Answer:
Step-by-step explanation:
Hello!
The variable of interest is X: number of criminals in the 10 Most Wanted list that were captured in a sample of 523 suspected criminals.
The number of criminals that were captured is 172 out of 523.
To calculate the sample proportion, you have to divide the number of success, in this case, captured criminals, by the total number of criminals sampled:
^p= 172/523= 0.329
I hope this helps!
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.
Learn more about regression model here
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Answer:
x = 7
Step-by-step explanation:
Because this is a right triangle, one angle is a right angle (90°). All triangles have all three angle measures adding up to 180°, so the other two angles' measures add up to 90° (90 + 90 = 180).
Combine like terms of the angle measures.
6x + 9x = 15x
-3 + (-12) = -15
15x - 15
Because the sum of the measures of the two angles have to equal 90,
15x - 15 = 90
Add 15 from both sides.
15x = 105
Divide both sides by 15.
x = 7
To check, substitute 7 in both expressions.
6(7) - 3 = 39
9(7) - 12 = 51
39 + 51 + 90 (the measure of the right angle) = 180, so the value of x is 7.
I hope this helped :)
Good job <span>Use the data below to construct a steam and leaf display on your own paper, then describe the distribution's shape</span>
