With a regression of machine hours and overhead, I got an R square of 0.913564068. That means there is a pretty high correlation and machine hours would be a good way to estimate overhead.
Challenge61_Data_ SierraLucas.xlsx (41.5 KB)
The best driver to use to predict the manufacturing overhead is machine hours as you can see in the screenshot below. The r-squared value is 0.913564068
meaning that the overhead cost and machine hours are very correlated and the p-value is essentially 0 meaning that our finding are statistically significant. Fun challenge.
Challenge61_Data.xlsx (35.0 KB)
I ran a correlation test to point me in the right direction as to which driver might be the most statistically significant driver for overhead.
Machine Hours turns out to be the best driver with an r squared value of .913 according to the regression test
Challenge61_Data.xlsx (50.3 KB)
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Machine hours is a much better way to allocate overhead because our regression table shows their r squared is .913, more than .05.
Challenge61_Data.xlsx (43.7 KB)
As I ran the regression with machine hours and manufacturing overhead, my R square was 0.913318508!
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Challenge61_Data.xlsx (41.4 KB)
Manufacturing hours is the most optimal MO rate!
Challenge61_Data.xlsx (62.8 KB)
The best cost drivers are machine hours and MOH
Challenge61_Data_KeyyanLugo.xlsx (62.9 KB)
R Square for Machine Hours|MOH is 0.913564. This proves that it is a much better cost driver than MOH|Headcount.
Challenge61_Data.xlsx (41.6 KB)
Here is my answer!
My R squared for manufacturing overhead and machine hours was .913 indicating that there was a strong correlation between the two!
Challenge61_Data.xlsx (30.6 KB)
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This is a very interesting challenge. It was very interesting to be able to run the regressions and find very key data. This could be very useful in many ways.
Challenge61_Data.xlsx (30.6 KB)
- It’s not quite $1000 on average, but actually $1138.55 on average.
- From how small the P-value and R Square are, the headcount and manufacturing overhead are not correlated.
- Not quite what I expected! The results show that the variables are almost perfectly correlated.
- R Square = 0.913564068
Challenge61_Jace Harrick .xlsx (76.7 KB)
Here was my solution. This was great in helping me grasp the concept of regression.
Because the R squared of machine hours is the highest with .91, I believe machine hours is the most accurate.
Challenge61_Data.xlsx (35.8 KB)
A great throwback to stats 121 and a reminder how powerful of a tool regression can be in making business decisions!
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Challenge61_Data.xlsx (41.5 KB)
Here’s my solution. It’s a little messy, but I forgot how much fun stats is!
Machine hours was the best cost driver for allocating overhead:
Tate_Telford_Challenge61_Data.xlsx (58.1 KB)
Challenge61_Data (1).xlsx (41.4 KB)
Here is my solution! Fun challenge!
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