For this case we must simplify the following expression:
By law of multiplication signs we have to:
We add similar terms:
We simplify:
Answer:
The answer is c by process of elimination
Answer:
d. Both I and II are false
Step-by-step explanation:
When there is a high degree of linear correlation between the predictors the errors are found.
The basic objective of the regression model is to separate the dependent and independent variables. So if the variables have high degree of linear correlation then the multi collinearity causes problems or has errors. It is not necessary that multi collinearity must be present with high degree of linear correlation.
For example we have 3 variable of heat length and time. And all of them have a high degree of correlation. With increase in heat and time the length increases . But for multi collinearity with the increase of time and decrease of heat length does not increase. So this causes errors.
y-hat = 135 + 6x + errors
The linear relationship between height and weight is inexact. The deterministic relation in such cases is then modified to allow the inexact relationship between variables and a non deterministic or probabilistic model is obtained which has error which are unknown random errors.
y- hat= a + bXi + ei (i=1,2,3...)
ei are the unknown random errors.
<u><em>So both statements are false.</em></u>
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Answer:
70
Step-by-step explanation:
First, put the values they gave you in order from least to greatest.
52 62, 72, 77
In order for the median to be 72, it has to be in the middle of the data set. Since there are 2 values on the left of 72, there must be 2 values on the right. The data set is also in order from least to greatest, so we know that the last value has to be over 72. Because it is placed to the right of 72. The only value of the options that isn't greater than 72 is 70.
Answer: k < 3.5
explanation: < means less than and > means more than and = means equal to so k < (less than) 3.5