d. r(Round to two decimal places) (Round to two decimal places)© 44mins Xe. Interpret r2 :O There is a large variation in the runs scored in baseball games, but if you only look at gameswith a fixed attendance, this variation on average is reduced by 36%.O There is a 36% chance that the regression line will be a good predictor for the runs scoredbased on the attendance of the game.O Given any fixed attendance, 36% of all of those games will have the predicted number of runsscored.O 36% of all games will have the average number of runs scored.f. The equation of the linear regression line is:ŷ =x (Please show your answers to two decimal places)g. Use the model to predict the runs scored at a game that has an attendance of 21,000 people.Runs scored =(Please round your answer to the nearest whole number.)h. Interpret the slope of the regression line in the context of the question:The slope has no practical meaning since the total number runs scored in a game must bepositive.O For every additional thousand people who attend a game, there tends to be an averageincrease of 0.09 runs scored.O As x goes up, y goes up.i. Interpret the y-intercept in the context of the question:O If the attendance of a baseball game is 0, then 2 runs will be scored.O The average runs scored is predicted to be 2.O The y-intercept has no practical meaning for this study.O The best prediction for a game with 0 attendance is that there will be 2 runs scored.Hint: Helpful Video on the Linear Regression Line 7 (+)Helpful Video on Correlation [+]Helpful Video on Hypothesis Tests for Correlation 7 (+]Hints A © 44mins XWhat is the relationship between the attendance at a major league ball game and the total number of runsscored? Attendance figures (in thousands) and the runs scored for 12 randomly selected games are shownbelow.Attendance5013543431606031424859Runs75438104677a. Find the correlation coefficient: r =Round to 2 decimal places.b. The null and alternative hypotheses for correlation are:Ho: ?v = 0H1: ?v + 0The p-value is:(Round to four decimal places)c. Use a level of significance of a = 0.05 to state the conclusion of the hypothesis test in the contextof the study.O There is statistically significant evidence to conclude that a game with a higher attendancewill have more runs scored than a game with lower attendance.O There is statistically significant evidence to conclude that there is a correlation between theattendance of baseball games and the runs scored. Thus, the regression line is useful.O There is statistically insignificant evidence to conclude that there is a correlation between theattendance of baseball games and the runs scored. Thus, the use of the regression line is notappropriate.O There is statistically significant evidence to conclude that a game with higher attendance willhave fewer runs scored than a game with lower attendance.d. r?(Round to two decimal places) (Round to two decimal places)e. Interpret r2 :O There is a large variation in the runs scored in baseball games, but if you only look at gameswith a fixed attendance, this variation on average is reduced by 36%.O There is a 36% chance that the regression line will be a good predictor for the runs scoredbased on the attendance of the game.O Given any fixed attendance, 36% of all of those games will have the predicted number of runsscored.O 36% of all games will have the average number of runs scored.f. The equation of the linear regression line is:

NOTE: As per the guidelines, we are supposed to solve first three sub parts only. a).Consider,…

e is a 36% chance that the regression line will be a good predictor for the runs scored | on the attendance of the game. any fixed attendance, 36% of all of those games will have the predicted number of runs f all games will have the average number of runs scored. n of the linear regression line is: r (Please show your answers to two decimal places) del to predict the runs scored at a game that has an attendance of 21,000 people. |(Please round your answer to the nearest whole number.) e slope of the regression line in the context of the question: lope has no practical meaning since the total number runs scored in a game must be

e is a 36% chance that the regression line will be a good predictor for the runs scored | on the attendance of the game. any fixed attendance, 36% of all of those games will have the predicted number of runs f all games will have the average number of runs scored. n of the linear regression line is: r (Please show your answers to two decimal places) del to predict the runs scored at a game that has an attendance of 21,000 people. |(Please round your answer to the nearest whole number.) e slope of the regression line in the context of the question: lope has no practical meaning since the total number runs scored in a game must be

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 least-squares regression line ŷ and the linear correlation coefficient r for the given data. Round the constants, a, b, and r, to the nearest hundredth. {(1, 4.3), (2, 6.4), (4, 7.8), (6, 11.7), (8, 16.4)}
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.)
I’m taking a statistics and probability class. Please get this correct because I want to learn. I have gotten wrong answers on here before
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…
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 3000chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 1004 952 1237 1116 1177 1249 Temperature (°F) 83.1 76 95.1 87.5 92.1 88.4 What is the regression equation? (^ over y)=_____+_____x (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 3000chirps 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. A. It is…
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? 1220 1075 986 88 5 843 821 849 697 758 1170 867 939 D Chirps in 1 min Temperature ("F) 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…
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm 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 90 mm Hg. Use a significance level of 0.05. Right Arm 103 102 96 76 76 Left Arm 174 167 149 148 148
Can you please check my work
The money raised and spent (both in millions of dollars) by all congressional campaigns for 8 recent 2-year periods are shown in the table. The equation of the regression line is y = 0.942x +27.609. Find the standard error of estimate s, and interpret the result. 793.9 1042.3 957.7 1203.3 450.7 673.7 745.1 778.6 Money raised, x Money spent, y 734.8 1024.2 929.1 1160.6 448.6 697.9 735.7 751.2 Find the standard error of estimate s, and interpret the result. (Round to three decimal places as needed.) How can the standard error of estimate be interpreted? O A. The standard error of estimate of the money raised for a specific amount of money spent is about s, million dollars. O B. The standard error of estimate of the money spent for a specific amount of money raised is about s, million dollars.
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm 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 90 mm Hg. Use a significance level of 0.05. Right Arm 101 100 92 75 75 O Left Arm 174 167 181 149 147 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is y = + x. (Round to one decimal place as needed.) Given that the systolic blood pressure in the right arm is 90 mm Hg, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)
The accompanying data represent the weights of various domestic cars and their gas mileages in the city. The linear correlation coefficient between the weight of a car and its miles per gallon in the city is r= - 0.972. The least-squares regression line treating weight as the explanatory variable and miles per gallon as the response variable is y= - 0.0070x + 44.4405. Complete parts (a) and (b) below. Click the icon to view the data table. ..... (a) What proportion of the variability in miles per gallon is explained by the relation between weight of the car and miles per gallon? The proportion of the variability in miles per gallon explained by the relation between weight of the car and miles per gallon is %. (Round to one decimal place as needed.) (b) Interpret the coefficient of determination. % of the variance in is by the linear model. Data Table (Round to one decimal p Full data set gas mileage Miles per Weight (pounds), x Weight (pounds), x Miles per Gallon, y Car Car Gallon, y…
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right arm 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 90 mm Hg. Use a significance level of 0.05. Right Arm 101 100 92 77 77 Left Arm 174 169 145 146 146