Two people present you with fits of their simple regression model for predicting house prices from square feet. You discover that the estimated intercept and slopes are exactly the same. This necessarily implies that these two people fit their models on exactly the same data set. 1 pointTrueFalse

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.

With Show least-squares fit line still selected, set r to 0.90. The points should be close tothe line, but not right on it. Below Generate new data set with: click Same r several times.A. Do all the least-squares fit lines for these scatter plots have the same slope?B. Do all the least-squares fit lines have the same y-intercept?C. What do all the least-squares fit lines have in common?A positive r indicates a positive correlation: as x increases, y also tends to increase.D. Set r to –0.90. Click Same r several times. What do the least-squares fit lines forthese scatter plots have in common?A negative r indicates a negative correlation: as x increases, y tends to decrease.

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

3. Set r to 0.00. Click Same r several times.A. Do all the least-squares fit lines for these scatter plots have the same slope?B. Do all the least-squares fit lines have the same y-intercept?C. What do all the least-squares fit lines have in common?When r = 0, there is no correlation in the data. This means that the value of y doesnot seem to be at all related to the value of x

Imagine a straight line through your data. Does the intercept of that line match what you would expect? Explain your reasoning.