You have a data set consisting of the sales prices of houses in your neighborhood, with each sale time-stamped by the month and year in which the house sold. You want to predict the average value of houses in your neighborhood over time, so you fit a simple regression model with average house price as the output and the time index (in months) as the input. Based on 10 months of data, the estimated intercept is $4569 and the estimated slope is 143 ($/month). If you extrapolate this trend forward in time, at which time index (in months) do you predict that your neighborhood's value will have doubled relative to the value at month (index) 10?Please enter a number below, round to the nearest month.

Question 5Your 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 intercept must you use? Hint: there are 0.092903 square meters in 1 square foot. You do not need to round your answer.

The level at which commercial lending institutions set mortgage interest rates has a significant effect on the volume of buying, selling, and construction of residential and commercial real estate. A researcher collected data on annual average mortgage interest rates (for conventional, fixed-rate, 30-year loans) for the period 1987–2010, and run a linear trend regression of Y (average mortgage interest rate expressed as a percentage) on X (time period, X = 0, 1, 2......, n). The estimated equation has a Y-intercept of 10.50 and a slope of -0.24. The slope can be interpreted as a. The average mortgage interest rate set by commercial lending institutes is predicted to fall by 0.24% per year, on average. b. The average mortgage interest rate set by commercial lending institutes is predicted to rise by 10.26% per year, on average. c. The average mortgage interest rate set by commercial lending institutes is predicted to rise by 10.74% per year, on average. d. TThe average mortgage interest rate set by commercial lending institutes is predicted to rise by 10.50% per year, on average.

I predict house prices based on multiple features like area, bedrooms, and bathrooms. Which regression method am I?

Question 1Assume you fit a regression model to predict house prices from square feet based on a training data set consisting of houses with square feet in the range of 1000 and 2000. In which interval would we expect predictions to do best?

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