You have a theory: people who have a high income (X) are generally healthier (Y). Youcollect some data from local nursing homes and retirement homes and find no linearcorrelation between income and health. Of the 4 correlation mistakes we discussed in class,which one is the most likely here? Why?A supplements company conducted a study and found a strong positive relationship betweenuse of its supplements and health. It advertised that it found a correlation coefficient of+1.25. What is wrong with this statement?

Correlation is the relation between two variables.

Answered: You have a theory: people who have a…

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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ab! ourses The accompanying technology output was obtained by using the paired data consisting of foot lengths (cm) and heights (cm) of a sample of 40 people. Along with the paired sample data, the technology was also given a foot length of 13.6 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 13.6 cm, what is the single value that is the best predicted height for that person? "se Hom E Click the icon to view the technology output. abus The single value that is the best predicted height is cm. endar (Round to the nearest whole number as needed.) entation ktbook a-F on unouncem ssignment: cudy Plan radebook Hydrogen Report.pdf Enter your answer in the answer box. Screen Shot 20-11...9.53 P 5,981 10 tv MacBook Pro (5
You are interested in knowing whether wealthier people are happier. You collected data from fifty people about their incomes and their overall happiness levels on a scale of 1 to 10. Upon analyzing the results, you find that the correlation coefficient has a value of -0.25. On the basis of this data, respond to the following: How would the results be affected if you increased the number of subjects in the study to one thousand? Why might that affect the overall correlation?
Would A be the answer?
A history instructor has given the same pretest and the same final examination each semester. he is interested in determining if there is a relationship between the scores of the two tests. he computes the linear correlation coefficient and notes that it is 1.15. what does the correlation coefficient value tells the instructor? a. there is a strong positive correlation between the test b. there is a strong negative correlation between the test c. the history instructor has made a computational error d. the correlation is something other than linear
A random sample of college students was surveyed about how they spend their time each week. The scatterplot below displays the relationship between the number of hours each student typically works per week at a part- or full-time job and the number of hours of television each student typically watches per week. The correlation between these variables is r = –0.63, and the equation we would use to predict hours spent watching TV based on hours spent working is as follows: Predicted hours spent watching TV = 17.21 – 0.23(hours spent working) Since we are using hours spent working to help us predict hours spent watching TV, we’d call hours spent working a(n) __________________ variable and hours spent watching TV a(n) __________________ variable. The correlation coefficient, along with what we see in the scatterplot, tells us that the relationship between the variables has a direction that is _________________ and a strength that is ______________________. According to the…
An ecologist is interested in exploring the relationship between pollination rate and the number of bees present at apple orchards. The relationship is: (Pollination rate) = 372.6 + 13.5 x (bees) with r = 0.80. The best interpretation of the correlation coefficient is: (pick one) 1. 64% of the variability in pollination rate observed at apple orchards is explained by the relationship with the number of bees 2. the correlation between pollination rate and number of bees is 0.64 3. 80% of the variability in number of bees at apple orchards is explained by the relationship with the pollination rate 4. the correlation between pollination rate and number of bees is 0.80
For a sample of eight bears, researchers measured the distances around the bears' chests and weighed the bears. Minitab was used to find that the value of the linear correlation coefficient is r = 0.862. Using a = 0.05, determine if there is a linear correlation between chest size and weight. What proportion of the variation in weight can be explained by the linear relationship between weight and chest size? Click here to view a table of critical values for the correlation coefficient. a. Is there a linear correlation between chest size and weight? O A. No, because the absolute value of r exceeds the critical value of 0.707. O B. Yes, because r falls between the critical values of -0.707 and 0.707. O C. Yes, because the absolute value of r exceeds the critical value of 0.707. O D. The answer cannot be determined from the given information. b. What proportion of the variation in weight can be explained by the linear relationship between weight and chest size? (Round to three decimal…
2. A researcher measures the relationship between temperature and freeway traffic, with a correlation of -0.61. We would say this relationship is: Negative and Moderate Negative and Strong Positive and Moderate Positive and Weak O o o O
Carefully read the description of the following two studies (A and B). For each study answer the following questions: Should a correlation analysis or multiple regression be used to analyze the results. Describe the basis for your answer – why did you select this test. Study A Dr. Banner want to determine whether his patients’ age is a factor in compliance with dialysis. He hypothesizes that older patients will be less compliant with dialysis. In other words, he believes the older the patient the more dialysis appointment they miss. Study B Dr. Banner’s intern does not agree. She believes there are other factors, along with age, that influence compliance such as race anxiety and mistrust of health care, side effects of treatment and issues related to culture. She wants to know who much each of these factors contribute to non-compliance.
A study of elementary school children, ages 6 to 11, finds a high positive correlation between shoe size x and score y on a test of reading comprehension. The observed correlation is most likely due to: the effect of a lurking variable, such as age or years of reading experience. a mistake, because the correlation must be negative. cause and effect (larger shoe size causes higher reading comprehension). reverse cause and effect (higher reading comprehension causes larger shoe size).
The accompanying technology output was obtained by using the paired data consisting of foot lengths (cm) and heights (cm) of a sample of 40 people. Along with the paired sample data, the technology was also given a foot length of 17.8 cm to be used for predicting height. The technology found that there is a linear correlation between height and foot length. If someone has a foot length of 17.8 cm, what is the single value that is the best predicted height for that person? Click the icon to view the technology output. The single value that is the best predicted height is (Round to the nearest whole number as needed.) cm. Technology Output The regression equation is Height = 53.3+4.25 Foot Length Predictor Constant Coef SE Coef 53.28 11.44 Foot Length 4.2537 0.4699 S = 5.50739 R-Sq=72.3% R-Sq (adj) = 71.6% T 4.66 9.05 Predicted Values for New Observations New Obs 1 Fit SE Fit 128.996 1.733 (124.309, 133.683) 95% CI Foot New Obs Length 1 17.8 0.000 0.000 Values of Predictors for New…
In a study of the stability of IQ scores, a large group of individuals is tested once at age 18 and again at age 35. The following results are obtained. At age 18: average score = 100, SD = 15 At age 35: average score = 100, SD = 15 The correlation coefficient between scores at age 18 and 35 is 0.80 and the scatter plot is football shaped. What percent of the individuals with a score of 115 at age 18 score more than 100 at age 35? Fill in the blank with a number between 0 and 100 to represent the percentage. (Do not enter the % sign.) Don't worry about rounding; any correctly rounded answer will receive full credit.

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