### Linear Regression and Correlation| x | y ||----|-------|| 2 | 6.32 || 3 | 9.28 || 4 | 0.14 || 5 | 0.7 || 6 | -4.04 || 7 | 3.22 |Compute the equation of the linear regression line in the form \( y = mx + b \), where \( m \) is the slope and \( b \) is the intercept.Use at least 3 decimal places. (Round if necessary)\[ y = \_\_\_\_\_ x + \_\_\_\_\_ \]Compute the correlation coefficient for this data set. Use at least 3 decimal places. (Round if necessary)\[ r = \_\_\_\_\_ \]Compute the P-value (Use \( H_A \): slope \(\neq 0\) for the alternative hypothesis.)Use at least 3 decimal places. (Round if necessary)\[ \text{P-value} = \_\_\_\_\_ \]At the alpha = 0.05 significance level, is the correlation significant?- [ ] Yes, significant correlation- [ ] No### Description of Graphs or DiagramsThe provided table displays the corresponding values of the variables \( x \) and \( y \), which will be used to perform linear regression analysis and correlation computation.1. **Equation of the Linear Regression Line**: - This will derive a linear equation \( y = mx + b \) that best fits the data. - \( m \) (slope) and \( b \) (intercept) are computed values that will be filled in.2. **Correlation Coefficient (\( r \))**: - This indicates the strength and direction of the linear relationship between \( x \) and \( y \).3. **P-value**: - This evaluates the statistical significance of the observed correlation.4. **Significance Check**: - Based on the computed P-value and the specified \( \alpha \) level of 0.05, determine if the observed correlation is statistically significant.

Answered: Image /qna-images/answer/d393a0d7-ad51-4045-856c-1f8f707775b4.jpg

y 2 6.32 9.28 4 0.14 5 0.7 6 -4.04 7 3.22 Compute the equation of the linear regression line in the form y = mx + b, where m is the slope and b is the intercept. Use at least 3 decimal places. (Round if necessary) y = x + Compute the correlation coeficient for this data set. Use at least 3 decimal places. (Round if necessary) r= Compute the P-value (Use H4: slope = 0 for the alternative hypothesis.) Use at least 3 decimal places. (Round if necessary) P-value = At the alpha = 0.05 significance level, is the correlation significant? O Yes, significant correlation O No 3.

y 2 6.32 9.28 4 0.14 5 0.7 6 -4.04 7 3.22 Compute the equation of the linear regression line in the form y = mx + b, where m is the slope and b is the intercept. Use at least 3 decimal places. (Round if necessary) y = x + Compute the correlation coeficient for this data set. Use at least 3 decimal places. (Round if necessary) r= Compute the P-value (Use H4: slope = 0 for the alternative hypothesis.) Use at least 3 decimal places. (Round if necessary) P-value = At the alpha = 0.05 significance level, is the correlation significant? O Yes, significant correlation O No 3.

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

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. The table shows the shoe size and heights (in.) for 6 men. Shoe size, x Height, y 7.5 8.5 9.5 12.0 13.0 13.5 65.0 67.0 72.0 74.0 74.0 72.0 Find the regression equation. シー+D (Round to three decimal places as needed.) Choose the correct graph below. OA. В. C. OD. 75- 75- 65- 65- 65-4 65 He 14 14 14 Shoe size Shoe size Shoe size Shoe size Height (in.) O 回 Height (in.) Height (in.) O 包 Height (in.)
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y (а) х %3D 503 feet (c) x = 321 feet (b) x = 645 feet (d) x = 726 feet 775 619 519 508 491 474 53 47 45 41 38 36 Find the regression equation. x+ (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.)
D. Calculate a linear regression using Stabley. Write the equation below. E. What is the correlation coefficient? f. What is the slope of your LSRL? Interpret the slope using context. G. What is the Y- intercept of your LSRL? Interpret the Y-intercept using context
How can the coefficient of determination be interpreted?
Two variables have a positive linear correlation. Is the slope of the regression line for the variables positive or negative? A. The slope is negative. As the independent variable increases the dependent variable also tends to increase. B. The slope is negative. As the independent variable increases the dependent variable tends to decrease. C. The slope is positive. As the independent variable increases the dependent variable also tends to increase. D. The slope is positive. As the independent variable increases the dependent variable tends to decrease.
6, 5. Kathy painted a picture and posted it on a social media site. The table shows the number of people who responded that they liked the picture since Kathy posted it. Time Since Picture Was Posted 5. 7. (days) Number of Likes 11 21 38 Write the regression equation that best models the data. a. y = 0.96(1. 55)* b. y = 2.07(1. 38)* c. y = 0.536x – 0.857x + 1.81 d. y = 1.38x´ + 0.30x + 0. 43
Can you please check my work
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (Each pair of variables has a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The caloric content and the sodium content (in milligrams) for 6 beef hot dogs are shown in the table below. 170 480 Calories, x Sodium, y 560 Find the regression equation. ŷ=x+ (Round to three decimal places as needed.) Choose the correct graph below. OA. 0ff 0 200 150 430 Calories 130 320 a 130 380 O B. 560 0 80 250 200 G Calories (a) Predict the value of y for x = 160. Choose the correct answer below. OA. 390.863 OB. 440.983 OC. 591.343 O D. not meaningful (b) Predict the value of y for x = 90. Choose the correct answer below. 190 510 (a) x (c) x 160 calories 140 calories O C. A 560+ 0-T 0 200 Calories (b) x = 90 calories (d) x = 220 calories OD. 560- 0 200 Calories Q
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The number of hours 6 students spent for a test and their scores on that test are shown below. Hours spent studying, x 1 2 2 3 4 6 Test score, y 39 44 52 47 64 70 (a)x=2hours (b)x=3.5hours (c)x=13hours (d)x=1.5hours Find the regression equation. y=______ x+ (_____) (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.)
Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line. (The pair of variables have a significant correlation.) Then use the regression equation to predict the value of y for each of the given x-values, if meaningful. The table below shows the heights (in feet) and the number of stories of six notable buildings in a city. Height, x Stories, y (b) x = 649 feet (d) x = 724 feet 766 620 520 508 494 484 (a) x = 503 feet 51 46 44 43 39 38 (c) x = 318 feet Find the regression equation. (Round the slope to three decimal places as needed. Round the y-intercept to two decimal places as needed.) Choose the correct graph below. O A. O B. O C. O D. 60- 60- 60 60- 0- 800 800 800 Height (feet 800 Height (feet) Height (Teet) Stories Stories:
Compute the least-squares regression equation for the given data set. Round the slope and yFintercept to at least four decimal places. 4 y 7. 3. Send data to Excel Regression line equation: y =|