A sales manager for a large department store believes that customer spending per visit with a sale is lower than customer spending without a sale, and would like to test that claim. A simple random sample of customer spending is taken from without a sale and with a sale. The results are shown below. Mean Variance Observations Hypothesized Mean Difference df t Stat P(T<=t) one-tail t Critical one-tail P(T<=t) two-tail t Critical two-tail Confidence Level Without sale With sale 82.904 71.316 1984.96 1826.41 150 200 0 328 -2.465 0.0071 -1.65 0.0142 -1.967 99% = -3 Samples from without sale: nwithout = Samples from with sale: nwith Point estimate for spending without sale: without = Ex: 1.234 Point estimate for spending with sale: with = Ex: 9 -2 -1 P = t = 0 Ex: 1.234 1 2 3

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A sales manager for a large department store believes that customer spending per visit with a sale is lower than customer spending without a sale and would like to test that claim. A simple random sample of customer spending is taken from visits without a sale and with a sale. The results are shown below.

|                   | Without sale | With sale |
|-------------------|--------------|-----------|
| **Mean**          | 82.904       | 71.316    |
| **Variance**      | 1984.96      | 1826.41   |
| **Observations**  | 200          | 150       |
| **Hypothesized Mean Difference** | 0          |           |
| **df**            | 328          |           |
| **t Stat**        | -2.465       |           |
| **P(T<=t) one-tail** | 0.0071    |           |
| **t Critical one-tail** | -1.65   |           |
| **P(T<=t) two-tail** | 0.0142    |           |
| **t Critical two-tail** | -1.967 |           |
| **Confidence Level** | 99%        |           |

**Graph Explanation:**
The graph is a bell-shaped curve illustrating a normal distribution with a blue shaded area on the left tail, indicating the critical region for a one-tailed t-test. The x-axis is labeled from -3 to 3, corresponding to t-scores. The curve is symmetric around zero.

**Equations:**
- Samples from without sale: \( n_{\text{without}} = \) Ex: 9
- Samples from with sale: \( n_{\text{with}} = \) 
- Point estimate for spending without sale: \( \bar{x}_{\text{without}} = \) Ex: 1.234
- Point estimate for spending with sale: \( \bar{x}_{\text{with}} = \) 

- \( p = \) Ex: 1.234
- \( t = \) 

This analysis aims to determine if there is a statistically significant difference in spending between customers visiting during sales versus those who do not.
Transcribed Image Text:A sales manager for a large department store believes that customer spending per visit with a sale is lower than customer spending without a sale and would like to test that claim. A simple random sample of customer spending is taken from visits without a sale and with a sale. The results are shown below. | | Without sale | With sale | |-------------------|--------------|-----------| | **Mean** | 82.904 | 71.316 | | **Variance** | 1984.96 | 1826.41 | | **Observations** | 200 | 150 | | **Hypothesized Mean Difference** | 0 | | | **df** | 328 | | | **t Stat** | -2.465 | | | **P(T<=t) one-tail** | 0.0071 | | | **t Critical one-tail** | -1.65 | | | **P(T<=t) two-tail** | 0.0142 | | | **t Critical two-tail** | -1.967 | | | **Confidence Level** | 99% | | **Graph Explanation:** The graph is a bell-shaped curve illustrating a normal distribution with a blue shaded area on the left tail, indicating the critical region for a one-tailed t-test. The x-axis is labeled from -3 to 3, corresponding to t-scores. The curve is symmetric around zero. **Equations:** - Samples from without sale: \( n_{\text{without}} = \) Ex: 9 - Samples from with sale: \( n_{\text{with}} = \) - Point estimate for spending without sale: \( \bar{x}_{\text{without}} = \) Ex: 1.234 - Point estimate for spending with sale: \( \bar{x}_{\text{with}} = \) - \( p = \) Ex: 1.234 - \( t = \) This analysis aims to determine if there is a statistically significant difference in spending between customers visiting during sales versus those who do not.
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