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.

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

An agent for a real estate company in a large city would like to be able to predict the monthly rental cost for apartments,based on the size of the apartment,as defined by square footage.A sample of eight apartments in a neighborhood was selected,and the information gathered revealed the data shown below.For these data,the regression coefficients are bo=373.5333 and b1=0.8553.Complete parts(a)through(d). Monthly Rent($); 9001,500,800,1600,1950,925,1825,1400, Size(Square Feet);850,1300,1050,1100,2000,650,1350,950 a.Determine the coefficient of determination, and interpret its (Round to three decimal places as needed.)

Read the following description of a data set.Faced with budget cuts, a state commission is looking into closing some of its public libraries. To help minimize the negative impact of the closures, the commission gathered data on local school and library facilities.The number of schools, x, and the number of libraries, y, were recorded for each town.The least squares regression line of this data set is:y=0.084x+1.743How many libraries does this line predict for a town with 5 schools?Round your answer to the nearest integer. libraries

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.

Situation: The Ipod Touch has been out for many years now and a lot of data has been collected.Relevant Relationship:There is a functional relationship between Price of an IPod Touch,𝑝 and Weekly Demand,𝑠.Below is a table of data that have been collectedPrice,𝑝,($) Weekly Demand,𝑠,(1,000s)150 208170 199190 200210 184230 182250 176A. Find the linear model that best fits this data using regression and enter the model below(for entry round the linear parameter value to nearest 0.01 and constant parameter to nearest 1)𝑠=  B. The squared correlation coefficient was 0.95(note: values less than 0.95 MAY mean the model is not appropriate for making predictions)Now answer these two questions using the UNROUNDED model parametersC. What does the model predict will be the weekly demand if the price of an ipod touch is $246 ? (nearest 100)D. According to the model at what should the price be set in order to have a weekly demand of 179,800 ipod Touches? $ (nearest $1)