The following data was collected to explore how the number of square feet in a house, the number of bedrooms, and the age of the house affectthe selling price of the house. The dependent variable is the selling price of the house, the first independent variable (x₁) is the square footage, thesecond independent variable (x₂) is the number of bedrooms, and the third independent variable (x) is the age of the house.Square Feet278616323054233222231661132629091793Effects on Selling Price of HousesNumber of Bedrooms Age56373732413214221032Selling Price274400137900116200159600135400201900170000182800217100Step 1 of 2: Find the p-value for the regression equation that fits the given data. Round your answer to four decimal places..Copy DataN Step 2 of 2: Determine if a statistically significant linear relationship exists between the independent and dependent variables at the 0.01 level ofsignificance. If the relationship is statistically significant, identify the multiple regression equation that best fits the data, rounding the answers tothree decimal places. Otherwise, indicate that there is not enough evidence to show that the relationship is statistically significant.

Square feetNumber of BedroomsAgeSelling price…

Answered: The following data was collected to…

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

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section: Chapter Questions

Problem 4SGR

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Similar questions

Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
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?
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?
Does Table 1 represent a linear function? If so, finda linear equation that models the data.
What is regression analysis? Describe the process of performing regression analysis on a graphing utility.
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.
The equation used to predict college GPA (range 0-4.0) is y = 0.17 +0.51x, +0.002x,, where x, is high school GPA (range 0-4.0) and x, is college board score (range 200-800). Use the multiple regression equation to predict college GPA for a high school GPA of 3.8 and a college board score of 500. The predicted college GPA for a high school GPA of 3.8 and a college board score of 500 is (Round to the nearest tenth as needed.)
Which pair of h0/h1 is used in the two sided test for the slope of a simple linear regression?

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