Linearity? Independence? Normality? Equal variance? ***************** Analysis o
ID: 3224175 • Letter: L
Question
Linearity?
Independence?
Normality?
Equal variance?
*****************
Analysis of Variance
Source
DF
Sum of
Squares
Mean
Square
F Value
Pr > F
Model
1
4329644
4329644
21.38
<.0001
Error
28
5669427
202480
Corrected Total
29
9999071
Analysis of Variance
Source
DF
Sum of
Squares
Mean
Square
F Value
Pr > F
Model
1
4329644
4329644
21.38
<.0001
Error
28
5669427
202480
Corrected Total
29
9999071
1500 1000 500 500 500 1000 1500 2000 Predicted Value 1500 1000 500 500 1000 -2 -1 0 2 Quantile 50 40 1500 0 1500 Residual Fit Diagnostics for voplus 500 1000 1500 2000 Predicted Value 2500 2000 E 1500 1000 500 500 1500 2500 Predicted Value Fit-Mean Residual 1500 1000 500 500 0.0 0.4 0.8 0.00 0.8 Proportion Less 0.05 0.10 0.15 0.20 Leverage 0.4 0.3 0.2 0.0 0 5 10 15 20 25 30 Observation Observations 30 Parameter Error DF 28 MS 202480 R-Square 0.433 Adj R-Square 0.4128Explanation / Answer
Answer:
From the given information or the output for linear regression model, we have to check the assumptions for the regression analysis and also we have to write the conclusion for the overall regression model. The assumption of linearity is approximately followed by the given regression model as shown in the given figure. Although there is not strong linear association exists between the given variables but there is considerable amount of linear association exists which is useful for the prediction of the dependent variable. Also, from the given data, it is observed that the given variables are not independent from each other as there is a significant correlation or linear relationship exists between these variables. The assumption of normality is valid as the given data follows approximate normal distribution. The assumption of equal variance for the given regression model is not valid. The p-value for the given regression model is given as 0.0001 which is very less. This means we reject the null hypothesis that there is no any significant relationship exists between the dependent and independent variables. This means we conclude that there is sufficient evidence that there is a statistically significant relationship exists between the dependent and independent variables.
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