Concept explainers
The following regression output was obtained from a study of architectural firms. The dependent variable is the total amount of fees in millions of dollars.
Predictor | Coefficient | SE Coefficient | t | p-value | ||||||||
Constant | 7.096 | 3.245 | 2.187 | 0.010 | ||||||||
x1 | 0.222 | 0.117 | 1.897 | 0.000 | ||||||||
x2 | -1.024 | 0.562 | -1.822 | 0.028 | ||||||||
x3 | -0.337 | 0.192 | -1.755 | 0.114 | ||||||||
x4 | 0.623 | 0.263 | 2.369 | 0.001 | ||||||||
x5 | -0.058 | 0.029 | -2.000 | 0.112 | ||||||||
Analysis of Variance | ||||||||||
Source | DF | SS | MS | F | p-value | |||||
Regression | 5 | 2,009.28 | 401.9 | 7.33 | 0.000 | |||||
Residual Error | 50 | 2,741.54 | 54.83 | |||||||
Total | 55 | 4,750.81 | ||||||||
x1 is the number of architects employed by the company.
x2 is the number of engineers employed by the company.
x3 is the number of years involved with health care projects.
x4 is the number of states in which the firm operates.
x5 is the percent of the firm’s work that is health care−related.
1 and 2 questions dont answer but C1,C2,C3 are mandatory
- Write out the regression equation. (Negative answers should be indicated by a minus sign. Round your answers to 3 decimal places.)
- How large is the sample? How many independent variables are there?
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c-1. At the 0.05 significance level, state the decision rule to test: H0: β1 = β2 = β3 =β4 = β5 = 0; H1: At least one β is 0. (Round your answer to 2 decimal places.)
---------------H0--------------------regression coefficient is not equal to 0
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c-2. Compute the value of the F statistic. (Round your answer to 2 decimal places.)
F value is
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c-3. What is the decision regarding H0: β1 = β2 = β3 = β4 = β5 = 0?
-----------------H0--------------Regression coefficient is not equal to zero
Reject Atleast one
Fail to reject No evidence that any
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- A regression analysis was performed to predict weight (y, in kg) using height (x, in cm) among 150 children. The coefficient of determination was . Which of the following is a valid interpretation? a. For each 1-cm increase in height, weight tends to increase by about 0.32 kg b. There is no association between weight and height c. Height accounts for about 32% of the total variability in weight d. The correlation between weight and height is about 0.32arrow_forwardListed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 273.3 mm. How does the result compare to the actual height of 1776 mm? Foot Length 281.9 278.3 253.2 258.7 278.7 257.8 274.2 262.2 Height 1784.8 1771.0 1675.6 1645.9 1858.7 1710.1 1789.2 1737.4 the regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 273.3 mm is enter your response here mm. (Round to the nearest integer as needed.)arrow_forwardIn a regression study, relating Price/unit (x) to Weekly Sales (in Kg.), with the scatter plot showing a strong negative direction, 63% of the variability in sales could be accounted for by the variation in the Unit Price. The correlation coefficient in this study is: 0.79 -0.4 -0.79 0.4arrow_forward
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