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Phase 1: Data Preparation. 1. Download the Mount Pleasant Real Estate Data from stat.hawkeslearning.com and open it with Microsoft Excel. 2. To ensure the data contains comparable properties, eliminate duplexes and properties whose prices are outliers. What limitations does this impose on our analysis? 3. The statistical tools from the current chapter focus on numeric data, so eliminate non- numeric variables from the data. Does this remove potentially useful information? 4. Are there any redundant variables we could eliminate? Phase 2: Discovering Relationships 5. How strongly does each remaining variable correlate to the price? 6. Which variable correlates most strongly with price? 7. Are any variables weakly correlated with price? Practically speaking, why do you think this is true? 3.5 3 2.5 2020 2015 2 1.5 1 0.5 0 $0 $200,000 $400,000 $600,000 $800,000 $1,000,000 $1,200,000 2010 2005 2000 Price vs. Stories 1995 .. -00-00-0 0000 MA mas Price vs. Year Built 80%. ●● ● ● 000 ●● ● 1990 $0 $200,000 $400,000 $600,000 $800,000 $1,000,000 $1,200,000

Phase 1: Data Preparation. 1. Download the Mount Pleasant Real Estate Data from stat.hawkeslearning.com and open it with Microsoft Excel. 2. To ensure the data contains comparable properties, eliminate duplexes and properties whose prices are outliers. What limitations does this impose on our analysis? 3. The statistical tools from the current chapter focus on numeric data, so eliminate non- numeric variables from the data. Does this remove potentially useful information? 4. Are there any redundant variables we could eliminate? Phase 2: Discovering Relationships 5. How strongly does each remaining variable correlate to the price? 6. Which variable correlates most strongly with price? 7. Are any variables weakly correlated with price? Practically speaking, why do you think this is true? 3.5 3 2.5 2020 2015 2 1.5 1 0.5 0 $0 $200,000 $400,000 $600,000 $800,000 $1,000,000 $1,200,000 2010 2005 2000 Price vs. Stories 1995 .. -00-00-0 0000 MA mas Price vs. Year Built 80%. ●● ● ● 000 ●● ● 1990 $0 $200,000 $400,000 $600,000 $800,000 $1,000,000 $1,200,000

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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. 1 Phase 1: Data Preparation. 1. Download the Mount Pleasant Real Estate Data from stat.hawkeslearning.com and open it with Microsoft Excel. 2. To ensure the data contains comparable properties, eliminate duplexes and properties whose prices are outliers. What limitations does this impose on our analysis? 3. The statistical tools from the current chapter focus on numeric data, so eliminate non- numeric variables from the data. Does this remove potentially useful information? 4. Are there any redundant variables we could eliminate? Phase 2: Discovering Relationships 5. How strongly does each remaining variable correlate to the price? 6. Which variable correlates most strongly with price? 7. Are any variables weakly correlated with price? Practically speaking, why do you think this is true? 3.5 3 2.5 2 1.5 1 2020 2015 2010 2005 2000 1995 Price vs. Stories 0.5 0 $0 $200,000 $400,000 $600,000 $800,000 $1,000,000 $1,200,000 1990 ●● - *0% no m ●● Homme ● Price vs. Year Built 881 ●● • * ● ** ● $0 $200,000 $400,000 $600,000 $800,000 $1,000,000 $1,200,000
Transcribed Image Text:. 1 Phase 1: Data Preparation. 1. Download the Mount Pleasant Real Estate Data from stat.hawkeslearning.com and open it with Microsoft Excel. 2. To ensure the data contains comparable properties, eliminate duplexes and properties whose prices are outliers. What limitations does this impose on our analysis? 3. The statistical tools from the current chapter focus on numeric data, so eliminate non- numeric variables from the data. Does this remove potentially useful information? 4. Are there any redundant variables we could eliminate? Phase 2: Discovering Relationships 5. How strongly does each remaining variable correlate to the price? 6. Which variable correlates most strongly with price? 7. Are any variables weakly correlated with price? Practically speaking, why do you think this is true? 3.5 3 2.5 2 1.5 1 2020 2015 2010 2005 2000 1995 Price vs. Stories 0.5 0 $0 $200,000 $400,000 $600,000 $800,000 $1,000,000 $1,200,000 1990 ●● - *0% no m ●● Homme ● Price vs. Year Built 881 ●● • * ● ** ● $0 $200,000 $400,000 $600,000 $800,000 $1,000,000 $1,200,000
Phase 3: Constructing Predictive Models. 8. Find the regression line ŷ = b + b₁x predicting home price by the variable most highly correlated to it. What proportion of the variablity in home prices is explained by this variable in a straigt line model? Find the five number summary for this variable. Predict the price of homes at each value of this five number summary using your regression line. List Price $1,200,000 $1,000,000 $800,000 $600,000 $400,000 $200,000 0 0 Square Footage Line Fit Plot ◆ List Price ■ Predicted List Price 1000 2000 3000 4000 5000 6000 9. Find the 95% confidence interval for B₁, the slope of the population linear regression model. Also, conduct a hypothesis test for the significance of B₁, that is test, Ho: P₁ = 0 vs. Ha: P₁ = 0 and state the conclusion. Does this result and the confidence interval agree? If so, why?
Transcribed Image Text:Phase 3: Constructing Predictive Models. 8. Find the regression line ŷ = b + b₁x predicting home price by the variable most highly correlated to it. What proportion of the variablity in home prices is explained by this variable in a straigt line model? Find the five number summary for this variable. Predict the price of homes at each value of this five number summary using your regression line. List Price $1,200,000 $1,000,000 $800,000 $600,000 $400,000 $200,000 0 0 Square Footage Line Fit Plot ◆ List Price ■ Predicted List Price 1000 2000 3000 4000 5000 6000 9. Find the 95% confidence interval for B₁, the slope of the population linear regression model. Also, conduct a hypothesis test for the significance of B₁, that is test, Ho: P₁ = 0 vs. Ha: P₁ = 0 and state the conclusion. Does this result and the confidence interval agree? If so, why?
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