Size($) Rent(sq.feet) 850 1950 1450 2600 1085 2200 1232 2500 718 1950 1485 2700 1136 2650 726 1935 700 1875 956 2150 1100 2400 1285 2650 1985 3300 1369 2800 1175 2400 1225 2450 1245 2100 1259 2700 1150 2200 896 2150 1361 2600 1040 2650 755 2200 1000 1800 1200 2750 A real estate agent wishes to examine the relationship between the selling price of a home (measured in thousands of dollars) and its size (measured in square feet). In particular she wants to assess if the size of the home can accurately predict the house’s selling price. Use the sample data in the RENT spreadsheet to answer the following questions about the relationship. Which characteristic is the dependent variable? Y = Which characteristic is the independent variable? X = Create a scatter plot for the data set. What does this tell you about the relationship between the variables? 4. What are the values of the regression coefficients? b0 = b1 = 5. Write down the equation for the prediction line: Yi = The value of a house increases on average by how much for each additional one square foot of size? When the square footage of a house is 0 ft2, what is the average predicted selling price?
Inverse Normal Distribution
The method used for finding the corresponding z-critical value in a normal distribution using the known probability is said to be an inverse normal distribution. The inverse normal distribution is a continuous probability distribution with a family of two parameters.
Mean, Median, Mode
It is a descriptive summary of a data set. It can be defined by using some of the measures. The central tendencies do not provide information regarding individual data from the dataset. However, they give a summary of the data set. The central tendency or measure of central tendency is a central or typical value for a probability distribution.
Z-Scores
A z-score is a unit of measurement used in statistics to describe the position of a raw score in terms of its distance from the mean, measured with reference to standard deviation from the mean. Z-scores are useful in statistics because they allow comparison between two scores that belong to different normal distributions.
Please answer all these questions and label, data included:
| Size($) | Rent(sq.feet) |
| 850 | 1950 |
| 1450 | 2600 |
| 1085 | 2200 |
| 1232 | 2500 |
| 718 | 1950 |
| 1485 | 2700 |
| 1136 | 2650 |
| 726 | 1935 |
| 700 | 1875 |
| 956 | 2150 |
| 1100 | 2400 |
| 1285 | 2650 |
| 1985 | 3300 |
| 1369 | 2800 |
| 1175 | 2400 |
| 1225 | 2450 |
| 1245 | 2100 |
| 1259 | 2700 |
| 1150 | 2200 |
| 896 | 2150 |
| 1361 | 2600 |
| 1040 | 2650 |
| 755 | 2200 |
| 1000 | 1800 |
| 1200 | 2750 |
A real estate agent wishes to examine the relationship between the selling price of a home (measured in thousands of dollars) and its size (measured in square feet). In particular she wants to assess if the size of the home can accurately predict the house’s selling price. Use the sample data in the RENT spreadsheet to answer the following questions about the relationship.
- Which characteristic is the dependent variable? Y =
- Which characteristic is the independent variable? X =
- Create a
scatter plot for the data set. What does this tell you about the relationship between the variables?
4. What are the values of the regression coefficients?
- b0 =
- b1 =
5. Write down the equation for the prediction line:
- Yi =
- The value of a house increases on average by how much for each additional one square foot of size?
- When the square footage of a house is 0 ft2, what is the average predicted selling price?
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