Executive Solution Overview
– a month ago **files (2)** – ## Chapter16DataSet2.xlsx – ## InstructionsW2.docx **## Chapter16DataSet2.xlsx** ## Chapter 16 Data Set 2 | | | | — | — | | Time | Correct | | 14.50 | 5 | | 13.40 | 7 | | 12.70 | 6 | | 16.40 | 2 | | 21.00 | 4 | | 13.90 | 3 | | 17.30 | 12 | | 12.50 | 5 | | 16.70 | 4 …
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– a month ago
**files (2)**
– ## Chapter16DataSet2.xlsx
– ## InstructionsW2.docx
**## Chapter16DataSet2.xlsx**
## Chapter 16 Data Set 2
| | |
| — | — |
| Time | Correct |
| 14.50 | 5 |
| 13.40 | 7 |
| 12.70 | 6 |
| 16.40 | 2 |
| 21.00 | 4 |
| 13.90 | 3 |
| 17.30 | 12 |
| 12.50 | 5 |
| 16.70 | 4 |
| 22.70 | 3 |
**## InstructionsW2.docx**
Instructions
The attached dataset (
Chapter 16 Data Set 2.xlsx [Download Chapter 16 Data Set 2.xlsx](https://henderson.instructure.com/courses/24639/files/2662987/download?download_frd=1) ) contains results from a timed test:
·
**Time** = average response time per item
·
**Correct** = number of guesses taken to answer correctly
For this discussion, you will use
**Excel** to analyze the relationship between the two variables.
✅ Tasks
1.
**Run a Regression**
· Use Excel’s
**Data Analysis → Regression** tool.
· Set
**Correct (number of guesses)** as the
_response (dependent variable)_.
· Set
**Time (average response time)** as the
_predictor (independent variable)_.
2.
**Interpret the Results**
· Is response time significant in predicting the number of guesses? (use the p-value at the 0.05 level).
· What does the
**sign of the coefficient** tell you about the relationship?
3.
**State the Regression Equation**
· Write the equation in the form:
Correct=a+b(Time)
where
_a_ = intercept and
_b_ = slope (from Excel output).
4.
**Make a Prediction**
· Using your equation, predict the number of guesses if the average response time is
**15 seconds**. (Insert 15 into your equation and calculate by hand.)
**## InstructionsW2.docx**
Instructions
The attached dataset (
Chapter 16 Data Set 2.xlsx [Download Chapter 16 Data Set 2.xlsx](https://henderson.instructure.com/courses/24639/files/2662987/download?download_frd=1) ) contains results from a timed test:
·
**Time** = average response time per item
·
**Correct** = number of guesses taken to answer correctly
For this discussion, you will use
**Excel** to analyze the relationship between the two variables.
✅ Tasks
1.
**Run a Regression**
· Use Excel’s
**Data Analysis → Regression** tool.
· Set
**Correct (number of guesses)** as the
_response (dependent variable)_.
· Set
**Time (average response time)** as the
_predictor (independent variable)_.
2.
**Interpret the Results**
· Is response time significant in predicting the number of guesses? (use the p-value at the 0.05 level).
· What does the
**sign of the coefficient** tell you about the relationship?
3.
**State the Regression Equation**
· Write the equation in the form:
Correct=a+b(Time)
where
_a_ = intercept and
_b_ = slope (from Excel output).
4.
**Make a Prediction**
· Using your equation, predict the number of guesses if the average response time is
**15 seconds**. (Insert 15 into your equation and calculate by hand.)
**## Chapter16DataSet2.xlsx**
## Chapter 16 Data Set 2
| | |
| — | — |
| Time | Correct |
| 14.50 | 5 |
| 13.40 | 7 |
| 12.70 | 6 |
| 16.40 | 2 |
| 21.00 | 4 |
| 13.90 | 3 |
| 17.30 | 12 |
| 12.50 | 5 |
| 16.70 | 4 |
| 22.70 | 3 |
**## InstructionsW2.docx**
Instructions
The attached dataset (
Chapter 16 Data Set 2.xlsx [Download Chapter 16 Data Set 2.xlsx](https://henderson.instructure.com/courses/24639/files/2662987/download?download_frd=1) ) contains results from a timed test:
·
**Time** = average response time per item
·
**Correct** = number of guesses taken to answer correctly
For this discussion, you will use
**Excel** to analyze the relationship between the two variables.
✅ Tasks
1.
**Run a Regression**
· Use Excel’s
**Data Analysis → Regression** tool.
· Set
**Correct (number of guesses)** as the
_response (dependent variable)_.
· Set
**Time (average response time)** as the
_predictor (independent variable)_.
2.
**Interpret the Results**
· Is response time significant in predicting the number of guesses? (use the p-value at the 0.05 level).
· What does the
**sign of the coefficient** tell you about the relationship?
3.
**State the Regression Equation**
· Write the equation in the form:
Correct=a+b(Time)
where
_a_ = intercept and
_b_ = slope (from Excel output).
4.
**Make a Prediction**
· Using your equation, predict the number of guesses if the average response time is
**15 seconds**. (Insert 15 into your equation and calculate by hand.)
**## Chapter16DataSet2.xlsx**
## Chapter 16 Data Set 2
| | |
| — | — |
| Time | Correct |
| 14.50 | 5 |
| 13.40 | 7 |
| 12.70 | 6 |
| 16.40 | 2 |
| 21.00 | 4 |
| 13.90 | 3 |
| 17.30 | 12 |
| 12.50 | 5 |
| 16.70 | 4 |
| 22.70 | 3 |
**## InstructionsW2.docx**
Instructions
The attached dataset (
Chapter 16 Data Set 2.xlsx [Download Chapter 16 Data Set 2.xlsx](https://henderson.instructure.com/courses/24639/files/2662987/download?download_frd=1) ) contains results from a timed test:
·
**Time** = average response time per item
·
**Correct** = number of guesses taken to answer correctly
For this discussion, you will use
**Excel** to analyze the relationship between the two variables.
✅ Tasks
1.
**Run a Regression**
· Use Excel’s
**Data Analysis → Regression** tool.
· Set
**Correct (number of guesses)** as the
_response (dependent variable)_.
· Set
**Time (average response time)** as the
_predictor (independent variable)_.
2.
**Interpret the Results**
· Is response time significant in predicting the number of guesses? (use the p-value at the 0.05 level).
· What does the
**sign of the coefficient** tell you about the relationship?
3.
**State the Regression Equation**
· Write the equation in the form:
Correct=a+b(Time)
where
_a_ = intercept and
_b_ = slope (from Excel output).
4.
**Make a Prediction**
· Using your equation, predict the number of guesses if the average response time is
**15 seconds**. (Insert 15 into your equation and calculate by hand.)
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