The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable 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 w predicted value? Chirps in 1 min Temperature ("F) 1220 1075 986 1170 867 88 5 84 3 82 1 84.9 69 7 758 939 D What is the regression equation? (Round the x-coefficient to four decimal places as needed Round the constant to two decimal places as needed) What is the best predicted temperature for a time when 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 decimal place as needed.) What is wrong with this predicted value? Choose the correct answer below O A. The first variable should have been the dependent variable O B. It is unrealistically high. The value 3000 is far outside of the range of observed values O C. It is only an approximation. An unrounded value would be considered accurate O D. Nothing is wrong with this value It can be treated as an accurate prediction.

The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable 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 w predicted value? Chirps in 1 min Temperature ("F) 1220 1075 986 1170 867 88 5 84 3 82 1 84.9 69 7 758 939 D What is the regression equation? (Round the x-coefficient to four decimal places as needed Round the constant to two decimal places as needed) What is the best predicted temperature for a time when 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 decimal place as needed.) What is wrong with this predicted value? Choose the correct answer below O A. The first variable should have been the dependent variable O B. It is unrealistically high. The value 3000 is far outside of the range of observed values O C. It is only an approximation. An unrounded value would be considered accurate O D. Nothing is wrong with this value It can be treated as an accurate prediction.

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

49
A patient is classified as having gestational diabetes if their average glucose level is above 140 milligrams per deciliter (mg/dl) one hour after a sugary drink is ingested. Rebecca's doctor is concerned that she may suffer from gestational diabetes. There is variation both in the actual glucose level and in the blood test that measures the level. Rebecca's measured glucose level one hour after ingesting the sugary drink varies according to the Normal distribution with μ=140+5 mg/dl and σ=5+1 mg/dl. Using the Central Limit Theorem, determine the probability of Rebecca being diagnosed with gestational diabetes if her glucose level is measured: Once? n=5+2 times n=5+4 times Comment on the relationship between the probabilities observed in (a), (b), and (c). Explain, using concepts from lecture why this occurs and what it means in context.
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 predicted value? Chirps in 1 min 769 828 993 1126 937 824 D Temperature (°F) 68.8 67.5 81.9 87.1 78,3 75.6 What is the regression equation? =ロ·ロ× (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.) Help me solve this View an example Get more help - Clear all Check answer 4:51 PM 66°F 全x D 4/15/2022 Sunny
A researcher calculates a regression equation to predict an insurance premium based on a person's age. One person in the sample was observed to have a premium of $500 at 20 years old, but the predicted value was $400. VWhat is the value of the residual for this person? Next Page Page 10 of 14
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right am blood pressure be the predictor (x) variable. Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 100 mm Hg Use a significance level of 0.05. Right Arm 103 102 95 78 Left Arm 175 169 147 146 144 m Click the icon to view the critical values of the Pearson correlation coetticient r The regression oquation is y =+O (Round to one decimal place as needed) Given that the systolic blood pressure in the right arm is 100 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg (Round to one decimal place as needed.)
Terminology Using the lengths (in.), chest sizes (in.), and weights (lb) of bears from Data Set 9 “Bear Measurements’’ in Appendix B, we get this regression equation: Weight = −274 + 0.426 Length +12.1 Chest Size. Identify the response and predictor variables.
Two variables are defined, a regression equation is given, and one data point is given. Weight Training = = Weight 92 +11.9(Training) = maximum weight capable of bench pressing (pounds) number of hours spent lifting weights a week The data point is an individual who trains 5 hours a week and can bench 150 pounds. Residual (a) Find the predicted value for the data point and compute the residual. Enter the exact answers. Predicted value = M. lbs lbs
A popular television show recently released a video previewing the upcoming season on his website as fans of the show discovered the video the number of views of the preview video has grown each day during the last two weeks the number of days in the release of the video and the natural log of the number of video views as shown in the scatterplot. based on the scatterplot and residual plot, what type of model would be appropriate for summarizing the relationship between days and numbers of views? an exponential model is appropriate because the Residual plot does not show a clear pattern A linear model is appropriate because of scatterplot shows a strong positive linear relationship A power model is appropriate because the scatterplot of days a natural log of use is strong and linear an exponential model is appropriate because the relationship between the days in the natural log of views in linear and the residual plot does not show a clear pattern
Two variable are found to have a strong negative linear correlation. Pick the regression equation that best fits this scenario. y=0.82x−28 ˆy=0.32x−28 y= -0.82x+28 ˆy= -0.32x+28
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 predicted value? Chirps in 1 min 760 1119 1125 850 965 785 D Temperature (°F) 70.5 90.8 90.8 74.7 77.1 69.9 What is the regression equation? (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.)
I was wondering specifically about part D and how to interpret my findings.
13/