There is a weak positive relationship between cholesterol and body mass index. The relationship between body mass and cholesterol level is not negative. Given the correlation between body mass and cholesterol, regression can be used to create an accurate prediction equation. The r-value given for the correlation between body mass and cholesterol level suggestions that using body mass to predict cholesterol leyel may be very

There is a weak positive relationship between cholesterol and body mass index. The relationship between body mass and cholesterol level is not negative. Given the correlation between body mass and cholesterol, regression can be used to create an accurate prediction equation. The r-value given for the correlation between body mass and cholesterol level suggestions that using body mass to predict cholesterol leyel may be very

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

Acrylamide is a chemical that is sometimes found in cooked starchy foods and which is thought to increase the risk of certain kinds of cancer. The paper "A Statistical Regression Model for the Estimation of Acrylamide Concentrations in French Fries for Excess Lifetime Cancer Risk Assessment"+ describes a study to investigate the effect of frying time (in seconds) and acrylamide concentration (in micrograms per kilogram) in french fries. The data in the accompanying table are approximate values read from a graph that appeared in the paper. Frying Acrylamide Time Concentration 150 240 240 270 300 300 150 125 + 195 185 135 275 USE SALT (a) Find the equation of the least-squares line for predicting acrylamide concentration using frying time. (Round your answers to four decimal places.) ŷ = (b) Does the equation of the least-squares line support the conclusion that longer frying times tend to be paired with higher acrylamide concentrations? Explain. O No, the least squares regression line…
The consumption function captures one of the key relationships in economics. It expresses consumption as a function of disposal income, where disposable income is income after taxes. The attached file "Regression-Dataset1" shows data of average US annual consumption (in $) and disposable income (in $) for the years 2000 to 2016. a)what is the Pearson correlation value between income and consumption? Select one: Ⓒa. 0.978 b. 0.109 c. None of the above d. 0.791
A researcher plans to study the causal effect of police on crime using data from a random sample of U.K. counties. He plans to regress the county’s crime rate on the (per capita) size of the county’s police force. Explain why this regression is likely to suffer from omitted variable bias. Which variable would you add to the regression to controlfor important omitted variable? Determine whether the regression will likely over or underestimate the effect of police on the crime rate?
The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 2323 3434 4040 4646 4747 Number of Bids 11 33 44 55 77 Table Copy Data Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places.
Suppose the entering freshmen at a certain college have a mean combined SAT score of 1231 with a standard deviation of 122 In the first semester, these students attained a mean GPA of 2.64, with a standard deviation of 0.53. A scatterplot showed the association to be reasonably linear, and the correlation between SAT score and GPA was 0.47 How do i find the regression line using the equation
The table below gives the number of hours five randomly selected students spent studying and their corresponding midterm exam grades. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the midterm exam grade that a student will earn based on the number of hours spent studying. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. hours studying 0 1 3 4 5 grades 63 65 80 81 87 Find the value of the coefficient of determination. Round your answer to three decimal places.
Suppose the correlation between height and weight for adults is +0.40.What proportion (or percent) of the variability in weight can be explained by the relationship with height?
An observational study is conducted to investigate the association between age and total serum cholesterol. The correlation is estimated at r = 0.35. The study involves n = 125 participants. The mean (std dev) age is 44.3 (10.0) years with an age range of 35 to 55 years, and mean (std dev) total cholesterol is 202.8 (38.4). a) Estimate the equation of the line that best describes the association between age (as the independent variable) and total serum cholesterol. b) Estimate the total serum cholesterol for a 50-year old person. c) Estimate the total serum cholesterol for a 70-year old person.
A linear relationship exists between two variables x and y. The correlation coefficient was calculated for the sample data and is equal to -0.93. Using the estimated regression equation, a straight line that will best fit the data points can be drawn through the points on the scattergram. The line slopes downward from left to right and the points are not scattered close to the line. True or false