predicted value? The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this Chirps in 1 min 1059 844 1156 1139 759 1164 Temperature ( F) 84.1 75 87.7 842 72.9 86.3 What is the regression equation? (Round the x-coefficient to four decimal places as needed.) What is the best predicted hen a bug is chirping at the rate of 3000 chirps per minute? The best predicted temperature when a bug is chirping at 3000 chirps per minute is F (Round to one decin What is wrong with this cted value? Choose the correct answer below. O A. The first variable shoul e dependent variable O B. It is only an approximation. An unrounded value would be considered accurate. O C. It is unrealistically high. The value 3000 is far outside of the range of observed values. O D. Nothing is wrong with this value. It can be treated as an accurate prediction. Time Remaining: 00:51:08 Next 6:26 P 5/22/202 DELL FO F10 F11 F12 6 3 P H LUse the given data to find the equation of the regression line. Round the final values to three significant digits, if necessary. x 6 8 20 28 36 y 2 4 13 20 30 O A. y= -3.79+0.897x O B. y= -3.79+0.801x O C. y= -2.79+0.897x OD. y= -2.79+0.950x Time Remaining: 00:50:58 Next DELL F2 F3 F4 FO F10 F11 F12 2 3 2 - ta - Backspace Home W U End S H Z . 13 NUse the given data to find the best predicted value of the response variable. Next question Six pairs of data yield r= 0.444 and the regression equation y = 5x +2. Also, y = 18.3. What is the best predicted value of y for x = 57 Use a significance level of 0.05. Click the icon to view the critical values of the Pearson correlation coefficient r. O A. 93.5 O B. 4.22 OC. 18.3 O D. 27 Time Remaining: 00:50:49 Next DELL FO F10 F11 F12 Perall 28 4 2 8 KD Forge Q W Y W End S D KD an 13 MEGiven the linear correlation coefficient r and the sample size n, determine the critical values of r and use your finding to state whether or not the given r represents a significant linear correlation. Use a significance level of 0.05. Round to three decimal places. r= -0.219, n= 90 Click here to view a table of critical values for the correlation coefficient. O A. Critical values: r= +0.207, no significant linear correlation O B. Critical values: r= 0.217, significant linear correlation O C. Critical values: r= +0.217, no significant linear correlation O D. Critical values: r= 10.207, significant linear correlation Time Remaining: 00:50:36 Next 6:26 PM 5/22/2022 DELL F3 FO F10 F11 F12 Geroll O X W Y W O D H Z . 17For 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 = 0.983. 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? lick here to view a table of critical values for the correlation coefficient a. Is there a linear correlation betw and weight? O A. No, because the at exceeds the critical value of 0.707 O B. Yes, because al values of - 0.707 and 0.707. O C. Yes, be exceeds the critical value of 0.707 O D. The answer nined 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 places as needed.) Time Remaining: 00:50 25 Next 6:27 PM 5/22/2022 DELL F4 F10 F11 FJ F12 - Backspace 2 4 6 7 W T 1 2 P . Page PaUp D. H L LD Ne 2 AxtoUse the given data set to complete parts (a) through (c) below. (Use a = 0.05.) y 6.08 5.38 8. 16 6.43 5.72 Click here to view a table of critical values for the correlation coefficient a. Construct a scatterplot. Choose the correct graph below OA DB. DC. D 16 16 12 12 12 2- 0 4 8 12 16 8 4 8 12 16 8 4 8 12 16 b. Find the linear correlation coefficient, r, then determine whether there is sufficient evidence to support the claim of a linear correlation between the two variables. The linear correlation coefficient is r = (Round to three decimal places as needed.) Using the linear correlation coefficient found in the prev mine whether there is sufficient evidence to support the claim of a linear correlation between the two variables. Choose the correct answer below. O A. There is sufficient evidence to support the tion between the two variables O B. There is insufficient ev ation between the two variables O C. There is sufficient evide orrelation between the two variables O D. There is insufficient ey in the two variables. c. Identify the feature of the data that would be missed if part (b) was completed without constructing the scatterplot. Choose the correct answer below. O A. The scatterplot does not reveal a perfect straight-line pattern O B. The scatterplot does not reve l a perfect straight-line pattern, and contains one outlier. O C. The scatterplot reve except for the presence of one outlier. O D. The scatterplot reveals a perfect straight-line pattern and does not contain any outliers. Time Remaining: 00:50 17 Submit quiz zzoz/zz/s 6:21 PM DELL Fo F10 F17 F12 2 5 3 2 4 mart Home W Y w Delete End POUP F G H L . 13 Me TI-84 Plus OF