Answer:
Divide all of them by 5.
30 g onions
16 g carrots
0.5 tablespoons of oil
130 g tomatoes
0.3 I vegetable stock (whatever this is)
I hope this helps! If there are calculation mistakes, I’m sorry because I did this all in my head...
Answer:
The treatment should be stated by the four companies,since it more interested in the quality among each of the companies to be compared.
Step-by-step explanation:
From the example given, Since an electronic company wants to differentiate their cell phones quality to the cell phones from their three main competitors.
If ANOVA is used to determine the average number of defects, then the treatment should be defined for the four companies because it is more interested in comparing the quality among the different companies.
Answer:
Step-by-step explanation:
Its the second option
Ramiro's Method is
Answer:
The correlation coefficient "tell us" that the model in question does not fit the data well (the correlation coefficient is near zero), in whose case we need to find another that can do it.
Step-by-step explanation:
Roughly speaking, the correlation coefficient "tell us" if two variables could present the following behavior:
- As one variable increases, the other variable increases too. In this case, the correlation coefficient is high and positively correlated. As the correlation coefficient is near 1, the correlation between two quantitative variables is almost perfect.
- As one variable decreases, the other variable decreases too. In this case, the correlation coefficient is also high, but negatively correlated. As the correlation coefficient is near -1, this correlation is almost perfect for this case.
- There could be no correlation at all. In this case, the correlation coefficient is near a <em>zero value</em>.
As we can follow from the question, a correlation coefficient of 0.02 is near to zero. In this case, the correlation coefficient is "telling us" that the two variables do not follow the cases 1 and 2 above described. Instead, it follows the case 3.
Therefore, the model in question does not fit the data well, in whose case we need to find another that can do it. For example, if the model is linear, we need to test an exponential model.
It is important to remember that the correlation coefficient does not tell us anything about that one variable causes the other variable, only behaviors as described above.
Answer:
n = 78
Step-by-step explanation:
hope that helped!