Answer:
Step-by-step explanation:
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:
It would be constant.
Step-by-step explanation:
Since there is no x value, the line will have no slope and just be horizontal about y= -2.
Answer:
Cost Price = Rs 10000
Step-by-step explanation:
Assume:
Cost of the item = x
Item was sold at a loss of 20%:
Loss = 20% of x = 0.2x
Item sold = x - 0.2x = 0.8x
Item sold at a profit of 10%:
Profit = 10% of x = 0.1x
item sold = x + 0.1x = 1.1x
Solve:
Difference = 1.1x - 0.8x = 0.3x
0.3x = Rs 3000
x = Rs 3000 ÷ 0.3
x = Rs 10000
