**Determine which plot shows the strongest linear correlation.**The image presents four scatter plots labeled A, B, C, and D, each with an x and y-axis.- **Plot A:** The data points are widely scattered without a clear linear pattern.- **Plot B:** The data points show a vague pattern but are not aligned closely to form a strong linear correlation.- **Plot C:** Similar to Plot A, the points are dispersed widely, lacking a discernible linear trend.- **Plot D:** The data points appear to have a linear trend, forming a diagonal line from the upper left to the lower right of the graph, indicating a stronger linear correlation compared to the other plots.**Analysis:** Plot D demonstrates the strongest linear correlation among the four, with data points aligning closely along a negative slope.

# From the given scattere plot between variable x and y .then to determine which plot represent str

Answered: Determine which plot shows the…

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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If variable X remains constant as variable Y decreases, the correlation is said to be: zero negative positive strong
A linear model fit to predict weekly Sales of frozen pizza (in kilograms) from the average Price (in dollars per unit) charged by a sample of stores in 41 recent weeks is Sales = 141,870.25-24,445.32 Price. For the data used to create the model, the average Sales was 52,750 kilograms (SD = 10,282 kilograms) and the correlation between Price and Sales was r=-0.554. If the Price in a particular week was 2 SD higher than the mean Price, how much pizza would the model predict was sold that week? The model would predict that kilograms of frozen pizza were sold. (Round to the nearest integer as needed.)
A manager of a local retail store analyzes the relationship between the number of rainy days and sales by reviewing the store’s data for the previous six months. She computes the correlation between sales and number of rainy days as -0.90. Select ALL that apply. a. There is a strong positive linear relationship between sales and number of rainy days. b. As the number of rainy days increases sales decrease. c. As the number of rainy days increases sales increase. d. There is a strong negative linear relationship between sales and the number of rainy days.
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Explain what correlation is and why two variables might be described as having a negative correlation. Age and education: A survey of U.S. adults showed that there is a negative correlation between age and education level. Does this mean that people become less educated as they become older? why or why not?
Construct a scatter plot for the given data. Determine whether there is a positive correlation, negative correlation, or no linear correlation. x-5-34 1 |-1-202 3-4 y11 6 |-6-13 4 1-4-58
Two variables are found to have a strong positive linear correlation. Pick the correct statement. An increase in one variable potentially causes a decrease in the other. An increase in one variable does not cause a decrease in the other. An increase in one variable does not cause an increase in the other. An increase in one variable potentially causes an increase in the other. An increase in one variable causes a decrease in the other. An increase in one variable causes an increase in the other.
Which scatter plot shown has the strongest negative correlation?
When two variables have a perfect linear relationship, the correlation coefficient is always 1. True False
Let x be the average number of employees in a group health insurance plan, and let y be the average administrative cost as a percentage of claims. x 3 7 15 32 75 y 40 35 30 26 18 (a) Make a scatter diagram of the data and visualize the line you think best fits the data. Flash Player version 10 or higher is required for this question. You can get Flash Player free from Adobe's website. (b) Would you say the correlation is low, moderate, or strong? positive or negative? strong and negative moderate and negative strong and positive low and negative low and positive moderate and positive (c) Use a calculator to verify that Σx = 132, Σx2 = 6932, Σy = 149, Σy2 = 4725, and Σxy = 2997. Compute r. (Round your answer to three decimal places.)r = As x increases, does the value of r imply that y should tend to increase or decrease? Explain. Given our value of r, we cannot draw any conclusions for the behavior of y as x increases. Given our value of r, y should tend to…
The table below gives the age and bone density for 5 women. Use the equation of the regression line, y= b0 + b1x, for predicting a women's bone density based on her age. The correlation coefficient may or may not be statically significant for the data given. Remember it wouldn't be appropiate to use regression line to make a prediction if the correlation coefficient isn;t statically significant. (y has a "hat" on the top) age 39 51 54 56 67 bone density 355 349 347 315 313 Find the estimated slope. Rund your answer to three decimal places. Find the estimated y-intercept. Round your answer to three decimal places. Determine the value of the dependent variable y at x+ 0 (y has a "hat" onthe top) Find the estimated value of y when x = 51. Round your answer to three decimal places. Substitute the values you found in steps 1 and 2 into the equation for the regression line to find the estimated linear model. According to this model, if the valueof the…
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…

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