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We appreciate your visit to CONCEPT REVIEW Assignment Task Carefully review the chapter for terms concepts and related insight Submission Provide the following for each item as handwritten notes 1. This page offers clear insights and highlights the essential aspects of the topic. Our goal is to provide a helpful and engaging learning experience. Explore the content and find the answers you need!

**CONCEPT REVIEW Assignment**

- **Task**: Carefully review the chapter for terms/concepts and related insight.

- **Submission**: Provide the following for each item as handwritten notes:
1. **Concept Definition/Brief Description**: A couple of lines or statements.
2. **Insightful Note/Comment**: Clarify or illustrate the concept (e.g., related qualitative or quantitative statement, example, application).

- **Due Date**: Thursday, 11/4/21

- **Reading Assignment**:
- Focus on Chapter 6 (Introduction, Section 6, 6.2, and 6.3 ONLY).
- As you read/review, submit handwritten responses for items 2-32 listed below.

**Questions:**

1. Define ‘outcome’.
2. Define point estimate.
3. What is ‘margin of error’?
4. What is standard error (SE)?
5. Define confidence interval.
6. Interpret qualitatively (in words) what a confidence interval estimate implies.
7. What is the formula for determining CI (for one sample, continuous variable, sample size > 30)?
8. Illustrate the steps in applying question 7 above (may be with a simple numerical example).
9. How does your answer for question 7 change when sample size < 30?
10. Illustrate the steps in applying question 9 above (may be with a simple numerical example).
11. What is the formula for determining CI (for one sample, dichotomous variable)?
12. Illustrate the steps in applying question 11 above (may be with a simple example).

Answer :

Final answer:

Concepts in statistics include outcome, point estimate, margin of error, standard error, and confidence interval. An outcome refers to a possible result or value in an experiment or study. A point estimate is a single value used to estimate an unknown population parameter. The margin of error measures the uncertainty associated with a point estimate. Standard error quantifies the variability of sample statistics. A confidence interval is a range of values within which the true population parameter is likely to lie. The formula for determining a confidence interval depends on the type of variable and sample size. When the sample size is greater than 30, the formula involves the Z-score. When the sample size is less than 30, the t-distribution is used. Make sure to review Chapter 6 for detailed explanations and examples.

Explanation:

Concepts in Statistics

In statistics, there are several important concepts that help us understand and analyze data. Let's review some of these concepts:

1. Outcome:

An outcome refers to a possible result or value that can occur in an experiment or study. It can be a measurement, observation, or event.

2. Point Estimate:

A point estimate is a single value that is used to estimate an unknown population parameter based on sample data. It provides an estimate of the true value of the parameter.

3. Margin of Error:

The margin of error is a measure of the uncertainty or variability associated with a point estimate. It represents the maximum amount by which the estimate may differ from the true population parameter.

4. Standard Error (SE):

The standard error is a measure of the variability of sample statistics. It quantifies the average amount of variation or dispersion in the estimates that would be obtained from different samples of the same size.

5. Confidence Interval:

A confidence interval is a range of values within which we can be confident that the true population parameter lies. It is calculated using a point estimate, margin of error, and a specified level of confidence.

6. Formula for Determining CI (Continuous Variable, Sample Size > 30):

A confidence interval for a continuous variable with a sample size greater than 30 can be calculated using the following formula:

CI = point estimate ± (Z * SE)

Where:

  • CI: Confidence Interval
  • point estimate: The estimated value of the population parameter
  • Z: The Z-score corresponding to the desired level of confidence
  • SE: Standard Error

7. Steps for Applying the Formula (Continuous Variable, Sample Size > 30):

Let's illustrate the steps with a simple numerical example:

  2. Calculate the point estimate using sample data.
  4. Determine the desired level of confidence (e.g., 95%, 99%).
  6. Find the corresponding Z-score for the desired level of confidence.
  8. Calculate the standard error (SE) using the sample data.
  10. Apply the formula: CI = point estimate ± (Z * SE)
  12. Interpret the confidence interval in the context of the problem.

8. Changes for Sample Size < 30:

When the sample size is less than 30, the formula for determining the confidence interval for a continuous variable changes. In this case, we use the t-distribution instead of the Z-distribution. The steps for applying the formula remain the same, but the critical value is obtained from the t-table instead of the Z-table.

9. Formula for Determining CI (Dichotomous Variable):

A confidence interval for a dichotomous variable can be calculated using the following formula:

CI = point estimate ± (Z * SE)

Where:

  • CI: Confidence Interval
  • point estimate: The estimated proportion or percentage
  • Z: The Z-score corresponding to the desired level of confidence
  • SE: Standard Error

10. Steps for Applying the Formula (Dichotomous Variable):

Let's illustrate the steps with a simple example:

  2. Calculate the point estimate using sample data.
  4. Determine the desired level of confidence (e.g., 95%, 99%).
  6. Find the corresponding Z-score for the desired level of confidence.
  8. Calculate the standard error (SE) using the sample data.
  10. Apply the formula: CI = point estimate ± (Z * SE)
  12. Interpret the confidence interval in the context of the problem.

Make sure to carefully review Chapter 6 of the assigned material for a more detailed explanation and additional examples.

Learn more about concepts in statistics here:

https://brainly.com/question/30279153

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