A football statistician is interested to see if the two teams have significantly different weights. What is the hypothesis test to be done? (Use 4, - Hz, where 4, is Team B and u, is Team A.) O Ho: Hy = Hz Ho: H = = 0.5 H1: H, = 0.5 O Ho: Hq = Hz O Hoi Hq> Hz B (b) What is the mean difference in weight (in pounds) between the two teams? (Use 4 - 42, where 4, is Team B and u, is Team A.) Ibs Which team, on average, had the higher weight? O Team A Team B O not enough information B (c) Assume the population variances for the two samples are equal (then df = n1 + n2 - 2). Find the t-statistic, critical value, and the p-value associated with the hypothesis test at the 99% confidence level. (Round your answers to three decimal places.) t-statistic critical value p-value B (d) At a 99% confidence level, should the null hypothesis be rejected or not? Fill in the blanks. The null hypothesis should -Select- . The mean weights between the two teams --Select-- V significantly different at the 99%% confidence level.

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**Oneway Analysis of Weight By Team**

**Graph Description:**
- The box plot compares the weights of two teams, labeled A and B. 
- The y-axis represents weight, and the x-axis lists the teams. 
- Each box plot shows the median (central line), quartiles (top and bottom of the box), and potential outliers (individual points). 

**Summary of Fit:**
- Rsquare: 0.000754
- Adj Rsquare: -0.03493
- Root Mean Square Error: 40.12822
- Mean of Response: 254.8667
- Observations (or Sum Wgts): 30

**t Test (Pooled t Test):**

**B - A:**
- Assuming equal variances:
  - Difference: 2.133
  - Std Err Dif: 14.673
  - t Ratio: 0.145397
  - DF: 28
  - Upper CL Dif: 42.677
  - Lower CL Dif: -38.411
  - Prob > |t|: 0.8854
  - Prob > t: 0.4427
  - Prob < t: 0.5573
  - Confidence: 0.95

**Analysis of Variance (ANOVA):**
- Source: 
  - Team: DF = 1, Sum of Squares = 34.133, Mean Square = 34.13, F Ratio = 0.0211, Prob > F = 0.8854
  - Error: DF = 28, Sum of Squares = 45209.333, Mean Square = 1614.62
  - C. Total: DF = 29, Sum of Squares = 45243.467

**Means for Oneway Anova:**
- Level: 
  - A: Number = 15, Mean = 253.800, Std Error = 10.375, Lower 95% = 232.55, Upper 95% = 275.05
  - B: Number = 15, Mean = 256.933, Std Error = 10.375, Lower 95% = 234.68, Upper 95% = 279.19
- Std Error uses a pooled estimate of error variance

**t Test (Assuming unequal variances
Transcribed Image Text:**Oneway Analysis of Weight By Team** **Graph Description:** - The box plot compares the weights of two teams, labeled A and B. - The y-axis represents weight, and the x-axis lists the teams. - Each box plot shows the median (central line), quartiles (top and bottom of the box), and potential outliers (individual points). **Summary of Fit:** - Rsquare: 0.000754 - Adj Rsquare: -0.03493 - Root Mean Square Error: 40.12822 - Mean of Response: 254.8667 - Observations (or Sum Wgts): 30 **t Test (Pooled t Test):** **B - A:** - Assuming equal variances: - Difference: 2.133 - Std Err Dif: 14.673 - t Ratio: 0.145397 - DF: 28 - Upper CL Dif: 42.677 - Lower CL Dif: -38.411 - Prob > |t|: 0.8854 - Prob > t: 0.4427 - Prob < t: 0.5573 - Confidence: 0.95 **Analysis of Variance (ANOVA):** - Source: - Team: DF = 1, Sum of Squares = 34.133, Mean Square = 34.13, F Ratio = 0.0211, Prob > F = 0.8854 - Error: DF = 28, Sum of Squares = 45209.333, Mean Square = 1614.62 - C. Total: DF = 29, Sum of Squares = 45243.467 **Means for Oneway Anova:** - Level: - A: Number = 15, Mean = 253.800, Std Error = 10.375, Lower 95% = 232.55, Upper 95% = 275.05 - B: Number = 15, Mean = 256.933, Std Error = 10.375, Lower 95% = 234.68, Upper 95% = 279.19 - Std Error uses a pooled estimate of error variance **t Test (Assuming unequal variances
### (a)
A football statistician is interested to see if the two teams have significantly different weights. What is the hypothesis test to be done? (Use \( \mu_1 - \mu_2 \) where \( \mu_1 \) is Team B and \( \mu_2 \) is Team A.)

- \( H_0: \mu_1 = \mu_2 \)
  
  \( H_1: \mu_1 < \mu_2 \)

- \( H_0: \mu_1 = 0.5 \)
  
  \( H_1: \mu_2 = 0.5 \)

- \( H_0: \mu_1 = \mu_2 \)
  
  \( H_1: \mu_1 \neq \mu_2 \) (This seems to be the correct choice considering the context.)

- \( H_0: \mu_1 > \mu_2 \)
  
  \( H_1: \mu_1 < \mu_2 \)

- Not enough information

### (b)
What is the mean difference in weight (in pounds) between the two teams? (Use \( \mu_1 - \mu_2 \) where \( \mu_1 \) is Team B and \( \mu_2 \) is Team A.)

\[ \_\_\_\_\_ \text{ lbs} \]

Which team, on average, had the higher weight?

- Team A
- Team B
- Not enough information

### (c)
Assume the population variances for the two samples are equal (then df = \( n_1 + n_2 - 2 \)). Find the t-statistic, critical value, and the p-value associated with the hypothesis test at the 99% confidence level. (Round your answers to three decimal places.)

- t-statistic: \[ \_\_\_\_ \]
- critical value: \[ \_\_\_\_ \]
- p-value: \[ \_\_\_\_ \]

### (d)
At a 99% confidence level, should the null hypothesis be rejected or not? Fill in the blanks.

The null hypothesis should \[ \text{---Select---} \] the mean weights between the two teams \[ \text{---Select---} \] significantly different at the 99% confidence level.
Transcribed Image Text:### (a) A football statistician is interested to see if the two teams have significantly different weights. What is the hypothesis test to be done? (Use \( \mu_1 - \mu_2 \) where \( \mu_1 \) is Team B and \( \mu_2 \) is Team A.) - \( H_0: \mu_1 = \mu_2 \) \( H_1: \mu_1 < \mu_2 \) - \( H_0: \mu_1 = 0.5 \) \( H_1: \mu_2 = 0.5 \) - \( H_0: \mu_1 = \mu_2 \) \( H_1: \mu_1 \neq \mu_2 \) (This seems to be the correct choice considering the context.) - \( H_0: \mu_1 > \mu_2 \) \( H_1: \mu_1 < \mu_2 \) - Not enough information ### (b) What is the mean difference in weight (in pounds) between the two teams? (Use \( \mu_1 - \mu_2 \) where \( \mu_1 \) is Team B and \( \mu_2 \) is Team A.) \[ \_\_\_\_\_ \text{ lbs} \] Which team, on average, had the higher weight? - Team A - Team B - Not enough information ### (c) Assume the population variances for the two samples are equal (then df = \( n_1 + n_2 - 2 \)). Find the t-statistic, critical value, and the p-value associated with the hypothesis test at the 99% confidence level. (Round your answers to three decimal places.) - t-statistic: \[ \_\_\_\_ \] - critical value: \[ \_\_\_\_ \] - p-value: \[ \_\_\_\_ \] ### (d) At a 99% confidence level, should the null hypothesis be rejected or not? Fill in the blanks. The null hypothesis should \[ \text{---Select---} \] the mean weights between the two teams \[ \text{---Select---} \] significantly different at the 99% confidence level.
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