*plz answer quick* the correlation coefficient between two quantitative variables is approximately 0.6. what does the value of this correlation coefficient indicate about how well the model fits the data? answer choice's a. the model is not a good fit. b. the model is a good fit. c. the correlation coefficient is not within the correct range. d. no conclusion can be drawn regarding how well the model fits the data.

A.  

The model is not a good fit.

Step-by-step explanation:

The correlation coefficient is a measure of the degree of association between two quantitative variables , such as weight and height.

On the other hand, the quantity R-squared is an indicator of the predictive power of a model. It is an indicator of how well the model fits the data. R-squared is the coefficient of determination.

R-squared = the square of the correlation coefficient

                  = 0.6 * 0.6

                  = 0.36

Therefore, only 36% of the variations in the dependent variable can be explained by the model. More than 50% can thus not be explained by the model. The model is thus not a good fit.

13

step-by-step explanation:

169-1=168

168-3=165

165-5=160

160-7=153

153-9=144

144-11=133

133-13=120

120-15=105

105-17=88

88-19=69

69-21=48

48-23=25

25-25=0

since it took 13times to completely subtract it hence its square root is 13

0.02

step-by-step explanation:

time of 9 192 631 770 periods of the radiation corresponding to the transition between the 2 hyperfine levels of the ground state of the caesium 133 atom at 0 k (but other seconds are sometimes used in astronomy)