The correlation coefficient between company sales and advertising expenses is 0.92. Which of the following is true about the bivariate data? A. There is a weak positive correlation between company sales and advertising expenses. B. There is a moderate positive correlation between company sales and advertising expenses. C. There is a high positive correlation between company sales and advertising expenses. D. There is a very high positive correlation between company sales

1. A medical student is studying heart rate. The student wants to know if there relationship between age and heart rate. She collects the following data from children selected at random. Heart rate Age (yrs) 92 89 90 84 79 102 108 12 86 14 a) Make a scatter plot of the data. b) Describe the relationship(positive, negative and none) you see between heart rate and age and determine the type of correlation (linear or non-linear) that exists. Relationship Туре» c) Draw a line of best fit. d) What do you think is the heart rate of a newborn baby? Justify your answer. e) What do you think is the heart rate of someone who is 20 years old? What type of prediction is this?

You wish to determine if there is a linear correlation between the age of a driver and the number of driver deaths. The following table represents the age of a driver and the number of driver deaths per 100,000. Use a significance level of 0.05 and round all values to 4 decimal places. Driver Age Number of Driver Deaths per 100,000 21 22 39 21 38 18 23 22 79 36 42 22 65 29 Ho: p = 0 Ha: p= 0 Find the Linear Correlation Coefficient r = Find the p-value p-value = The p-value is O Less than (or equal to) a Greater than a The p-value leads to a decision to O Reject Ho O Accept Ho O Do Not Reject Ho The conclusion is O There is insufficient evidence to make a conclusion about the linear correlation between driver age and number of driver deaths. There is a significant linear correlation between driver age and number of driver deaths. O There is a significant negative linear correlation between driver age and number of driver deaths. O There is a significant positive linear correlation…

A student wants to compute the correlation coefficient with two data pairs. What value or values of r should he expect? A. -1 B. 1 C. 0 D.|1| Given a set of paired data (X,Y), if Y is dependent on X, then what value of a correlation coefficient wouldyou expect? A. -1 B. 1 C. 0 D.|1|

You wish to determine if there is a negative linear correlation between the age of a driver and the number of driver deaths. The following table represents the age of a driver and the number of driver deaths per 100,000. Use a significance level of 0.01 and round all values to 4 decimal places. Driver Age Number of Driver Deaths per 100,000 31 23 60 27 33 18 64 36 72 31 65 31   Ho: ρ = 0Ha: ρ < 0 Find the Linear Correlation Coefficient    r =  Find the p-value   p-value =    The p-value is Less than (or equal to) αα Greater than αα   The p-value leads to a decision to Reject Ho Accept Ho Do Not Reject Ho   The conclusion is  There is insufficient evidence to make a conclusion about the linear correlation between driver age and number of driver deaths. There is a significant linear correlation between driver age and number of driver deaths. There is a significant positive linear correlation between driver age and number of driver deaths. There is a…

Statistics Question

You wish to determine if there is a positive linear correlation between the age of a driver and the number of driver deaths. The following table represents the age of a driver and the number of driver deaths per 100,000. Use a significance level of 0.01 and round all values to 4 decimal places. Driver Age Number of Driver Deaths per 100,000 66 20 75 32 44 31 44 36 19 23 65 29 61 22 58 25 71 27   Ho: ρ = 0Ha: ρ > 0 Find the Linear Correlation Coefficient    r =  Find the p-value   p-value =

Which of the following statements is sensible? A 102.4% of the total variation in a football player's weight is accounted for, or explained by its linear regression with the time spent practicing football. B. The correlation coefficient between a car's length and its fuel efficiency is7 es per gallon. C. There is a very strong direct linear correlation (0.95) between amount of d od consumption and the brand of dog food. D. The correlation coefficient between the amounts of fertilizer used and quantity of beans harvested is 0.42. OD O A OB

2. Consider the following examples; would you expect the correlation coefficient to be close to 1, -1, or 0 (10 points):   The amount of time a dog is walked, and the number of hours the dog sleeps at night     Outdoor temperature and snowfall.         The length of time someone wears braces and their SAT scores.         3. The following graph represents the amount of time that someone studies for their final exam, and their final examination grade (HINT) (20 points).       Student 1 Student 2 Student 3 Student 4 Student 5 Student 6 Hours Studied 4 7 11 8 10 8 Final Exam Grade 55% 72% 88% 93% 91% 84%     Graph the following students as ordered pairs. Let the x-axis represent the hours studied, and the y-axis represent the students’ final exam grade.                         Circle the value that best represents the estimate of r: (circle one)   -0.99, -0.62, -0.30, 0, 0.30, 0.75, 0.85, 0.99

A survey was taken in 2018 that asked people about their saving habits. Researchers wanted to know if people who saved more also spent less. The scatterplot below shows their results when comparing two variables: the amount people reported that they put into savings each month, and the amount they reported that they spent on clothes. The researchers found the correlation coefficient for this data to be -0.239. Which of the following is true about these variables? a. There is no relationship between savings and money spent on clothes each month.b. There is a weak, positive linear relationship between savings and money spent on clothes each month.c. There is a perfect, negative linear relationship between savings and money spent on clothes each month.d. There is a weak, negative linear relationship between savings and money spent on clothes each month.