Independent variable is the predictor variable which is the height and dependent variable is the response variable which is weight in this scenario.
The square of correlation coefficient gives the coefficient of determination. It is denoted by R² (R squared).
We are given:
R = 0.75
So,
R² = 0.75²
R² = 0.5625
R² = 56.25 %
The coefficient of determination tells how much of the trend of dependent data can be explained by the independent data using the linear regression model. So in the given case, Height can explain 56.25% of the trend in the weight.
Answer:
184,549,376
Step-by-step explanation:
Answer: B. $430.80
Step-by-step explanation:
Given : Last year Baron Enterprises had $800 million of sales.
It had $270 million of fixed assets that were used at 65% (=0.65) of capacity last year.
Now, the used asset =
million
Now, Baron Enterprises had $800 million of sales in $175.5 million of assets , if we use all of $270 million of fixed assets , then the sales will be :-
![\dfrac{800}{175.5}\times270=1230.76923077\approx1230.80\text{ million}](https://tex.z-dn.net/?f=%5Cdfrac%7B800%7D%7B175.5%7D%5Ctimes270%3D1230.76923077%5Capprox1230.80%5Ctext%7B%20million%7D)
Now, the increase in Baron's sales before it is required to increase its fixed assets = ![\$1230.80-$800=\$430.80\text{ millions}](https://tex.z-dn.net/?f=%5C%241230.80-%24800%3D%5C%24430.80%5Ctext%7B%20millions%7D)
Hence, the increase in Baron's ( in million ) sales before it is required to increase its fixed assets = $430.80
Answer:
If David were summarizing the data from his sample, he would use Descriptive statistics. If he wanted to know whether or not his sample results could be generalized to the population, he would use Hypothesis testing statistics.
Step-by-step explanation:
Hello!
There are two types of statistics.
1. Descriptive statistics.
This method allows you to summarize the observed data of a sample, it gives you an idea of the data distribution shape, its variability, most common values, etc... You can summarize the data using numerical measures (for example: mean, median, mode, variance) or graphics (for example histogram, scatterplots, boxplots)
2. Hypothesis testing.
Using this method you can test the results of an experiment, using the previously summarized sample data, and reach a valid conclusion over your claims that can be generalized to the population of study afterward.
I hope it helps!
Answer:
End fraction right brace
Step-by-step explanation:
I really hope I helped GL <33!