document.write( "Question 1163538: The accompanying table shows a portion of a data set that refers to the property taxes owed by a homeowner (in $) and the size of the home (in square feet) in an affluent suburb 30 miles outside New York City.
\n" ); document.write( "Taxes Size
\n" ); document.write( "21902 2370
\n" ); document.write( "17316 2352
\n" ); document.write( "18208 1930
\n" ); document.write( "15632 1137
\n" ); document.write( "43990 5626
\n" ); document.write( "33662 2575
\n" ); document.write( "15172 2222
\n" ); document.write( "16742 1923
\n" ); document.write( "18286 2041
\n" ); document.write( "16073 1267
\n" ); document.write( "15200 1398
\n" ); document.write( "36052 3085
\n" ); document.write( "31023 2869
\n" ); document.write( "42089 3343
\n" ); document.write( "14413 1632
\n" ); document.write( "38913 3902
\n" ); document.write( "25354 3940
\n" ); document.write( "22985 2550
\n" ); document.write( "16195 3563
\n" ); document.write( "29289 2898\r
\n" ); document.write( "\n" ); document.write( "1. Estimate the sample regression equation that enables us to predict property taxes on the basis of the size of the home. (Round your answers to 2 decimal places.)\r
\n" ); document.write( "\n" ); document.write( " Taxes =_____ +_____ Size.\r
\n" ); document.write( "\n" ); document.write( "2. Interpret the slope coefficient.
\n" ); document.write( "
\n" ); document.write( " a. As Size increases by 1 square foot, the property taxes are predicted to increase by $7.03.
\n" ); document.write( " b. As Property Taxes increase by 1 dollar, the size of the house increases by 7.03 ft.\r
\n" ); document.write( "\n" ); document.write( "3. Predict the property taxes for a 1,600-square-foot home. (Round coefficient estimates to at least 4 decimal places and final answer to 2 decimal places.)
\n" ); document.write( "
\n" ); document.write( " Taxes = _____
\n" ); document.write( "
\n" ); document.write( "
\n" ); document.write( "

Algebra.Com's Answer #854374 by CPhill(2264)\"\" \"About 
You can put this solution on YOUR website!
```python?code_reference&code_event_index=2
\n" ); document.write( "import numpy as np
\n" ); document.write( "import pandas as pd
\n" ); document.write( "from sklearn.linear_model import LinearRegression\r
\n" ); document.write( "\n" ); document.write( "# Data
\n" ); document.write( "taxes = np.array([21902, 17316, 18208, 15632, 43990, 33662, 15172, 16742, 18286, 16073, 15200, 36052, 31023, 42089, 14413, 38913, 25354, 22985, 16195, 29289])
\n" ); document.write( "size = np.array([2370, 2352, 1930, 1137, 5626, 2575, 2222, 1923, 2041, 1267, 1398, 3085, 2869, 3343, 1632, 3902, 3940, 2550, 3563, 2898])\r
\n" ); document.write( "\n" ); document.write( "# Reshape for regression
\n" ); document.write( "X = size.reshape(-1, 1)
\n" ); document.write( "y = taxes\r
\n" ); document.write( "\n" ); document.write( "# Fit model
\n" ); document.write( "model = LinearRegression()
\n" ); document.write( "model.fit(X, y)\r
\n" ); document.write( "\n" ); document.write( "# Get parameters
\n" ); document.write( "intercept = model.intercept_
\n" ); document.write( "slope = model.coef_[0]\r
\n" ); document.write( "\n" ); document.write( "print(f\"Intercept: {intercept}\")
\n" ); document.write( "print(f\"Slope: {slope}\")\r
\n" ); document.write( "\n" ); document.write( "# Prediction for 1600 sq ft
\n" ); document.write( "prediction = intercept + slope * 1600
\n" ); document.write( "print(f\"Prediction for 1600 sq ft: {prediction}\")\r
\n" ); document.write( "
\n" ); document.write( "\n" ); document.write( "```
\n" ); document.write( "```text?code_stdout&code_event_index=2
\n" ); document.write( "Intercept: 5929.920950651071
\n" ); document.write( "Slope: 7.029199798319719
\n" ); document.write( "Prediction for 1600 sq ft: 17176.64062796262\r
\n" ); document.write( "\n" ); document.write( "```
\n" ); document.write( "To find the relationship between property taxes and home size, we perform a simple linear regression analysis using the provided data.\r
\n" ); document.write( "\n" ); document.write( "### 1. Sample Regression Equation
\n" ); document.write( "Using the data provided, the calculated coefficients for the regression line ($\text{Taxes} = \beta_0 + \beta_1 \cdot \text{Size}$) are:
\n" ); document.write( "* **Intercept ($\beta_0$):** $5929.92$
\n" ); document.write( "* **Slope ($\beta_1$):** $7.03$\r
\n" ); document.write( "\n" ); document.write( "The estimated regression equation is:
\n" ); document.write( "$$\text{Taxes} = 5929.92 + 7.03 \cdot \text{Size}$$\r
\n" ); document.write( "\n" ); document.write( "---\r
\n" ); document.write( "\n" ); document.write( "### 2. Interpretation of the Slope Coefficient
\n" ); document.write( "The slope coefficient ($7.03$) represents the average change in the dependent variable (Taxes) for every one-unit increase in the independent variable (Size).\r
\n" ); document.write( "\n" ); document.write( "* **Correct Interpretation:** **a. As Size increases by 1 square foot, the property taxes are predicted to increase by $7.03.**\r
\n" ); document.write( "\n" ); document.write( "---\r
\n" ); document.write( "\n" ); document.write( "### 3. Prediction for a 1,600-Square-Foot Home
\n" ); document.write( "To predict the taxes for a house with a size of $1,600$ square feet, we use the regression equation with higher precision coefficients as requested:
\n" ); document.write( "* Intercept $\approx 5929.9210$
\n" ); document.write( "* Slope $\approx 7.0292$\r
\n" ); document.write( "\n" ); document.write( "$$\text{Taxes} = 5929.9210 + 7.0292 \times (1600)$$
\n" ); document.write( "$$\text{Taxes} = 5929.9210 + 11246.72$$
\n" ); document.write( "$$\text{Taxes} = 17176.641$$\r
\n" ); document.write( "\n" ); document.write( "Rounding to two decimal places, the predicted property taxes are:
\n" ); document.write( "**Taxes = $17,176.64**
\n" ); document.write( "
\n" );