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Question 1 (The following data is from Q1 of HW2) Suppose we wanted to predict the selling price ...
Question 1 Suppose we wanted to predict the selling price of a house using its size...
Question 1 Suppose we wanted to predict the selling price of a house using its size in a certain area of a city. A random sample of six houses were selected from the area. The data is presented in the following table with size given in hundreds of square feet, and sale price in thousands of dollars. Size (Xi) 12 15 18 21 24 27 Price (Yi) 60 85 75 105 120 110 a) Find the least squares estimate for the...
The AAA Club of a small town Emergency Road Service (ERS) to its members. By tracking the weather...
The AAA Club of a small town Emergency Road Service (ERS) to its members. By tracking the weather conditions the Club can divert resources from other Club activities to the ERS for projected peak days. This will lead to better services for Club members and also greatly reduces stress on the Club staff. So they want to determine if the number of calls the Club will receive in a day is predictable from the weather forecast given on the previous...
Question 1 Suppose we are given the data 2 -1 22: 2 -2 0 2 0...
Question 1 Suppose we are given the data 2 -1 22: 2 -2 0 2 0 -1 -2 -3 30 2 2 2 1 0 -1 0 -1 We aim at fitting the linear model Y; = Bo + Bizil + B22i2 + Ei, i = 1, 2, ..., 7. (1) Find the least square estimate B; (2) Find the R2 statistic; (3) Find ô2 and Cov(); (4) Find a 95% confidence interval for B1; (5) Test Ho: B1 =...
[a] We want to predict the price of houses from the size of the house (sarft). Please graph a scatterplot and see if the association is linear enough. (20 pts) There is a fairly linear association wi...
[a] We want to predict the price of houses from the size of the house (sarft). Please graph a scatterplot and see if the association is linear enough. (20 pts) There is a fairly linear association with very few outliers. Statkey Descriptive Statistics for Two Quantitative Variables Summary Statistics sih 013695 293546014 77.192 102713 445 650000 Sample Siee 0.788 140.211 11204 145 600000 550000 Scatterplot Controls a Show Regression Line 450000 300000 50000 1500 [b] Find the regression equation for...
5. 1 Data were collected for a random sample of 220 home sales from a U.S. community in 2003 Let Price denote the selling price (in $1000), BDR the number of bedrooms, Bath the number of bathrooms, H...
5. 1 Data were collected for a random sample of 220 home sales from a U.S. community in 2003 Let Price denote the selling price (in $1000), BDR the number of bedrooms, Bath the number of bathrooms, Hsize the size of the house (in sq. ft.), Lsize the lot size (in sq. ft.), Age the age of the house (in years), and Poor a binary variable that is equal to 1 if the condition of the house is reported as...
2. Use the data in hpricel.wfl uploaded on Moodle for this exercise. We assume that all assump- tions of the Classical Linear Model are satisfied for the model used in this question....
2. Use the data in hpricel.wfl uploaded on Moodle for this exercise. We assume that all assump- tions of the Classical Linear Model are satisfied for the model used in this question. (a) Estimate the model and report the results in the usual form, including the standard error of the regression. Obtain the predicted price when we plug in lotsize - 10, 000, sqrft - 2,300, and bdrms- 4; round this price to the nearest dollar. (b) Run a regression...
Could you teach me do this by Ti83or 84？ Below is data collected over 6 specific years. The data collected is the Consumer Price Index (CPT) and the cost of a slice of pizza We would...
Could you teach me do this by Ti83or 84？
Below is data collected over 6 specific years. The data collected is the Consumer Price Index (CPT) and the cost of a slice of pizza We would like to build a model using the CPI to predict the cost of a slice of pizza in a given year. Year 1960 1973 1986 1995 2002 2003 CPI (x) 30.2 48.3 112.3 162.2 191.9 197.8 Cost of a slice 0.15 0.35 1.00 1.25...
4. Suppose you have the following estimation results from the monthly data of January 1960 to...
4. Suppose you have the following estimation results from the monthly data of January 1960 to December 1972: yt = 2.03 +0.152: (1.2) (0.088) where the standard errors are in parentheses, Residual Sum of Squares (RSS)=18.48, and Explained Sum of Squares (ESS)=109.60. Assume that Classical Assump- tions for SLRM (Simple Linear Regression Model) hold. Do the following questions. Let ß be the population coefficient on It. (a) Determine the sample size. (b) Calculate R2. :B+0) under 10 % (c) Test...
A Realtor is interested in modeling the selling price of houses based on the square footage, the ...
A Realtor is interested in modeling the selling price of houses based on the square footage, the age of the house, and the style. The data was collected in the two largest cities in Arkansas and is presented in an excel file. We need two indicator variables for the style of the house. I will choose Traditional as the base category lif the house is a rambler lif the house is victorian rambler Oif not Now use Minitab output to...
The AAA Club of a small town Emergency Road Service (ERS) to its members. By tracking the weather conditions the Club can divert resources from other Club activities to the ERS for projected peak days. This will lead to better services for Club members and also greatly reduces stress on the Club staff. So they want to determine if the number of calls the Club will receive in a day is predictable from the weather forecast given on the previous...
Question 1 Suppose we are given the data 2 -1 22: 2 -2 0 2 0 -1 -2 -3 30 2 2 2 1 0 -1 0 -1 We aim at fitting the linear model Y; = Bo + Bizil + B22i2 + Ei, i = 1, 2, ..., 7. (1) Find the least square estimate B; (2) Find the R2 statistic; (3) Find ô2 and Cov(); (4) Find a 95% confidence interval for B1; (5) Test Ho: B1 =...
[a] We want to predict the price of houses from the size of the house (sarft). Please graph a scatterplot and see if the association is linear enough. (20 pts) There is a fairly linear association with very few outliers. Statkey Descriptive Statistics for Two Quantitative Variables Summary Statistics sih 013695 293546014 77.192 102713 445 650000 Sample Siee 0.788 140.211 11204 145 600000 550000 Scatterplot Controls a Show Regression Line 450000 300000 50000 1500 [b] Find the regression equation for...
5. 1 Data were collected for a random sample of 220 home sales from a U.S. community in 2003 Let Price denote the selling price (in $1000), BDR the number of bedrooms, Bath the number of bathrooms, Hsize the size of the house (in sq. ft.), Lsize the lot size (in sq. ft.), Age the age of the house (in years), and Poor a binary variable that is equal to 1 if the condition of the house is reported as...
2. Use the data in hpricel.wfl uploaded on Moodle for this exercise. We assume that all assump- tions of the Classical Linear Model are satisfied for the model used in this question. (a) Estimate the model and report the results in the usual form, including the standard error of the regression. Obtain the predicted price when we plug in lotsize - 10, 000, sqrft - 2,300, and bdrms- 4; round this price to the nearest dollar. (b) Run a regression...
Could you teach me do this by Ti83or 84？
Below is data collected over 6 specific years. The data collected is the Consumer Price Index (CPT) and the cost of a slice of pizza We would like to build a model using the CPI to predict the cost of a slice of pizza in a given year. Year 1960 1973 1986 1995 2002 2003 CPI (x) 30.2 48.3 112.3 162.2 191.9 197.8 Cost of a slice 0.15 0.35 1.00 1.25...
4. Suppose you have the following estimation results from the monthly data of January 1960 to December 1972: yt = 2.03 +0.152: (1.2) (0.088) where the standard errors are in parentheses, Residual Sum of Squares (RSS)=18.48, and Explained Sum of Squares (ESS)=109.60. Assume that Classical Assump- tions for SLRM (Simple Linear Regression Model) hold. Do the following questions. Let ß be the population coefficient on It. (a) Determine the sample size. (b) Calculate R2. :B+0) under 10 % (c) Test...
A Realtor is interested in modeling the selling price of houses based on the square footage, the age of the house, and the style. The data was collected in the two largest cities in Arkansas and is presented in an excel file. We need two indicator variables for the style of the house. I will choose Traditional as the base category lif the house is a rambler lif the house is victorian rambler Oif not Now use Minitab output to...
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