plz help asap. ### 17.5 Practice#### Plot 1:![Plot](/link-to-image)This plot shows data points over 16 quarters. The y-axis represents the data values ranging from 0 to 60, and the x-axis represents the quarters (t) from 2 to 16.#### Question a:What type of pattern exists in the data?**Answer:**- **Horizontal** (Correct)#### Question b:Use the following dummy variables to develop an estimated regression equation to account for seasonal effects in the data:- Qtr1 = 1 if Quarter 1, 0 otherwise- Qtr2 = 1 if Quarter 2, 0 otherwise- Qtr3 = 1 if Quarter 3, 0 otherwiseEnter negative values as negative numbers.Estimated regression equation:\[ \hat{Y} = \ \_ + \ \_ (\text{Qtr1}) + \ \_ (\text{Qtr2}) + \ \_ (\text{Qtr3}) \]#### Question c:Compute the quarterly forecasts for next year.- **Quarter 1 forecast**: $\_$ (Incorrect)- **Quarter 2 forecast**: $\_$ (Incorrect)- **Quarter 3 forecast**: $\_$ (Incorrect)- **Quarter 4 forecast**: $\_$ (Incorrect)#### Feedback:- **Partially Correct**#### Additional Features:- **Hints:** [Available]- **Check My Work:** [Available]#### Diagram Explanation:The plot displays quarterly data over a period, showing ups and downs which might suggest seasonal patterns or other variations over time. Each quarter may have unique data points influencing the regression analysis to determine the patterns accurately.For visual learners, reviewing the graph can help in understanding how the data points correlate and estimating regression helps in predicting future trends based on past data. The marked incorrect answers can guide users to reassess their calculations or approach to improve their understanding and accuracy. ## Exercise 17.28 Algo (Seasonality and Trend)### Consider the following time series.| Quarter | Year 1 | Year 2 | Year 3 ||:-------:|:------:|:------:|:------:|| 1 | 71 | 68 | 62 || 2 | 49 | 41 | 51 || 3 | 58 | 60 | 53 || 4 | 82 | 78 | 71 |**a. Construct a time series plot.**1. **Graph 1:** This time series plot displays quarterly data points over three years. The X-axis represents the quarters (from 1 to 12) and the Y-axis represents the values of the time series starting from 20 up to 80. The plot shows the following patterns: - A peak value in Quarter 4 for each year, indicating a seasonal high. - A trough typically found in Quarter 2, indicating a seasonal low. - A repeating pattern over the three years, suggesting seasonality in the data. 2. **Graph 2:** This graph replicates the same data in another plot format. Each point on the graph represents a specific quarter's value across the three years, helping to visualize trends and patterns over time more clearly.The graphs provide insight into seasonal fluctuations, which can help in forecasting and planning. The seasonal high in Quarter 4 and low in Quarter 2 appear consistently over the three-year period.

QuarterYear 1Year 2Year 31716862249415135860534827871

-40 20 Plot 1 What type of pattern exists in the data? S Quarter 2 forecast Quarter 3 forecast Horizontal # b. Use the following dummy variables to develop an estimated regression equation to account for seasonal effects in the data: Qtr1=1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2,0 otherwise; Qtr3= 1 if Quarter 3, 0 otherwise. Enter negative values as negative numbers. Qtrl + Qtr2 + c. Compute the quarterly forecasts for next year. Quarter 1 forecast Quarter 4 forecast 6 ▾ Hide Feedback 8 Partially Correct Quarter (t) 10 12 14 Qtr3

-40 20 Plot 1 What type of pattern exists in the data? S Quarter 2 forecast Quarter 3 forecast Horizontal # b. Use the following dummy variables to develop an estimated regression equation to account for seasonal effects in the data: Qtr1=1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2,0 otherwise; Qtr3= 1 if Quarter 3, 0 otherwise. Enter negative values as negative numbers. Qtrl + Qtr2 + c. Compute the quarterly forecasts for next year. Quarter 1 forecast Quarter 4 forecast 6 ▾ Hide Feedback 8 Partially Correct Quarter (t) 10 12 14 Qtr3

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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plz help asap.

### 17.5 Practice

#### Plot 1:
![Plot](/link-to-image)
This plot shows data points over 16 quarters. The y-axis represents the data values ranging from 0 to 60, and the x-axis represents the quarters (t) from 2 to 16.

#### Question a:
What type of pattern exists in the data?

**Answer:**
- **Horizontal** (Correct)

#### Question b:
Use the following dummy variables to develop an estimated regression equation to account for seasonal effects in the data:
- Qtr1 = 1 if Quarter 1, 0 otherwise
- Qtr2 = 1 if Quarter 2, 0 otherwise
- Qtr3 = 1 if Quarter 3, 0 otherwise

Enter negative values as negative numbers.

Estimated regression equation:
\[ \hat{Y} = \ \_ + \ \_ (\text{Qtr1}) + \ \_ (\text{Qtr2}) + \ \_ (\text{Qtr3}) \]

#### Question c:
Compute the quarterly forecasts for next year.

- **Quarter 1 forecast**: $\_$ (Incorrect)
- **Quarter 2 forecast**: $\_$ (Incorrect)
- **Quarter 3 forecast**: $\_$ (Incorrect)
- **Quarter 4 forecast**: $\_$ (Incorrect)

#### Feedback:
- **Partially Correct**

#### Additional Features:
- **Hints:** [Available]
- **Check My Work:** [Available]

#### Diagram Explanation:
The plot displays quarterly data over a period, showing ups and downs which might suggest seasonal patterns or other variations over time. Each quarter may have unique data points influencing the regression analysis to determine the patterns accurately.

For visual learners, reviewing the graph can help in understanding how the data points correlate and estimating regression helps in predicting future trends based on past data. The marked incorrect answers can guide users to reassess their calculations or approach to improve their understanding and accuracy.

## Exercise 17.28 Algo (Seasonality and Trend)

### Consider the following time series.

| Quarter | Year 1 | Year 2 | Year 3 |
|:-------:|:------:|:------:|:------:|
| 1 | 71 | 68 | 62 |
| 2 | 49 | 41 | 51 |
| 3 | 58 | 60 | 53 |
| 4 | 82 | 78 | 71 |

**a. Construct a time series plot.**

1. **Graph 1:** This time series plot displays quarterly data points over three years. The X-axis represents the quarters (from 1 to 12) and the Y-axis represents the values of the time series starting from 20 up to 80. The plot shows the following patterns:
- A peak value in Quarter 4 for each year, indicating a seasonal high.
- A trough typically found in Quarter 2, indicating a seasonal low.
- A repeating pattern over the three years, suggesting seasonality in the data.

2. **Graph 2:** This graph replicates the same data in another plot format. Each point on the graph represents a specific quarter's value across the three years, helping to visualize trends and patterns over time more clearly.

The graphs provide insight into seasonal fluctuations, which can help in forecasting and planning. The seasonal high in Quarter 4 and low in Quarter 2 appear consistently over the three-year period.

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Step 1: Write the given information.

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Step 2: Construct a time series plot using the given data:

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Step 3: Rearrange the data set that can be used for regression analysis.

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Step 4: Determine the regression equation to account for seasonal effects in the data.

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Step 5: Determine the regression equation to account for seasonal effects and trend.

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Step 6: Determine the quarterly forecast for next year.

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