Weeks on Market Selling Price (in thousands of $) 18 125 48 73 51 61 33 76.5 56 53 9. 133 25 94 47 90 32 107 27 84 23 54 16 102
Weeks on Market Selling Price (in thousands of $) 18 125 48 73 51 61 33 76.5 56 53 9. 133 25 94 47 90 32 107 27 84 23 54 16 102
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Need help with 4, 5, and 6 both papers go hand in hand
![### Calculator Instructions for Regression Analysis
1. **Data Entry:**
- Input "Weeks on Market" into L1 (List 1).
- Input "Selling Price" into L2 (List 2).
2. **Accessing Regression Function:**
- Press [STAT].
- Use arrows to navigate to the CALC menu.
- Use arrows to go down to option 4: LinReg (ax+b).
3. **Data Selection:**
- Depending on your calculator version, select the list where your data is entered.
- Press the [ENTER] key.
### Analysis Questions
2. **Correlation Coefficient:**
- What is the value of the correlation coefficient? __________
3. **Interpreting Correlation:**
- Correlation indicates strength and direction. Discuss if your interpretation of this correlation matches the strength and direction found in the graph on page 1. Explain.
4. **Regression Coefficients:**
- Using calculator output from LinReg (ax+b), find:
- a = slope = __________
- b = y-intercept = __________
5. **Regression Equation:**
- Write out the regression equation: ____________________
6. **Prediction for Condos on Market:**
- Predict the selling price (in thousands of dollars) for a condo on the market for:
- **35 weeks:**
- **10 weeks:**
- **72 weeks:**
> This guide assists students in performing linear regression analysis to predict property sales based on market duration.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fafb35480-1da6-4606-ae06-eef29574bfe2%2F7d419356-e3c7-4316-bc67-6fde951e377d%2Fmra37pe_processed.jpeg&w=3840&q=75)
Transcribed Image Text:### Calculator Instructions for Regression Analysis
1. **Data Entry:**
- Input "Weeks on Market" into L1 (List 1).
- Input "Selling Price" into L2 (List 2).
2. **Accessing Regression Function:**
- Press [STAT].
- Use arrows to navigate to the CALC menu.
- Use arrows to go down to option 4: LinReg (ax+b).
3. **Data Selection:**
- Depending on your calculator version, select the list where your data is entered.
- Press the [ENTER] key.
### Analysis Questions
2. **Correlation Coefficient:**
- What is the value of the correlation coefficient? __________
3. **Interpreting Correlation:**
- Correlation indicates strength and direction. Discuss if your interpretation of this correlation matches the strength and direction found in the graph on page 1. Explain.
4. **Regression Coefficients:**
- Using calculator output from LinReg (ax+b), find:
- a = slope = __________
- b = y-intercept = __________
5. **Regression Equation:**
- Write out the regression equation: ____________________
6. **Prediction for Condos on Market:**
- Predict the selling price (in thousands of dollars) for a condo on the market for:
- **35 weeks:**
- **10 weeks:**
- **72 weeks:**
> This guide assists students in performing linear regression analysis to predict property sales based on market duration.

Transcribed Image Text:This educational material involves analyzing the relationship between weeks on the market and selling price, using the data provided below:
### Data Table
- **Weeks on Market:** 18 | **Selling Price (in thousands of $):** 125
- **Weeks on Market:** 48 | **Selling Price (in thousands of $):** 73
- **Weeks on Market:** 51 | **Selling Price (in thousands of $):** 61
- **Weeks on Market:** 33 | **Selling Price (in thousands of $):** 76.5
- **Weeks on Market:** 56 | **Selling Price (in thousands of $):** 53
- **Weeks on Market:** 9 | **Selling Price (in thousands of $):** 133
- **Weeks on Market:** 25 | **Selling Price (in thousands of $):** 94
- **Weeks on Market:** 47 | **Selling Price (in thousands of $):** 90
- **Weeks on Market:** 30 | **Selling Price (in thousands of $):** 107
- **Weeks on Market:** 27 | **Selling Price (in thousands of $):** 84
- **Weeks on Market:** 23 | **Selling Price (in thousands of $):** 54
- **Weeks on Market:** 16 | **Selling Price (in thousands of $):** 102
### Analysis Questions
Based on the given scatter plot:
- **Form:** Linear
- **Direction:** Negative
- **Strength:** Moderate
- **Outlier(s):** None
The task involves creating a scatter plot to visualize the data, helping in identifying trends such as the linear, negative, and moderate relationship between the length of time a property remains on the market and its selling price.
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