Data from a town, on 20 randomly selected houses that have been sold, include da
ID: 3257261 • Letter: D
Question
Data from a town, on 20 randomly selected houses that have been sold, include data on price (exist1000s) and size (1000 ft^2). These data produce the graphs and computer output from the accompanying table. Use the given results to complete parts a through e. click the icon to view the computer output click the icon to view the plots. a) Explain in words and numbers what the regression says. First explain in numbers. What is the regression model? Now explain in words. What does the regression model say? A. The model suggests that homes cost about exist10.220 per thousand square feet. B. The model suggests that homes cost about exist06.320 per thousand square feet. C. The model suggests that the price of homes increases about 06.32 thousand square feet per dollar D. The model suggests that the price of homes increases abo 10.22 thousand square feet per dollar. b) The intercept is negative. Discuss its value, taking note of the P-value. The value of intercept is (Do not round.) Does this value make any sense? A. A negative intercept is meaningless. but because the P-value is small the intercept is not significantly different from zero. B. The intercept must be zero for the regression model to be a good fit, but because the P-value is large, the intercept is not significantly different from zero. C. The intercept must be zero for the regression model to be a good fit, but because the P-value is small the intercept is not significantly different from zero. D. A negative intercept is meaningless, but because the P-value is large, the intercept is not significantly different from zero. c) The output reports s = 17.19. Explain what this means in this context... A. According to this model, the average sze of a house varies with a margin of error of 17.19 thousand square feet. B. The amounts by which the sizes of houses differ from predictions made by this model vary with a standard deviation of 17.10 thousand square feet. C. According to this model, the average price of a house varies with a margin of error of S17.19 thousand. D. The amounts by which house prices differ from predictions made by this model vary with a standard deviation of exist17.19 thousand. d) What's the value of the standard error of the slope of the regression line? The value of the standard error of the slope is e) Explain what that means in this context. A. The slopes of regression lines for models based on different samples of houses would vary with a standard deviation of about exist3.140 per thousand square feet. B. The slopes of regression lines for models based on different samples of houses would vary with a standard deviation of about 8.48 thousand square feet. C. Since the P-value is small, the slope of the model is within plusminus 8.48% of the slope of the regression line based on this sample. D. Since the P-value is small, the slope of the model is within plusminus 3.14% of the slope of the regression line based on this sample.Explanation / Answer
a)
The regression model is
Y = -10.22 + 96.32 X
The model suggests that the price of homes increases about 96.32 thousand square feet per dollar.
b) The value of intercept is -10.22
The intercept must be zero for the regression model to be a good fit, but because the p-value is large, the intercept is not significantly different from zero.
c) The amount by which the price of houses differ from predictions made by the model vary with a standard deviation 17.19 thousand.
d) The value of the standard error of the slope of the regression line is 3.14
e) Since p-value is small, the slope of the model is within +- 3.14 of the slope of the regression line based on this sample.
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