The assumptions of a regression model can be evaluated by plotting and analyzing the error terms.
Important assumptions in regression model analysis are
- There should be a linear and additive relationship between dependent (response) variable and independent (predictor) variable(s).
- There should be no correlation between the residual (error) terms. Absence of this phenomenon is known as auto correlation.
- The independent variables should not be correlated. Absence of this phenomenon is known as multi col-linearity.
- The error terms must have constant variance. This phenomenon is known as homoskedasticity. The presence of non-constant variance is referred to heteroskedasticity.
- The error terms must be normally distributed.
Hence we can conclude that the assumptions of a regression model can be evaluated by plotting and analyzing the error terms.
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Answer:
The required recursive formula is
Step-by-step explanation:
Mohamed decided to track the number of leaves on the tree in his backyard each year.
The first year there were 500 leaves
Each year thereafter the number of leaves was 40% more than the year before so that means
For the third year the number of leaves increase 40% than the year before so that means
Similarly for fourth year,
So we can clearly see the pattern here
Let f(n) be the number of leaves on the tree in Mohameds back yard in the nth year since he started tracking it then general recursive formula is
This is the required recursive formula to find the number of leaves for the nth year.
Bonus:
Lets find out the number of leaves in the 10th year,
So there will be 10330 leaves in the 10th year.
Answer:
add 12 for all sides I think
Step-by-step explanation:
but if 12 yd is on both sides you must add 12+12 and the after getting the answer add then with 12+12
Answer:
2 miles is your answer hope this helps!!!!!!
Step-by-step explanation:
