a)The alternative hypothesis is:O H1 : The covariance between CVD and Enrollment Site is negative.H1 : The correlation between CVD and Site is zero.O H1 : CVD and Enrollment Site are independent.O H1 : CVD and Enrollment Site are dependent.O H1 : The covariance between CVD and Enrollment Site is positive.O H1 : The covariance between CVD and Site is zero.b)Under independence, calculate the expected number of patients with a family history of CVD, at Hospital 3.Round your answer to 2 decimal places. A cross-sectional study is conducted to investigate cardiovascular disease (CVD) risk factors among a sample of patients seeking medical care at one ofthree local hospitals. A total of 800 patients are enrolled. Based on the following data, we would like to determine if there is a significant associationbetween the family history of CVD and the enrollment site.Enrollment SiteFamily History of CVDHospital 1Hospital 2Hospital 3TotalYes342838100No182126392700Total216154430800• Given:The value of the test statistic is x? = 11.894.Use a = 0.1 as the level of significance.

Test is that whether there is a significant association between the family history of CVD and the…

Answered: The alternative hypothesis is: O H1 :…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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The table below includes data from taxi rides. The distances are in miles, the times are in minutes, the fares are in dollars, and the tips are in dollars. Is there sufficient evidence to conclude that there is a linear correlation between the time of the ride and the tip amount? Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value of r. Determine whether there is sufficient evidence to support a claim of linear correlation between the two variables. Use a significance level of α = 0.01. Does it appear that riders base their tips on the time of the ride? Click here for information on the taxi rides. Construct a scatterplot. Choose the correct graph below. OA. 25 은 Q Q 0+ 0 35 Ride time (minutes) Determine the linear correlation coefficient. The linear correlation coefficient is r= (Round to three decimal places as needed.) &B. 25- Q 0- bro 0 35 Ride time (minutes) Taxi data Distance 0.63 6.00 6.30 1.89 Time Fare Tip C... 2.44 18.00 14.30…
A polling organization is checking its database to see if the data sources it used sampled the same ZIP codes. The variable Datasource = 1 if the data source is MetroMedia, 2 if the data source is DataQwest, and 3 if it's RollingPoll. The organization finds that the correlation between five-digit ZIP code and DATAsource is - 0.0229. It concludes that the correlation is low enough to state that there is no dependency between ZIP code and Source of Data. Comment. Choose the correct answer below. O A. A correlation of -0.0229 is very close to zero, which implies that there is a very strong relation between the variables O B. A correlation must be between 0 and 1, so the correlation of -0.0229 must have been calculated incorrectly. O C. The source of the data is categorical, which will almost always result in very low correlation values. These types of correlations can only be interpreted in the rare instances when the correlation is close to -1 or 1. O D. The source of the data is…
For a data set of weights (pounds) and highway fuel consumption amounts (mpg) of twelve types of automobile, the linear correlation coefficient is found and the P-value is 0.022. Write a statement that interprets the P-value and includes a conclusion about linear correlation. The P-value indicates that the probability of a linear correlation coefficient that is at least as extreme is %, which is so there sufficient evidence to conclude that there is a linear correlation between weight and highway fuel consumption in automobiles. (Type an integer or a decimal. Do not round.)
A student noticed that when she gets less than 8 hours of sleep at night, she scores lower on her test grades. Can one conclude that this positive correlation shows a causal relationship? OYes, because it is well known that less sleep lowers test scores OYes, because it is not a negative correlation O No, because all students get less than 8 hours of sleep at night No, because it is possible that the student gets lower test grades because of another factor, such as not studying
One professor who teaches statistics gave students one homework assignment. He asked students to find a two-variable data set and compute the Pearson correlation coefficient. One student found a data set: one variable is students' midterm test score in an introductory microeconomics class and total observations are 45 students; while the other one variable is also students' midterm test score in another introductory microeconomics class and total observations are 57 students. Both of these two classes were taught by the same instructor in spring 2018. Does this student's data set make sense? Why or why not? Discuss and explain your reasons. You must provide your statistical analysis and reasons.
A sample of 548 ethnically diverse students from Massachusetts were followed over a 19-month period from 1995 and 1997 in a study of the relationship between TV viewing and eating habits. For each additional hour of television viewed per day, the number of fruit and vegetable servings per day was found to decrease on average by 0.14 serving. (a) For this study, what is the dependent variable? O location of students O number of fruit and vegetable servings per day O number of non fruit or vegetable servings per day O number of hours of television viewed per day year What is the independent variable? O location of students O number of fruit and vegetable servings per day O number of non fruit or vegetable servings per day O number of hours of television viewed per day O year (b) Would the least-squares line for predicting number of servings of fruits and vegetables using number of hours spent watching TV as a predictor have a positive or negative slope? Explain. O The slope of the least…
The table below includes data from taxi rides. The distances are in miles, the times are in minutes, the fares are in dollars, and the tips are in dollars. Is there sufficient evidence to conclude that there is a linear correlation between the time of the ride and the tip amount? Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value of r. Determine whether there is sufficient evidence to support a claim of linear correlation between the two variables. Use a significance level of α=0.01. Does it appear that riders base their tips on the time of the ride? Choose the correct graph below (attached image) Determine the linear correlation coefficient. The linear correlation coefficient is r=_____ (Round to three decimal places as needed.) Determine the null and alternative hypotheses. H0: ρ ▼ greater than> equals= not equals≠ less than< enter your response here H1: ρ ▼ equals= not equals≠ less…
There is a strong positive linear correlation between the number of clear days and the height of bean plants. Does this mean that more clear days cause the bean plants to grow taller? A. Yes. Since the number of clear days and height are positively correlated, this means a greater number of clear days causes the bean plants to grow taller. B. No. Any number of factors could also affect the height of the bean plants. C. No. Since the number of clear days and height are linearly correlated, this means a smaller number of clear days causes the bean plants to grow taller. D. Yes. Since the number of clear days and height are linearly correlated, this means a greater number of clear days causes the bean plants to grow taller.
e. Compute the covariance and correlation coefficient r between the right tire pressure. f. How do you know the relationship between pressure in the left and right tires is positive. g. What value would a correlation coefficient (r) be close to if there was no (linear) relationship between the 2 tire pressures?
Q4
If we find that there is a linear correlation between the concentration of carbon dioxide in our atmosphere and the global temperature, does that indicate that changes in the concentration of carbon dioxide cause changes in the global temperature? Choose the correct answer below. A. No. The presence of a linear correlation between two variables does not imply that one of the variables is the cause of the other variable. B. Yes. The presence of a linear correlation between two variables implies that one of the variables is the cause of the other variable.
Which scatterplot shows a strong association between two variables even though the correlation coefficient is probably near zero because it's not linear?

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