Dr. Erie, a researcher at the Moore County Botany Institute, reported the following correlations.The correlation between pH level of soil and tomato weight is r₁ = -0.4. The correlation between hoursof sunlight and tomato weight is r2 = 0.1.According to this information, which of the following statements are true? Select all that apply.Larger tomatoes result from more daily hours of sunlight.Larger tomatoes result from higher soil pH level.Tomato size is related more strongly to hours of sunlight than to soil pH level.None of these are true.

Correlation coefficient : Correlation coefficient tells about the strength of relationship between…

Answered: Dr. Erie, a researcher at the Moore…

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 weights (in pounds) of 6 vehicles and the variability of their braking distances (in feet) when stopping on a dry surface are shown in the table. Can you conclude that there is a significant linear correlation between vehicle weight and variability in braking distance on a dry surface? Use a = 0.05. Weight, x Variability in braking distance, y 5980 5370 6500 5100 5820 4800 1.72 1.97 1.87 1.63 1.64 1.50 Click here to view a table of critical values for Student's t-distribution. Setup the hypothesis for the test. Ho: Ha: P Identify the critical value(s). Select the correct choice below and fill in any answer boxes within your choice. (Round to three decimal places as needed.) O A. The critical value is. B. The critical values are - to and to %3D Calculate the test statistic. t= (Round to three decimal places as needed.) What is your conclusion? There enough evidence at the 5% level of significance to conclude that there a significant linear correlation between vehicle weight and…
Dr. Wright, a researcher at the Young County Botany Institute, reported the following correlations. The correlation between pH level of soil and tomato weight is r₁ = 0.8. The correlation between hours of sunlight and tomato weight is r2 = 0.7. According to this information, which of the following statements are true? Select all that apply. ✓Larger tomatoes result from more daily hours of sunlight. Larger tomatoes result from lower soil pH level. ✔Tomato size is related more strongly to hours of sunlight than to soil pH level. None of these are true.
Using the below data, how will you judge the degree of correlation between the two variables x and y? Edxdy = 125 n =60 Ox=5 Cy = 7 O a. Very high degree of correlation O b. Very low degree of correlation O C. Perfect correlation O d. No correlation
A special education teacher did research on whether or not there is a relationship between the number of students in his class and the number incidents of “acting out” behaviors exhibited by the autistic students in the classroom. He collects data for a year and aggregates them by month. He obtained the statistics below, r= -.863 R2=.74 b= -1.212294 a= 131.176598 10.) How does the presence of more students affect the incidents in the class? a) as students are added the incidences increase b) as students are added the incidences decrease c) the number of students does not affect acting out d) the number of students caused more incidents How much of the variability of acting out is explained by the number of students in the class?___________
Using the below data, how will you judge the degree of correlation between the two variables x and y? Edxdy = 125 Ox = 5 n = 60 Oy = 7 O a. Very low degree of correlation O b. No correlation O C. Perfect correlation O d. Very high degree of correlation
The correlation coefficient between variables X and Y is 0.68. What does this signify? a. When X increases by 1, Y increases by 0.68 b. When Y increases 1, X increases by 0.68 c. The variance shared by X and Y is 68% d. The variance shared by X and Y is less 20%
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.
Would A be the answer?
If the linear association between two numerical variables is studied and the correlation coefficient is 0.32, it can be concluded that:a. The relationship between the variables is inverse and weak. b. The percentage of variability observed in the data that is explained by the error is 89.76%. c. The percentage of observed variability in the data that is explained by the model is 32%. d. None is correct Please explain clearly, thank you
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…
Which of the following correlation coefficients indicate the strongest relationship between two variables? A .20 B .87 C .80 D -.95

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