The second option: The student should have distributed and written -2x+5, not -2x-5.
That’s because the two negatives make a positive when multiplying/distributing.
Answer:
A. R2 = 0.6724, meaning 67.24% of the total variation in test scores can be explained by the least‑squares regression line.
Step-by-step explanation:
John is predicting test scores of students on the basis of their home work averages and he get the following regression equation
y=0.2 x +82.
Here, dependent variable y is the test scores and independent variable x is home averages because test scores are predicted on the basis of home work averages.
The coefficient of determination R² indicates the explained variability of dependent variable due to its linear relationship with independent variable.
We are given that correlation coefficient r= 0.82.
coefficient of determination R²=0.82²=0.6724 or 67.24%.
Thus, we can say that 67.24% of total variability in test scores is explained by its linear relationship with homework averages.
Also, we can say that, R2 = 0.6724, meaning 67.24% of the total variation in test scores can be explained by the least‑squares regression line.
The mean is simply the average of the data set.
Mean = (3+4+6+7+9+9+11)/7 = 7
The median, on the other hand, is the middle data when you arrange them from least to greatest. The middle data here is 7.
Hence, initially, the mean and median are equal. In order to make the mean less than median, add another data point which makes it the lowest. For example, we can add 1 as the new data point. The mean would be:
Mean = (1+3+4+6+7+9+9+11)/8 = 6.25
The median is (6+7)/2 = 6.5
Therefore, you can add any number less than 3. For example, that could be 1.
=>
÷
convert to improper fraction
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÷
convert to multiply
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×
simplify and solve
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×
=>
Answer:
D. ±11
Step-by-step explanation:
x = ±
which simplifies to:
x = 11 and x = -11 (which is basically ±11)
