Assume that adults have IQ scores that are normally distributed with a mean of mu equals μ=105 and a standard deviation sigma eq
1 answer:
The probability that a randomly selected adult has an IQ less than
135 is 0.97725
Step-by-step explanation:
Assume that adults have IQ scores that are normally distributed with a mean of mu equals μ = 105 and a standard deviation sigma equals σ = 15
We need to find the probability that a randomly selected adult has an IQ less than 135
For the probability that X < b;
- Convert b into a z-score using z = (X - μ)/σ, where μ is the mean and σ is the standard deviation
- Use the normal distribution table of z to find the area to the left of the z-value ⇒ P(X < b)
∵ z = (X - μ)/σ
∵ μ = 105 , σ = 15 and X = 135
∴
- Use z-table to find the area corresponding to z-score of 2
∵ The area to the left of z-score of 2 = 0.97725
∴ P(X < 136) = 0.97725
The probability that a randomly selected adult has an IQ less than
135 is 0.97725
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Answer:
The error sum of squares is SSE=12.97
The regression sum of squares is SSR=6.430
The total sum of squares is SST=19.4
Step-by-step explanation:
Linear regression is a way "to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables)".
Regression estimates are "used to describe data and to explain the relationship between one dependent variable and one or more independent variables"
The linear model is given by the following equation , where y is the dependent variable, x the independent variable, m the slope and b the intercept.
For this case we have the following info given:
n= 40 represent the sample size
The total sum of squares is given by this formula:
And from the formulas for a simple regression, we need to calculate the slope for the regression like this:
And if we replace the values given we have:
We have another useful equation in order to find the sum of squares for the regression, given by:
And we know this equivalence:
And solving for the sum of squares for the error we have:
So then we have the final solutions:
The error sum of squares is SSE=12.97
The regression sum of squares is SSR=6.430
The total sum of squares is SST=19.4
Answer:
1st one
Step-by-step explanation:
