Q1) Use following data set for prices for custom homes comparing square feet (in hundreds) and price (in thousands).
|
Square Feet
|
Price
|
|
26
|
259
|
|
27
|
274
|
|
33
|
315
|
|
29
|
296
|
|
29
|
325
|
|
34
|
380
|
|
30
|
359
|
|
40
|
523
|
|
22
|
215
|
a) Create a scatterplot for this data set in region to right.
b) Based on scatterplot, does it look like linear regression model is suitable for this data? Explain why or why not?
c) Add line-of-best fit (trend line/linear regression line) to your scatterplot. Write down the equation of the trend line below.
d) Use your trend line to forecast cost of a home with 2300 (23) square feet.
e) Find out the value of correlation coefficient. Describe what value tells you about data pairs?
f) Does value of correlation coefficient tell you there is or is not statistically significant evidence that correlation exists between square feet and price of custom homes? Describe your position.
g) What percent of variation in cost can be described by regression line in regard to knowing square footage? (HINT: review the meaning of the coefficient of determination value)