please respond/summarize your thoughts on this post:

I collected 50 houses worth of data in my zip code of 98056. The excel spreadsheet is attached. I moved my data to Mini tab because I preferred the statistical software. I copied all my Mini tab calculations onto my Excel spreadsheet to demonstrate my work. Here are the steps I used for my regression analysis.

Step 1: I used house value (price) as the dependent variable. The independent variables included: the year the house was built, the house area, the lot area, how many bedrooms, how many bathrooms, number of garage spaces and number of house levels. Type of data set is cross section. I decided to use Alpha level 0.05 since this is what we default to if Alpha is not given in questions.

Step 2: A sample size of 50 was collected, so n30, so normality is applicable. The rule I used was Doane's Rule. n/k 5. At least 5 observations per predictor. n=50 and k=8 50/8 = 6.25, which is 5 because there are at least 5 observations per predictor.

Step 3: I made a scatter plot with regression to examine for linearity issues. This was run through Mini tab and I added the graph to my Excel document.

Step 4: I ran a regression analysis through Mini tab using the Fit Regression Model. The regression model summary was added to my Excel document.

Step 5: I checked the model for validity. I did notice a few outliers. The was one house I noticed when collecting data that was small and sold for a lower price, but it was on a very large lot. It stood out to me because it was a large lot of land for a low price. In Mini-tab I did a probability plot of SRES and the p-value was .404, which is greater than 0.05, so this should be normal. The probability plot of SRES is also on my Excel document.

Step 6: I checked the model for usefulness. The regression p-value was 0, so this is useful. The constant (intercept) p-value was 0.400, so this was useless. Year Built (slope) p-value was 0.371, so this was useless. House Area (slope) p-value was 0, so this was useful. Lot area (slope) p-value was 0.149, so this was useless. Bedroom (slope) p-value was 0.234, so this was useless. Bathroom (slope) p-value was 0.159, so this was useless. Garage Spaces (slope) p-value is 0.922, so this was useless. House level (slope) p-value was 0.814, so this was useless. Lot area, Garage Spaces, and House Level had a VIF less than 2, so those two variables would have mild to no multi collinearity. Whereas Year Built, House Area, Bedroom, and Bathroom had VIF numbers higher than 2, so these variables are showing multi collinearity. These variables are not showing strong multi collinearity, but there is more than mild variance inflation. There were some unusual observations. One I spoke to earlier was a small house that sold for a lower price, bit it was on over an acre of land, so the lot size was very large compared to the price it was sold for. This was a very unusual observation in my data set. There were also a few observations with large residual.

Step 7: This is the regression equation that I calculated in Mini tab and have added to my Excel document. House Value (Price) = -3041755 + 1686 Year Built + 263.9 House Area + 6.07 Lot Area - 54862 Bedroom + 71604 Bathroom - 4185 Garage Spaces + 18059 House Level. The R-squared was 65.89% and the R-squared (adjusted) was 60.20% I was able to see the coefficients of the different variables and the only one with a useful p-value was house area with a p-value of 0.000. All the other variables had p-values greater than Alpha. The constant (intercept) also had a useless p-value of 0.400. The S value was 186248. The constant coefficient is -3041755.

Step 8: In this discussion question we were asked to predict out house value. I used Mini tab to do a prediction for House Value (Price). I copied this from Mini tab and added it to my Excel document. I inputted the variables for my house data:

Year Built: 1942

House Area: 1250

Lot Area: 6373

Bedroom: 2

Bathroom:1

Garage Spaces: 0

House Level: 1

The prediction I received was the Fit value of 581495. In my opinion, this amount seems on the higher end, but then again the housing market is very hot right now and houses are selling for very high prices. The prediction I received did seem reasonable, it did not seem like it was an outlier value. This is a reasonable estimate for my house price.