Bigger snakes weigh more. Here is data comparing the length in centimeters to weight in grams of a certain species of snake. Assume both variables follow a normal distribution. Test the claim of correlation at the α = 0.05 level of significance. Find the P-value Will the null hypothesis be rejected? Round the regression equation to one decimal place and use it to predict the weight of a snake 42 cm in length. Length 80 32 61 19 25 45 46 76 Weight 211 65 75 20 80 125 65 150

Bigger snakes weigh more. Here is data comparing the length in centimeters to weight in grams of a certain species of snake. Assume both variables follow a normal distribution. Test the claim of correlation at the α = 0.05 level of significance. Find the P-value Will the null hypothesis be rejected? Round the regression equation to one decimal place and use it to predict the weight of a snake 42 cm in length. Length 80 32 61 19 25 45 46 76 Weight 211 65 75 20 80 125 65 150

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.5: Correlation And Causation

Problem 2CYU

See similar textbooks

Length	80	32	61	19	25	45	46	76
Weight	211	65	75	20	80	125	65	150

Similar questions

Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with an altitude of 6327 ft (or 6.327 thousand feet). Altitude 2 8 13 23 28 31 32 Temperature 56 40 27 −1 −34 −41 −50 a. Find the explained variation. (Round to two decimal places as needed.) b. Find the unexplained variation. (Round to five decimal places as needed.) c. Find the indicated prediction interval. ______°F<y<______°F
Larger engines use more gas. Here is data comparing the vehicle engine size in liters to city mile per gallon (mpg). Assume both variables follow a normal distribution. Assume both variables follow a normal distribution. Test the claim of correlation at the α = 0.05 level of significance. Find the P-value ["", "", "", "", ""] Will the null hypothesis be rejected? ["", "", "", "", ""] Round the regression equation to one decimal place and use it to predict the city mpg of a vehicle with an engine 6.0 L in size. ["", "", "", "", ""] Size 6.2 5.4 3.0 3.9 3.6 5.0 6.4 3.5 City mpg 13 14 20 19 19 20 15 17
The accompanying table lists systolic blood pressures (mm Hg) and diastolic blood pressures (mm Hg) of adult females. Find the (a) explained variation, (b) unexplained variation, and (c) prediction interval for a systolic blood pressure of 118 mm Hg using a 99% confidence level. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. Systolic Diastolic 127 69 103 65 130 73 104 64 157 74 97 51 155 90 111 69 124 68 112 75 103 60 128 67
Consider the data below. Suppose that in the aerobic exercise group we also measured the number of hours of aerobic exercise per week; the mean is 5.2 hours with a standard deviation of 2.1 hours. The sample correlation is -0.42. a) Estimate the equation of the regression line that best describes the relationship between number of hours of exercise per week and HDL cholesterol level (Assume that the dependent variable is HDL level). b) Estimate the HDL level for a person who exercises 7 hours per week. c) Estimate the HDL level for a person who does not exercise. Exercise Group N Mean Standard Deviation Weight Training 20 49.7 10.2 Aerobic Exercise 20 43.1 11.1 Stretching/Yoga 20 57.0 12.5
People age, the number of hours per week spent on exercise tends to decrease. Here is a random sample comparing are and hours per week of exercise. Assume both variables follow a normal distribution. Test the claim of correlation at the α = 0.05 level of significance. Find the P-value ["", "", "", "", ""] Will the null hypothesis be rejected? ["", "", "", "", ""] Round the regression equation to one decimal place and use it to predict the hours of exercise per week for someone 37 years old. ["", "", "", "", ""] Age 21 36 41 45 31 25 50 61 Hours 15 14 6 12 10 20 5 4