Use the set of points to compute the following. The slope of the regression line for this data is computed to be b₁ = -0.3865546, and the standard error of b₁is computed as s = 0.2373361.Part: 0 / 3Part 1 of 3yThe critical value for a 95% confidence interval is11 1518 95X17 22 17 11 622 1213Send data to ExcelFind the critical value for a 95% confidence interval for B₁ and round the answer to three decimal places.S13 12 13

Here given value of slope of regression line and standard error of slope.Significance level = 0.05No…

Answered: Find the critical value for a 95%…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.6: Regression And Median-fit Lines

Problem 4GP

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
Respiratory Rate Researchers have found that the 95 th percentile the value at which 95% of the data are at or below for respiratory rates in breath per minute during the first 3 years of infancy are given by y=101.82411-0.0125995x+0.00013401x2 for awake infants and y=101.72858-0.0139928x+0.00017646x2 for sleeping infants, where x is the age in months. Source: Pediatrics. a. What is the domain for each function? b. For each respiratory rate, is the rate decreasing or increasing over the first 3 years of life? Hint: Is the graph of the quadratic in the exponent opening upward or downward? Where is the vertex? c. Verify your answer to part b using a graphing calculator. d. For a 1- year-old infant in the 95 th percentile, how much higher is the walking respiratory rate then the sleeping respiratory rate? e. f.
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 fitted regression line for these data.
find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. In each case, there is sujficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. Altitude and Temperature Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded by the author during Delta Flight 1053 from New Orleans to Atlanta. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet).
For each gender, estimate an earnings regression where the dependent variable is Annual Earnings and the independent variable is Age. Report the results for each regression, and provide an interpretation of the coefficient associated with age. Female- annual earning: 49347.58 age: 42.81 education: 14.4 Male- Annual earning: 69984.54 age: 42.86 education: 14.11.
One of the residuals in a linear regression model is equal to 6.5. Other results from the model areMSE = 3.2, MSR = 4.5, SSE = 182, and SSR = 243. The value of the standardized residual is:a. 3.06b. 3.63c. 0.48d. 0.42e. None of the other answers is correct
The least-square regression line for the given data is y=-0.206x + 2.097. Determine the residual of a data point for which x= 1 and y=-3, rounding to three decimal places. X -5 y 11 OA. - 4.891 OB. - 1.715 O C. - 1.109 OD. 1.891 - 3 -6 4 8 1 - 3 - 1 -2 -2 1 ло 0 5 25 -5 3 6 -40 7
10) A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y).The results of the regression were:y=ax+b a=-0.767 b=31.009 r2=0.609961 r=-0.781 Use this to predict the number of situps a person who watches 7.5 hours of TV can do (to one decimal place)
The least-square regression line for the given data is y = -0.206x + 2.097. Determine the residual of a data point for which x = -3 and y = -6, rounding to three decimal places. X y -5 - 3 11 -6 OA. 2.715 OB. -6.333 O C. -8.715 OD. -3.285 4 8 1 - 3 - 1 -2 -2 1 ... 0 5 2 -5 3 6 -40 7
For a set of 10 pairs of values of x and y, the regression line of x on y is x 0; mean and standard deviation of y being 8 and 2 respectively. Later it is known that a pair (x = 3, y = 8) was wrongly recorded and the correct pair detected is (x = 8, y = 3). Find the correct regression line of x on y.
Find the correlation coefficient given the estimated regression equation = 25+6x and ² = .8754.

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Find the critical value for a 95% confidence interval for B, and round the answer to three decimal places. The critical value for a 95% confidence interval is X