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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Hhhhmmmmm...
13.50 for food
2.40 for drink
Since there is a voucher for 1/3 of food,
13.50 multiplied by 1/3 is 4.5 which is the discount
13.50-4.5=9
9 is the final cost for the food.
Add 9 to 2.40 for the drink which results in a total of 11.40
To find the amount charged for service you multiply 11.40 by .15
11.40 × .15 = 1.71
You add 1.71 to 11.40
The total is 13.11
Answer:
16
Step-by-step explanation:
3 times 6 is 18 and 12 divided by 6 is 2
18 - 2 =16
use the acronym PEMDAS
A= 30ft, because you only have to multiply LxW= A that is equal to 2x15=30ft
