Consider the following set of ordered pairs.X 2 5 1 2 20y 6 75 66Assuming that the regression equation is y=4.957 +0.435x and that the SSE = 0.2609, construct a 95%confidence interval for the slope.Click here to see the t-distribution table, page 1 Click here to see the t-distribution table. page 2Construct a 95% confidence interval for the slope.LCL = and UCL =(Round to three decimal places as needed.)

Given data, x y 2 6 5 7 1 5 2 6 2 6 Regression equation: y^=4.957+0.435x SSE=0.2609

Answered: Consider the following set of ordered…

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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Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. 9. 8 11 9. 11 14 4 12 y 7.47 6.66 13.28 7.1 7.59 8.43 6.09 5.39 7.9 6.48 5.65 O A. OB. Oc. OD. Ay 25- Ay 25- Ay 25- AV 25- 20 20 20 20 15 15- 15- 15 10 10- 10- 10- 55 51 5- 0- Ó 5 10 15 20 25 0- 05 10 15 20 25 0- 5 10 15 20 25 O 5 10 15 20 25 Find the equation of the regression line. (Round the constant two decimal places as needed. Round the coefficient to three decimal places as needed.) Identify a characteristic of the data that is ignored by the regression line. O A. There is an influential point that strongly affects the graph of the regression line. O B. There is no characteristic of the data that is ignored by the regression line. O C. The data has a pattern that is not a straight line. D. There is no trend in the data.
Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. 12 10 14 13 6 y 7.26 6.82 12.79 7.28 7.92 9.07 6.29 5.46 8.45 6.44 6.02 Create a scatterplot of the data. Choose the correct graph below. O B. Oc. OD. 25- 25 25- 25- 20- 20- 20 205 15 15 15 15 10- 105 10- 10 53 07 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 o 5 10 15 20 25 8.
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Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. 9 9 11 10 11 14 4 13 7 6 y 7.59 6.87 13.06 7.32 8.58 6.09 5.62 8.35 6.73 7.77 5.9 Create a scatterplot of the data. Choose the correct graph below. O A. OB. OC. Oc. OD. Ay 25- Ay 25- Ay 25- 25- 20 20- 20- 20- 155 155 155 155 105 105 105 105 5- 55 55 55 0- Ó 5 10 15 20 25 0- 0- 05 10 15 20 25 Ó 5 10 15 20 25 0 5 10 15 20 25 Find the equation of the regression line. (Round the constant two decimal places as needed. Round the coefficient to three decimal places as needed.) Identify a characteristic of the data that is ignored by the regression line. O A. The data has a pattern that is not a straight line. O B. There is no characteristic of the data that is ignored by the regression line. OC. There is an influential point that strongly affects the graph of the regression line. O D. There is no trend in the data.
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Use the given data to find the equation of the regression line. Examine the scatterplot and identify a characteristic of the data that is ignored by the regression line. 11 8 11 13 6 13 6 y 7.24 6.87 12.78 6.87 7.75 8.85 6.28 5.61 8.28 6.72 5.52 Create a scatterplot of the data. Choose the correct graph below. VA. OB. OC. OD. 25- Ay 25- 25- 25- 20- 20 20- 20 15 155 155 155 10- 10- 10 10- 5- 5- 5 55 0- 05 10 15 20 25 0- O 5 10 15 20 25 04 05 10 15 20 25 0- 05 10 15 20 25 Find the equation of the regression line. (Round the constant two decimal places as needed. Round the coefficient to three decimal places as needed.)
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The data show the chest size and weight of several bears. Find the regression equation, letting chest size be the independent (x) variable. Then find the best predicted weight of a bear with a chest size of 41 inches. Is the result close to the actual weight of 153 pounds? Use a significance level of 0.05. Chest size (inches) Weight (pounds) Click the icon to view the critical values of the Pearson correlation coefficient r. 40 53 38 43 44 58 D 227 360 153 206 234 414 What is the regression equation? y=+x (Round to one decimal place as needed.) What is the best predicted weight of a bear with a chest size of 41 inches? The best predicted weight for a bear with a chest size of 41 inches is (Round to one decimal place as needed.) Is the result close to the actual weight of 153 pounds? O A. This result is exactly the same as the actual weight of the bear. O B. This result is close to the actual weight of the bear. O C. This result is very close to the actual weight of the bear. O D. This…
Prev 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, y = bo + 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 28 33 36 42 45 Number of Bids 1 7 8 9 10 Step 3 of 6: Find the estimated value of y when x = 33. Round your answer to three decimal places. Table Copy Data Next
Find the regression equation, letting overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is 2.5 cm. Can the prediction be correct? What is wrong with predicting the weight in this case? Use a significance level of 0.05. Overhead Width (cm) 7.6 7.4 9.8 8.8 9.3 7.3 Weight (kg) 142 163 256 188 231 156 The regression equation is y= + x. (Round to one decimal place as needed.) The best predicted weight for an overhead width of 2.5 cm is kg? (Round to one decimal place as needed.) Can the prediction be correct? What is wrong with predicting the weight in this case? A. The prediction cannot be correct because a negative weight does not make sense. The width in this case is beyond the scope of the available sample data. B. The prediction cannot be correct because a negative weight does not make sense and because there is…
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Please answer A,B, D, F, & G pls and thank you!

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