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 = 0.940x + 34. 128. Find the coefficient of determination andinterpret the result.785.8784.51032.7963.81211.5Money raised, xMoney spent, y456.6654.6733.8449.4691.4734.6764.4739.31018.6930.31172.9Find the coefficient of determination and interpret the result.2 = 0.989(Round to hree decimal places as needed.)

The coefficient of determination is the percentage of variation in y explained by the variable x.…

Answered: The money raised and spent (both in…

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter4: Linear Functions

Section: Chapter Questions

Problem 30PT: For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or...

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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?
For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy
For the following exercises, consider the data in Table 5, which shows the percent of unemployed in a city ofpeople25 years or older who are college graduates is given below, by year. 41. Based on the set of data given in Table 7, calculatethe regression line using a calculator or othertechnology tool, and determine the correlationcoefficient to three decimal places.
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?
In a fisheries researcher's experiment, the correlation between the number of eggs in the nest and the number of viable (surviving) eggs for a sample of nests is r = 0.67. The equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y = 0.72x + 17.07. For a nest with 140 eggs, what is the predicted number of viable eggs?
If we performed a t-test, how many people would be in the study based on the regression model in photo.
In the equation, Y’ = b0 + bxX, b0 represents the: Group of answer choices correlation between X and Y. slope coefficient of the regression line. intercept of the regression line with the Y-axis. predicted value of Y from knowing X.
A car dealer wants to estimate the price of a used car based on the age of the car and the mileage. Based on a sample of 20 cars, she determines the sample regression equation that predicts price taxes on the basis of the age (in years) of the number of miles is Price=21,619-1022Age-0.03 Miles (a) If the age of the car was fixed and the mileage was increased by 10,000, how much would the price increase or decrease and by how much? (b) Predict the selling price of a five-year-old car with 65,000 miles. (Round your answers to the nearest whole number.)
A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict y = the market price of a home (in $1,000s), using two independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. With this regression model, the predicted price of a 10-year old home with 2,500 square feet of living area is __________. $205.00 $200,000.00 $205,000.00 $255,000.00
A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict y = the market price of a home (in $1,000s), using independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. The regression coefficient of x2 suggests this: __________. If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes If the square feet area of living space is kept constant, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted increase of $2500 in the price of the homes Whatever be the square feet area of the living space, a 1 year increase in the age of the homes will result in a predicted drop of $2500 in the price of the homes
A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict y = the market price of a home (in $1,000s), using independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. The regression coefficient of x1 suggests this: __________. The addition of 1 square foot area of living space results in a predicted increase of $68.00 in the price of the home if the age of the home were held constant The addition of 1 square foot area of living space results in a predicted increase of $0.068 in the price of the home for homes of different ages The addition of 1 square foot area of living space results in a predicted increase of $68.00 in the price of the home with the age of the home allowed to vary The addition of 1 square foot area of living space results in a predicted increase of $0.068 in the price of the home if the age of the home were held constant
Assume a person got score of 32.5 on Test A and a score of 95.25 on Test B. Using the regression equation (B' = 2.3A + 9.5), what is the error of prediction for this person?

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