Question

A local real estate appraiser wants to develop a statistical model to predict the appraised value of houses in an area of town called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of main rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model: E(y) = ?? + ??x Where y is the appraised value of a house (in $10,000 units) and x is the number of main rooms. A regression analysis using data for n = 20 houses gave the following results: Estimated slope coefficient (estimate for ??) = 3.0 Standard error for the slope coefficient = 1.0 Test statistic for the slope coefficient t = 3.000, to 3 decimal places, n-2 = 18 df P-value for the slope coefficient = 0.00769 (to 5 decimal places). Is there significant evidence, at (alpha) ? = 0.05, of a linear relationship between appraised value (y) and number of main rooms (x)? What is your conclusion? The test results also give a 95% confidence interval for the slope coefficient ?? as (0.899, 5.101). Does this confidence interval back up your conclusion above?

          A local real estate appraiser wants to develop a statistical model to predict the appraised value of houses in an area of town called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of main rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model:

E(y) = ?? + ??x

Where y is the appraised value of a house (in $10,000 units) and x is the number of main rooms. A regression analysis using data for n = 20 houses gave the following results:

Estimated slope coefficient (estimate for ??) = 3.0
Standard error for the slope coefficient = 1.0
Test statistic for the slope coefficient t = 3.000, to 3 decimal places, n-2 = 18 df
P-value for the slope coefficient = 0.00769 (to 5 decimal places).

Is there significant evidence, at (alpha) ? = 0.05, of a linear relationship between appraised value (y) and number of main rooms (x)? What is your conclusion?

The test results also give a 95% confidence interval for the slope coefficient ?? as (0.899, 5.101). Does this confidence interval back up your conclusion above?

A local real estate appraiser wants to develop a statistical model to predict the appraised value of houses in an area of town called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of main rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model:

E(y) = ?? + ??x

Where y is the appraised value of a house (in 10,000 units) and x is the number of main rooms. A regression analysis using data for n = 20 houses gave the following results:

Estimated slope coefficient (estimate for ??) = 3.0
Standard error for the slope coefficient = 1.0
Test statistic for the slope coefficient t = 3.000, to 3 decimal places, n-2 = 18 df
P-value for the slope coefficient = 0.00769 (to 5 decimal places).

Is there significant evidence, at (alpha) ? = 0.05, of a linear relationship between appraised value (y) and number of main rooms (x)? What is your conclusion?

The test results also give a 95% confidence interval for the slope coefficient ?? as (0.899, 5.101). Does this confidence interval back up your conclusion above?

Added by Antonia P.

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