Find & interpret the y-intercept (a) if an interpretation is possible. If it’s not possible, just report the y-intercept and explain why it can not be interpreted practically Use your model to estimate the price of a car that has 100,000 miles. Is this prediction reasonable? Why or why not? Provide a brief conclusion or discussion of the results that includes one of the following: potential problems or sources of biasare the results what you expected?are there other variables that may also be useful to predict the price of a car **Simple Linear Regression Analysis****Regression Results:**- **Dependent Variable:** Sale Price- **Independent Variable:** Mileage**Regression Equation:**\[ \text{Sale Price} = 50103.948 - 0.40458624 \times \text{Mileage} \]**Sample Size:** 30- **Correlation Coefficient (R):** -0.51297305- **Coefficient of Determination (R-squared):** 0.26314135- **Estimate of Error Standard Deviation:** 9953.8887**Parameter Estimates:**| Parameter | Estimate | Standard Error | Alternative | Degrees of Freedom (DF) | T-Statistic | P-value ||-----------|------------|----------------|-------------|-------------------------|--------------|---------|| Intercept | 50103.948 | 3680.1341 | ≠ 0 | 28 | 13.614707 | <0.0001 || Slope | -0.40458624 | 0.12794686 | ≠ 0 | 28 | -3.1621427 | 0.0037 |**Analysis of Variance Table for Regression Model:**| Source | Degrees of Freedom (DF) | Sum of Squares (SS) | Mean Square (MS) | F-statistic | P-value ||--------|-------------------------|---------------------|------------------|------------|---------|| Model | 1 | 9.9071446e8 | 9.9071446e8 | 9.9991466 | 0.0037 || Error | 28 | 2.7742372e9 | 99079901 | | || Total | 29 | 3.7649517e9 | | | |**Explanation:**- The regression equation suggests that for each additional mile driven, the sale price decreases by approximately $0.40.- The negative correlation coefficient indicates a weak negative linear relationship between mileage and sale price.- The p-values for both the intercept and slope are less than 0.05, suggesting that both are statistically significant.- The F-statistic and its associated p-value indicate that the model is statistically significant. ### Scatter Plot: Relationship Between Mileage and Sale Price#### Description:This scatter plot illustrates the relationship between the mileage of vehicles and their sale prices. The x-axis represents the mileage of the vehicles, ranging from 0 to 70,000 miles. The y-axis denotes the sale price, which ranges from $30,000 to $70,000.#### Key Observations:- The data points are scattered throughout the plot, indicating the individual sale prices for varying mileages.- There is a visible concentration of data points in the mileage range of 10,000 to 30,000 miles and sale prices from $30,000 to $50,000.- Higher sale prices, especially those above $60,000, tend to be associated with lower mileage.- As mileage increases beyond 50,000 miles, there appears to be a tendency for sale prices to cluster around the lower end of the price spectrum, although some outliers exist.#### Analysis:This plot can be used to assess how mileage impacts the sale price of vehicles, potentially useful for buyers and sellers to make informed decisions. Generally, it is often observed that lower mileage cars are able to command higher prices, likely due to wear and tear factors.

Hello! As you have posted more than 3 sub parts, we are answering the first 3 sub-parts. In case you require the unanswered parts also, kindly re-post that parts separately. 1. From the given output, the value of intercept is 50103.948. Interpretation: The sales price of a car constantly increases by 50103.948 dollar, by considering the mileage as 0. 2. The estimate of the price of a car that has 100,000 miles is calculated below: From the output, the regression equation is Sale Price=50103.948-0.40458624Mileage Substitute Mileage=100000 in regression equation. Therefore, Sale Price=50103.948-0.40458624*100000 =9645.324 From the above prediction, the sales price was predicted as low for the large value of Mileage. Hence, the prediction is reasonable. 3. From the output, the value of R^2=0.2631. That is, 26.31% of the variation is explained by the regression model. Yes, the results are as expected. If the Mileage increases, then the price of a car decreases. If the mileage decreases, then the price of a car increases. There may be chance for other variables that may also be useful to predict the price of a car. For example, the maintenance of a car, model of a car are also predicts the price of a car.