Answer:
See Bottom!
Step-by-step explanation:
17) 9
18) 2.0000
19) 20000000000
20) 00.804
21) 266.0000000000
22) 15.00
23) 77.5
24) 830.0000000000
The unit rate would be found if you made a ratio first of 60:3600. You notice that 60 can go into 3600 by 60 times. This means you get a unit ratio of 1:60. This means that one clownfish cost 60 dollars.
The most common method for fitting a regression line is the method of least-squares. This method calculates the best-fitting line for the observed data by minimizing the sum of the squares of the vertical deviations from each data point to the line (if a point lies on the fitted line exactly, then its vertical deviation is 0). Because the deviations are first squared, then summed, there are no cancellations between positive and negative values.Example<span>The dataset "Televisions, Physicians, and Life Expectancy" contains, among other variables, the number of people per television set and the number of people per physician for 40 countries. Since both variables probably reflect the level of wealth in each country, it is reasonable to assume that there is some positive association between them. After removing 8 countries with missing values from the dataset, the remaining 32 countries have a correlation coefficient of 0.852 for number of people per television set and number of people per physician. The </span>r²<span> value is 0.726 (the square of the correlation coefficient), indicating that 72.6% of the variation in one variable may be explained by the other. </span><span>(Note: see correlation for more detail.)</span><span> Suppose we choose to consider number of people per television set as the explanatory variable, and number of people per physician as the dependent variable. Using the MINITAB "REGRESS" command gives the following results:</span>
<span>The regression equation is People.Phys. = 1019 + 56.2 People.Tel.</span>
Answer:
<h2>0.7</h2>
Step-by-step explanation:
The coefficient of determination which is also known as the R² value is expressed as shown;
Sum of square of total (SST)= sum of square of error (SSE )+ sum of square of regression (SSR)
Given SSE = 60 and SSR = 140
SST = 60 + 140
SST = 200
Since R² = SSR/SST
R² = 140/200
R² = 0.7
Hence, the coefficient of determination is 0.7. Note that the coefficient of determination always lies between 0 and 1.
10 x 7 = ?(70) 25 x 7 = ?(175) 175 + 70 = 245
