House #1 costs $500,000.
House #2 costs $165,000.
House #3 costs $1,695,000.
House #4 costs $480,000.
House #5 costs $380,000.
Frequency Distribution Table:
House #1 500,000
House #2 165,000
House #3 1,695,000
House #4 480,000
House #5 380,000
Mean: 644,000
(500,000+ 165,000+1,695,000+480,000+380,000)/5
- 644,000
The mean cost for a 5-bedroom house in California based on this data is $644,000.
Visual Representation:
Standard Deviation: 602364.0926
FORMULA
$$S = \sqrt{\frac{\sum(x - \bar{x})^2}{n-1}}$$
So, we plug-ins all the numbers,
$$S = \sqrt{\frac{(500,000-644,000)^2+(165,000-644,000)^2+(1,695,000-644,000)^2+(480,000-644,000)^2+(380,000-644,000)^2}{5-1}}$$
- 144,000+479,000+1,051,000+164,000+264,000
4
We made more digits in the following, to show how we solve it with the
144,000-2.0736E10
479,000-2.29441E11
1,051,000 1.104601E12
164,000-2.6896E10
264,000 6,9696E10
15.131811
Now, we divide the sum which is 15.131801, and divide it to 4, since 5
to 4.
- 15.131811
4
= 1451370000000
4
= 362842500000
S=602364.0926
Center of measure:
The best center of measure in our data would be the mean. The mean shows the average price for
a house in California, based on our data. The mean is $644,000.
Spread Measure:
The best spread measure for our data would be the interquartile range because it decrease the
effect of outliers, like house #3, and describe a better idea of what the middle ranging houses
would cost.
This is the inferential portion of the project. Essentially, you need to determine the type of
inferential methods (estimation or hypothesis testing) that apply to the data. This is where you
use your data to answer the research question(s). Do some estimation of parameters (ie.
confidence intervals). For example, suppose you wanted to know whether the proportion of males
who favor tax increases on everyone to pay down the national debt differs from the proportion of
females, you would test
HO:pm-Pf versus H1:pmépf
The ability to do these types of comparisons hinges on the type of demographic information you
collected in the original survey. So, be sure your survey has this type of data in it. State any final
conclusions you have found based on your survey results and data analysis. Conduct an
appropriate hypothesis test for your research objective.
1. Select the appropriate analysis calculation for your objective.
2. Conclude your analysis of the dataset you used in Stage 1.
3. Share what difficulties you may encounter and how you overcome these difficulties. In
other words, how would you do differently next time?