting of weights (in Ib) of 32 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4500 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 32 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? Click the icon to view the Minitab display. Minitab output The linear correlation coefficient is (Round to three decimal places as needed.) The regression equation is Highway = 50.9- 0.00539 Weight Predictor Сoef SE Coef P Constant 50.856 2.856 17.48 0.000 Weight - 0.0053916 0.0007634 - 7.59 0.000 S= 2.10918 R-Sq = 64.2% R-…

A random sample of 7 countries gave the following figures for X = annual per capita income and Y = urbanization rate (percentage of people living in urban area): Country X (US dollars) Y (%) 1 3500 20 2 2700 24 3 2100 16 4 2400 19 5 3000 26 6 2000 19 7 1900 17 Calculate the correlation coefficient for the sample. Explain the results At the 5% significant level, test whether per capita income and urbanization rate are unrelated.

The Minitab output shown below was obtained by using paired data consisting of weights (in Ib) of 27 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4500 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 27 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? Click the icon to view the Minitab display. Minitab output The linear correlation coefficient is. (Round to three decimal places as needed.) The regression equation is Highway = 50.4- 0.00535 Weight %3D Predictor Сoef SE Coef T Constant 50.383 2.732 17.75 0.000 Weight - 0.0053502 0.0007856 -7.55 0.000 S= 2.17876 R-Sq = 65.1% R-…

The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 31 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 4000 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 31 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? Click the icon to view the Minitab display. The linear correlation coefficient is (Round to three decimal places as needed.) Is there sufficient evidence to support a claim of linear correlation? Yes O No Minitab output The regression equation is Highway = 50.8 -0.00508 Weight Predictor Coef SE Coef T P Constant 50.772 2.793…

The Minitab output shown below was obtained by using paired data consisting of weights (in lb) of 26 cars and their highway fuel consumption amounts (in mi/gal). Along with the paired sample data, Minitab was also given a car weight of 3000 lb to be used for predicting the highway fuel consumption amount. Use the information provided in the display to determine the value of the linear correlation coefficient. (Be careful to correctly identify the sign of the correlation coefficient.) Given that there are 26 pairs of data, is there sufficient evidence to support a claim of linear correlation between the weights of cars and their highway fuel consumption amounts? Click the icon to view the Minitab display. The linear correlation coefficient is (Round to three decimal places as needed.) Minitab output The regression equation is Highway = 50.3 -0.00539 Weight Predictor Coef SE Coef Constant 50.288 2.998 Weight -0.0053868 0.0007773 |S=2.11773 R-Sq=64.0% R-Sq(adj) = 60.9% Predicted Values…