Answer: 24
Step-by-step explanation:
245-28= 217
217÷9= 24.1
Answer:
C.
Step-by-step explanation:
I just got it right
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:2
Step-by-step explanation:
Answer:
b. You would conclude that the differences in the average scores can be traced to differences in the working memory of the two groups.
Step-by-step explanation:
Though the average scores of the two sets could have lead to various conditions, but retentive ability deminishes with respect to an increase in age. With respect to the age of the elderly people involved, it is expected that some of them would not be able to retain information for a long period of time. Thus, their average score is 72%.
The college students' are younger, so it is expected that they should be able to retain more information. That ability is one of the reasons why their average score is 85%.
It can be concluded from the research that the differences in the average scores is probably due to the working memory of the two groups.
