A company would like to know if efficiency can be improved through a training course for employees. A sample of employees is taken. The time taken for an employee to complete a task before training and after training is observed. 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 n= Ex: 9 Before 33.1 16.81 30 Degrees of freedom: df = 0 29 -2.282 0.051 -1.699 0.025 -2.045 95% After 31.7 12.25 30 P = Ex: 1.234 -3 -2 -1 t = Ex: 1.234 Point estimate for before training: before Ex: 1.234 Point estimate for after training: after 0 2 3

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### Efficiency Improvement Analysis Through Training

A company would like to know if efficiency can be improved through a training course for employees. A sample of employees is taken. The time taken for an employee to complete a task before training and after training is observed.

#### Data Summary

|                  | Before | After  |
|------------------|--------|-------|
| **Mean**         | 33.1   | 31.7  |
| **Variance**     | 16.81  | 12.25 |
| **Observations** | 30     | 30    |

- **Hypothesized Mean Difference:** 0
- **Degrees of Freedom (df):** 29
- **t Statistic:** -2.282
- **P(T < t) one-tail:** 0.051
- **t Critical one-tail:** -1.699
- **P(T < t) two-tail:** 0.025
- **t Critical two-tail:** -2.045
- **Confidence Level:** 95%

#### Graph Explanation

The graph displays a standard bell curve representing the t-distribution, with labels for `p = Ex: 1.234` and `t = Ex: 1.234`.

#### Calculations

- **Sample Size (n):** \( n = \text{Ex: 9} \)
- **Point Estimate for Before Training (\( \bar{x}_{\text{before}} \)):** 33.1
- **Point Estimate for After Training (\( \bar{x}_{\text{after}} \))**: 31.7

### Key Terms Explained

- **Degrees of Freedom (df):** The number of values in a calculation that are free to vary.
- **t Statistic:** A ratio used in hypothesis testing.
- **P-Value:** Measures the probability that the observed data would occur by chance.
- **Confidence Level:** Indicates the reliability of the estimate.

This analysis demonstrates the statistical approach to evaluate the effectiveness of training on employee efficiency using samples, hypothesis testing, and t-distribution.
Transcribed Image Text:### Efficiency Improvement Analysis Through Training A company would like to know if efficiency can be improved through a training course for employees. A sample of employees is taken. The time taken for an employee to complete a task before training and after training is observed. #### Data Summary | | Before | After | |------------------|--------|-------| | **Mean** | 33.1 | 31.7 | | **Variance** | 16.81 | 12.25 | | **Observations** | 30 | 30 | - **Hypothesized Mean Difference:** 0 - **Degrees of Freedom (df):** 29 - **t Statistic:** -2.282 - **P(T < t) one-tail:** 0.051 - **t Critical one-tail:** -1.699 - **P(T < t) two-tail:** 0.025 - **t Critical two-tail:** -2.045 - **Confidence Level:** 95% #### Graph Explanation The graph displays a standard bell curve representing the t-distribution, with labels for `p = Ex: 1.234` and `t = Ex: 1.234`. #### Calculations - **Sample Size (n):** \( n = \text{Ex: 9} \) - **Point Estimate for Before Training (\( \bar{x}_{\text{before}} \)):** 33.1 - **Point Estimate for After Training (\( \bar{x}_{\text{after}} \))**: 31.7 ### Key Terms Explained - **Degrees of Freedom (df):** The number of values in a calculation that are free to vary. - **t Statistic:** A ratio used in hypothesis testing. - **P-Value:** Measures the probability that the observed data would occur by chance. - **Confidence Level:** Indicates the reliability of the estimate. This analysis demonstrates the statistical approach to evaluate the effectiveness of training on employee efficiency using samples, hypothesis testing, and t-distribution.
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