Data Analysis and Predictive Modeling in Linear Regression

“On my honor, I, Wilkens Cadet have neither given nor received unauthorized aid in doing this assignment.” Chapter 1 - Data analysis ............................................................................................................................. 1 1.1 Scatterplot ......................................................................................................................................... 1 1.2 Linear Regression Summary ............................................................................................................... 1 1.3 Parameters ........................................................................................................................................ 1 1.4 Simple Linear Regression Equation .................................................................................................... 2 1.5 Predicted values ................................................................................................................................. 2 Chapter 2: Using the model ......................................................................................................................... 3 2.1 Applying the model to predict values in the dependent variable ...................................................... 3 2.2 Using the model to compare with other models ............................................................................... 3 Chapter 1 - Data analysis 1.1 Scatterplot In Figure 1 You can see the Expected Price on the Scatter Plot 0 5 10 15 20 25 30 35 0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 1800000 2000000 Expected Price Y' Linear (Y') Y Figure 1-Scatter Plot

Is it appropriate to perform a simple linear regression to this dataset? In other words, is the pattern of the dataset in the graph resembling a line? It is appropriate to perform a simple linear regression to the dataset because the data is constructed within a line. Although the argument can be made not to do to the fact that it may resemble a Parabola. 1.2 Linear Regression Summary In Figure 2 You can see the Linear Regression Summary Data Figure 2- Linear Regression Summary Data 1.3 Parameters In Figure 3 Is the values used to calculate the equation of the line. The original value was slightly different from the full manual and was therefore adjusted accordingly. Manual Value- x_=1544.703(1545) and Y_=$812,459($812,459). Intercept: -14,584 and slope: 535.

Figure 3- Manual Data 1.4 Simple Linear Regression Equation In Equation 1 you can find the generic equation of the line Y ' = ax + b Equation 1- Generic Equation Of The Line Sale s Price ' = 535.41 x − 14,583.68 In Figure 4 you can see the equation of the figure sales_price_predicted = value-of-b (name of column) + value-of-a Figure 4-Equation 1.5 Predicted values In Figure 5 you can see the Linear Regression Combo Chart used to showcase the Expected Price

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Figure 5- Linear Regression Combo Chart Chapter 2: Using the model 2.1 Applying the model to predict values in the dependent variable How much would be the price of a house if the client wants to buy a house with a living space of 2,500 sq. Ft? The Price of a house if the client wants to buy a house with a living s[ace of 2’500 would be $1,323,929 2.2 Using the model to compare with other models In the regression summary in Excel, you’ll find some statistics. One of them is R Square. This value indicated how close your data are to the fitted regression line. The closer to 1, the better the fit. Answer the following question based on the comparison between the R square you found, and the one in the case: 0 5 10 15 20 25 30 35 0 200000 400000 600000 800000 1000000 1200000 1400000 1600000 1800000 2000000 Expected Price Y' Linear (Y') Y

Another company created a regression model using more than one independent variable and got an R Square value of 0.7. Which model is better, yours, or the one created by the company? Why? Explain using evidence.

“On my honor

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