Answer:
39 & —34
Step-by-step explanation:
ATTACHED PICTURE!
Answer:
Bias is the difference between the average prediction of our model and the correct value which we are trying to predict and variance is the variability of model prediction for a given data p[oint or a value which tells us the spread of our data the variance perform very well on training data but has high error rates on test data on the other hand if our model has small training sets then it's going to have smaller variance & & high bias and its contribute more to the overall error than bias. If our model is too simple and has very few parameters then it may have high bias and low variable. As the model go this is conceptually trivial and is much simpler than what people commonly envision when they think of modelling but it helps us to clearly illustrate the difference bewteen bias & variance.
Answer:
Range = 13
Mean = 8.3
Variance = 17.61
Step-by-step explanation:
Given the population dataset :
2, 9, 15, 4, 12, 9, 13, 6, 3, 10
1.) Range : (maximum - minimum)
Maximum = 15 ; minimum = 2
Range = (15 - 2) = 13
2.) population mean (μ) :
μ = ΣX / n
n = sample size
μ = (2 + 9 + 15 + 4 + 12 + 9 + 13 + 6 + 3 + 10) / 10
μ = 83 / 10
μ = 8.3
3.) Population variance (s²)
Σ(x - μ)² / n
=[(2 - 8.3)^2 + (9 - 8.3)^2 + (15 - 8.3)^2 + (4 - 8.3)^2 + (12 - 8.3)^2 + (9 - 8.3)^2 + (13 - 8.3)^2 + (6 - 8.3)^2 + (3 - 8.3)^2 + (10 - 8.3)^2] / 10
s² = 176.1 / 10
s² = 17.61
Answer:
Step-by-step explanation:
A. If children are considered people under the age of 18, then the answer would be
13 < a < 18 where the a represents age, where the inequality signs do not have an equals to because those ages are excluded
B. This inequality can be shown on a number line with open circles denoting the endpoints 13 and 18 as they are not included, and a line connecting these two points which represents the ages encompassed by the range
Answer:
3
Step-by-step explanation: