A local real estate valuer wants to develop a statistical model to predict the value of properties in a certain suburb. One of the many variables thought to be an important predictor of real estate value is the floor area of the house. Let y = value of the property (in $) and x = floor area of the house (in square metres). Using data collected for a sample of n = 82 properties, the valuer created the following linear regression model:𝑃𝑟𝑜𝑝𝑒𝑟𝑡𝑦 𝑣𝑎𝑙𝑢𝑒^=196,600+2,185∗𝐹𝑙𝑜𝑜𝑟 𝑎𝑟𝑒𝑎Interpret the estimate of the slope of the line.Hint: The "slope" of a line and how to interpret it is first referenced in the Section 2.6 notes.Question 3Select one:When the floor area of a house is zero square metres (section only), I predict the property value to be $2,185.For each additional one square metre of floor area in the house, I predict the property value to increase by $2,185For each additional dollar of property value, I predict the floor area in the house to increase by 2,185 square metres.For a property with zero square metres of floor area in the house (section only), I predict the propoerty value to be $196,600.For each additional one square metre of floor area in the house, I predict the property value to increase by $196,600.Clear my choice

A local real estate appraiser wants to develop a statistical model to predict the appraised value of properties in a section of the suburb called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Let y = appraised value of the properties (in $thousands) and x = number of rooms. Using data collected for a sample of n = 74 properties in East Meadow, the appraiser fit the following linear regression model:𝐴𝑝𝑝𝑟𝑎𝑖𝑠𝑒𝑑 𝑉𝑎𝑙𝑢𝑒^=174.80+25.80∗𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑟𝑜𝑜𝑚𝑠Give a practical interpretation of the estimate of the y-intercept.Hint: When interpreting the "y-intercept" give consideration to whether it is a meaningful interpretation in context. Question 4Select one:When the number of rooms in the house is zero (section only), I predict that the property will an appraised value of $174,800.For each additional one room in the house, I predict the appraised value to increase $174,800.There is no practical interpretation, since a house with zero rooms (section only) is a nonsensical value.For each additional one room in the house, I predict the appraised value to increase $25,800.When the number of rooms in the house is zero (section only), I predict that the property will an appraised value of $25,800.

Question 6Your friend in the U.S. gives you a simple regression fit for predicting house prices from square feet. The estimated intercept is -44850 and the estimated slope is 280.76. You believe that your housing market behaves very similarly, but houses are measured in square meters. To make predictions for inputs in square meters, what slope must you use? Hint: there are 0.092903 square meters in 1 square foot. You do not need to round your answer.1 point

Suppose the linear regression line y = 3.009x – 77.131 predicts a pizza parlor's profits based on the number of pizzas sold. If x represents the number of pizzas sold, and y represents the pizza parlor's profits in dollars, about how much can the pizza parlor expect in profits if it sells 275 pizzas?A.$825B.$750C.$675D.$900SUBMITarrow_backPREVIOUS

The slope of a regression line represents:a.The average change in the dependent variable for a unit change in the independent variableb.The intercept of the regression linec.The correlation coefficient between the variablesd.The residual value in regression analysis

According to the inverse regression function and the regression slope and intercept from predicting prices from square-feet, what is the estimated square-feet for a house costing $800,000? You do not need to round your answer.