Problem #2: The data show bug chirps per minute at different temperatures. The significant level is 0.05 Chirps in I min Temperature (°F) 900 500 902 600 700 1000 77.3 80.3 73.9 78.9 67.1 88.7 a) What is the regression equation? b) Check the P-value. Is the P-value < a? answer part c. c) What is the best predicted temperature for a time when a bug is chirping at a rate of 1100 chirps per minute? if yes use the line for prediction. If not, find the mean temperature to StatCrunch commands: stat < regression < simple linear < choose x and y < choose Hypothesis test< Check if P-value is less than or equal to a enter the value of X in the predicted value< compute! If P-value is > a, the predicted value is the mean average of Y values. © G. Nasnas

Problem #2: The data show bug chirps per minute at different temperatures. The significant level is 0.05 Chirps in I min Temperature (°F) 900 500 902 600 700 1000 77.3 80.3 73.9 78.9 67.1 88.7 a) What is the regression equation? b) Check the P-value. Is the P-value < a? answer part c. c) What is the best predicted temperature for a time when a bug is chirping at a rate of 1100 chirps per minute? if yes use the line for prediction. If not, find the mean temperature to StatCrunch commands: stat < regression < simple linear < choose x and y < choose Hypothesis test< Check if P-value is less than or equal to a enter the value of X in the predicted value< compute! If P-value is > a, the predicted value is the mean average of Y values. © G. Nasnas

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

The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
question 3 part 2 (thats the equation for the problem) The following estimated regression equation based on 10 observations was presented. ŷ = 21.1270 + 0.5508x1 + 0.4940x2 Here, SST = 6,736.125, SSR = 6,218.375, sb1 = 0.0815, and sb2 = 0.0561.
Question 4 B i-iii Good Day , I hope you are keeping well and safe. Please assit with the following question. b) Finally, the researcher is interested in examining the regression model for knowledge, attitude and practices towards the COFLU-20. The following model was developed to forecast individual practices towards COFLU-20 using knowledge and attitude scores. P = α + β K + δ A where P = Practice towards COFLU-20 score K = Knowledge towards COFLU-20 score A = Attitude towards COFLU-20 score The data are processed using MINITAB and the output in Exhibit 1 below was obtained: Exhibit 1 The regression equation is ************ Predictor Coef SE t-ratio P Constant 4.755 0.462 10.282 0 Knowledge 0.8 0.039 2.055 0.041 Attitude 0.024 0.6 0.393 0.695 R-sq = 81.5% Analysis of Variance SOURCE DF SS MS F P Regression 2 38.06 19.03 2.284 0.104 Error 297 2474.887 8.333 Total 299…
Finally, the researcher is interested in examining the regression model for knowledge, attitude and practices towards the COFLU-20. The following model was developed to forecast individual practices towards COFLU-20 using knowledge and attitude scores. P = α + β K + δ A where P = Practice towards COFLU-20 score K = Knowledge towards COFLU-20 score A = Attitude towards COFLU-20 score The data are processed using MINITAB and the output in Exhibit 1 below was obtained: Exhibit 1 The regression equation is ************* Predictor Coef SE t-ratio P Constant 4.755 0.462 10.282 0 Knowledge 0.8 0.039 2.055 0.041 Attitude 0.024 0.6 0.393 0.695 R-sq = 81.5% Analysis of Variance SOURCE DF SS…
QUESTION 2 XXX Electric Illuminating Company is doing a survey on the relationship between electricity used in kilowatt-hours (thousand) and the number of rooms in a private single-family residence. A random sample of 10 homes was selected and the electricity consumption recorded as below. ii. Find a suitable linear regression equation ? = ? + ??. iii. Determine the number of kilowatt-hours (thousand) for an eleven-room residence.
Ch 13. 7: Refer to the Lincolnville School District bus data. First, add a variable to change the type of engine (diesel or gasoline) to a qualitative variable. If the engine type is diesel, then set the qualitative variable to 0. If the engine type is gasoline, then set the qualitative variable to 1. Develop a regression equation using statistical software with maintenance cost as the dependent variable and age, odometer miles, miles since last maintenance, and engine type as the independent variables.