Answer D-F **Time Series Data Analysis**Consider the following time series data:| Quarter | Year 1 | Year 2 | Year 3 ||---------|--------|--------|--------|| 1 | 4 | 6 | 7 || 2 | 2 | 3 | 6 || 3 | 3 | 5 | 6 || 4 | 5 | 7 | 8 |**(a) Choose the correct time series plot.**Four graphs labeled (i), (ii), (iii), and (iv) are presented, showing potential plots of the time series data. Each graph has:- X-axis labeled as "Time Period," with increments for each Year and Quarter (e.g., Year 1, Quarter 1 to Year 3, Quarter 4).- Y-axis labeled as "Time Series Values," indicating values from the dataset.**Descriptions of Graphs:**1. **Graph (i):** Shows a line graph with data points that match the provided table, indicating correct representation of the data.2. **Graph (ii):** Displays a decreasing trend with fluctuations, which does not match the table.3. **Graph (iii):** Shows an inconsistent pattern that does not reflect the data accurately.4. **Graph (iv):** Presents a pattern inconsistent with the provided table values.**Choose your answer:**- Select from the dropdown which plot correctly represents the time series data.**What type of pattern exists in the data?**- Use the dropdown to specify the pattern observed in the data (e.g., trend, seasonality, etc.).**Note:** The correct graph to select is (i) as it accurately reflects the given data. ### Instructions for Developing Forecast Models**(d)** Use a multiple regression model to create an equation that accounts for trend and seasonal effects in the data. Incorporate the dummy variables from part (b) to capture these seasonal effects and define a variable `t` for time such that `t = 1` for Quarter 1 in Year 1, `t = 2` for Quarter 2 in Year 1, and so on, up to `t = 12` for Quarter 4 in Year 3.#### Equation Format:\[ \text{Value} = \Box + \Box \times \text{Qtr1} + \Box \times \text{Qtr2} + \Box \times \text{Qtr3} + \Box \times t \]- Round answers to three decimal places.- Use a minus sign for negative numbers, even if preceded by a plus sign (e.g., -300).**(e)** Calculate quarterly forecasts for the next year using the model from part (d).- **Quarter 1 forecast**: \_\_\_\_\_\_- **Quarter 2 forecast**: \_\_\_\_\_\_- **Quarter 3 forecast**: \_\_\_\_\_\_- **Quarter 4 forecast**: \_\_\_\_\_\_*Note*: Do not round interim computations; round the final answers to three decimal places.**(f)** Determine the effectiveness of the model: compare the model from part (b) to the model from part (d).- Calculate the Mean Squared Error (MSE) for each. - **Model from part (b) MSE**: \_\_\_\_\_\_ - **Model from part (d) MSE**: \_\_\_\_\_\_- Select the more effective model from a dropdown menu.- Provide a justification for your answer.*Note*: Justifications will not be graded but may be reviewed. Inputs:- Text boxes for entering numerical answers.- A dropdown menu for selecting the preferred model based on MSE.- A text box for justifying the choice.

Given information: The time series data is given.

) Use a multiple regression model to develop an equation to account for trend and seasonal effects in the data. Use the dummy variables you developed in part (b) to capture seasonal efe such that t= 1 for Quarter 1 in Year 1, t= 2 for Quarter 2 in Vear 1,.t = 12 for Quarter 4 in Vear 3. If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) | Qer3 + Value = Qtri + Qtr2 + ) Compute the quarterly forecasts for next year based on the model you developed in part (d). Do not round your interim computations and round your final answer to three decimal places.