racing bikes. Brand Weight Price ($) A 17.8 2,100 B 16.1 6,350 14.9 8,370 15.9 6,200 17.2 4,000 F 13.1 8,600 16.2 6,000 H 17.1 2,680 17.6 3,400 14.1 8,000 ese data provided the estimated regression equation ý = 28,506 - 1,433x. For these data, SSE = 7,009,621.71 and SST = 51,682,800. Use the F test to determine whether the weight for a bike and the price are related at the 0.05 level of significa te the null and alternative hypotheses. O Hoi Bq = 0 H: B, = 0 O Hoi Bo = 0 H: Bo = 0 H: Bo = 0 Hi Bq < 0 d the value of the test statistic. (Round your answer to two decimal places.)

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## Data Analysis of Road-Racing Bikes

Consider the following data on \( x = \text{weight (pounds)} \) and \( y = \text{price (\$)} \) for 10 road-racing bikes.

| Brand | Weight (lbs) | Price ($) |
|-------|--------------|-----------|
| A     | 17.8         | 2,100     |
| B     | 16.1         | 6,350     |
| C     | 14.9         | 8,370     |
| D     | 15.9         | 6,200     |
| E     | 17.2         | 4,000     |
| F     | 13.1         | 8,600     |
| G     | 16.2         | 6,000     |
| H     | 17.1         | 2,680     |
| I     | 17.6         | 3,400     |
| J     | 14.1         | 8,000     |

These data provided the estimated regression equation:
\[ 
\hat{y} = 28,506 - 1,433x 
\]

- **Sum of Squared Errors (SSE):** 7,009,621.71
- **Total Sum of Squares (SST):** 51,682,800

Use the F test to determine whether the weight for a bike and the price are related at the 0.05 level of significance.

### Hypotheses

State the null and alternative hypotheses:

1. \( H_0: \beta_1 = 0 \)  
   \( H_a: \beta_1 \neq 0 \)
2. \( H_0: \beta_1 = 0 \)  
   \( H_a: \beta_1 > 0 \)
3. \( H_0: \beta_1 = 0 \)  
   \( H_a: \beta_1 < 0 \)

Find the value of the test statistic. (Round your answer to two decimal places.)

\[ \boxed{} \]
Transcribed Image Text:## Data Analysis of Road-Racing Bikes Consider the following data on \( x = \text{weight (pounds)} \) and \( y = \text{price (\$)} \) for 10 road-racing bikes. | Brand | Weight (lbs) | Price ($) | |-------|--------------|-----------| | A | 17.8 | 2,100 | | B | 16.1 | 6,350 | | C | 14.9 | 8,370 | | D | 15.9 | 6,200 | | E | 17.2 | 4,000 | | F | 13.1 | 8,600 | | G | 16.2 | 6,000 | | H | 17.1 | 2,680 | | I | 17.6 | 3,400 | | J | 14.1 | 8,000 | These data provided the estimated regression equation: \[ \hat{y} = 28,506 - 1,433x \] - **Sum of Squared Errors (SSE):** 7,009,621.71 - **Total Sum of Squares (SST):** 51,682,800 Use the F test to determine whether the weight for a bike and the price are related at the 0.05 level of significance. ### Hypotheses State the null and alternative hypotheses: 1. \( H_0: \beta_1 = 0 \) \( H_a: \beta_1 \neq 0 \) 2. \( H_0: \beta_1 = 0 \) \( H_a: \beta_1 > 0 \) 3. \( H_0: \beta_1 = 0 \) \( H_a: \beta_1 < 0 \) Find the value of the test statistic. (Round your answer to two decimal places.) \[ \boxed{} \]
**Find the p-value. (Round your answer to three decimal places.)**

p-value = [______]

**State your conclusion.**

- ○ Do not reject \( H_0 \). We cannot conclude that the relationship between weight (pounds) and price ($) is significant.
- ○ Do not reject \( H_0 \). We conclude that the relationship between weight (pounds) and price ($) is significant.
- ○ Reject \( H_0 \). We conclude that the relationship between weight (pounds) and price ($) is significant.
- ○ Reject \( H_0 \). We cannot conclude that the relationship between weight (pounds) and price ($) is significant.
Transcribed Image Text:**Find the p-value. (Round your answer to three decimal places.)** p-value = [______] **State your conclusion.** - ○ Do not reject \( H_0 \). We cannot conclude that the relationship between weight (pounds) and price ($) is significant. - ○ Do not reject \( H_0 \). We conclude that the relationship between weight (pounds) and price ($) is significant. - ○ Reject \( H_0 \). We conclude that the relationship between weight (pounds) and price ($) is significant. - ○ Reject \( H_0 \). We cannot conclude that the relationship between weight (pounds) and price ($) is significant.
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