using the regression equation. What is wrong with this predicted temperature? in 1 min ature (°F) 948 759 1222 1092 841 1123 780 1207 O 75.2 65.2 94.5 84.3 70.7 82.9 71.9 94.3 ression equation is y =+ (Ox. the y-intercept to one decimal place as needed. Round the slope to four decimal places as needed.) t predicted temperature at a time when a cricket chirps 3000 times in 1 minute, based on the regression e to the nearest integer as needed.) wrong with this predicted temperature? It is unrealistically high. The value 3000 is far outside of the range of observed values. The chirps in 1 minute should have been the dependent variable. It is only an approximation. An unrounded value would be considered accurate. Nothing is wrong with this value. It can be treated as an accurate prediction.

using the regression equation. What is wrong with this predicted temperature? in 1 min ature (°F) 948 759 1222 1092 841 1123 780 1207 O 75.2 65.2 94.5 84.3 70.7 82.9 71.9 94.3 ression equation is y =+ (Ox. the y-intercept to one decimal place as needed. Round the slope to four decimal places as needed.) t predicted temperature at a time when a cricket chirps 3000 times in 1 minute, based on the regression e to the nearest integer as needed.) wrong with this predicted temperature? It is unrealistically high. The value 3000 is far outside of the range of observed values. The chirps in 1 minute should have been the dependent variable. It is only an approximation. An unrounded value would be considered accurate. Nothing is wrong with this value. It can be treated as an accurate prediction.

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

See similar textbooks

Similar questions

Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?
What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
Table 2 shows a recent graduate’s credit card balance each month after graduation. a. Use exponential regression to fit a model to these data. b. If spending continues at this rate, what will the graduate’s credit card debt be one year after graduating?
If your graphing calculator is capable of computing a least-squares sinusoidal regression model, use it to find a second model for the data. Graph this new equation along with your first model. How do they compare?
For the 2011 season, suppose the average number of passing yards per attempt for a certain NFL team was 6.1. Use the estimated regression equation developed in part (c) to predict the percentage of games won by that NFL team. (Note: For the 2011 season, suppose this NFL team's record was 7 wins and 9 losses. Round your answer to the nearest integer.)
An investigation into the relationship between an adolescent mother's age x in years and the birth weight y of her baby in grams yielded the regression equation y= - 1163.45 + 245.15x as well as r = .88369, r2= .78091, SSE = 337212.45, and s= 205.30844 1) What is the predicted birth weight for a baby brn to a 17 year old woman? 2) What is the propotion of the variability in the weights of babies born to adolescent mothers that is accounted for by the mother's age? 3) For every additional year in the mother's age that mean birth weight of the baby? (a) increases by about 245g (b) decreases by about 245g (c) increases by about 1163g (d) increases by about 1163g (e) changes by an amount that cannot be determined from the information given.
A carpenter wanted to predict how many pieces of furniture they could produce in a certain time frame. They calculated a linear regression equation and obtained the following results:Using these results how many pieces can they create in 5 hours? y^=2x+2
It is known that the linear regression equation: y= -2.88+1.77x, with a coefficient of determination of 0.81. Based on the two information, the correlation coefficient is
We wish to predict the salary for baseball players (y) using the variables RBI (x1) and HR (x2), then we use a regression equation of the form yˆ=b0+b1x1+b2x2 HR - Home runs - hits on which the batter successfully touched all four bases, without the contribution of a fielding error. RBI - Run batted in - number of runners who scored due to a batters's action, except when batter grounded into double play or reached on an error Salary is in millions of dollars.
If we performed a t-test, how many people would be in the study based on the regression model in photo.
wrie down the estimated regression equation?