Move the shape down by one quadrant and flip it so where “U” was at, “W” is in its place and Vice versa
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The median number of minutes for Jake and Sarah are equal, but the mean numbers are different.
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For this, you never said the choices, but I’ve done this before, so I’m going to use the answer choices I had, and hopefully they are right.
Our choices are -
• The median number of minutes for Jake is higher than the median number of minutes for Sarah.
• The mean number of minutes for Sarah is higher than the mean number of minutes for Jake.
• The mean number of minutes for Jake and Sarah are equal, but the median number of minutes are different.
• The median number of minutes for Jake and Sarah are equal, but the mean number of minutes are different.
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So to answer the question, we neee to find the median and mean for each data set, so -
Jack = [90 median] [89.6 mean]
Sarah = [90 median] [89.5 mean]
We can clearly see the median for both is 90, so we can eliminate all the choices that say they are unequal.
We can also see that Jack has a higher mean (89.6) compared to Sarah (89.5).
We can eliminate all the choices that don’t imply that too.
That leaves us with -
• The median number of minutes for Jake and Sarah are equal, but the mean number of minutes are different.
Complete question is;
Regarding the violation of multicollinearity, which of the following description is wrong?
a. It changes the intercept of the regression line.
b. It changes the sign of the slope.
c. It changes the slope of the regression line.
d. It changes the value of F-tests.
e. It changes the value of T-tests
Answer:
a. It changes the intercept of the regression line
Step-by-step explanation:
Multicollinearity is a term used in multiple regression analysis to show a high correlation between independent variables of a study.
Since it deals with independent variables correlation, it means it must be found before getting the regression equation.
Now, looking at the options, the one that doesn't relate with multicollinearity is option A because the intercept of the regression line is the value of y that is predicted when x is 0. Meanwhile, multicollinearity from definition above does in no way change the intercept of the regression line because it doesn't predict the y-value when x is zero.
Answer:
1. <1 = <2 - given
2. <1 and <3 are vertical <s - def of vertical <s
3. <1 = <3 - vertical angles theorem
4. <3 = <2 - transitive property
5. p II q - converse of corresponding angles theorem
Step-by-step explanation:
edg 2020
