A +1>3guyz at least solve one question
1 answer:
You might be interested in
Answer:
The significance level α is 0.02.
Step-by-step explanation:
A hypothesis test for single mean can be performed to determine whether the average starting salary for a 21-25 year old college grad exceeds $52,000 per year.
The hypothesis is defined as follows:
<em>H₀</em>: The average starting salary for a 21-25 year old college grad does not exceeds $52,000 per year, i.e. <em>µ</em> ≤ 52,000.
<em>Hₐ</em>: The average starting salary for a 21-25 year old college grad exceeds $52,000 per year, i.e. <em>µ</em> > 52,000.
The information provided is:
<em>σ</em> = $5,745
<em>n</em> = 65
Also, if then the null hypothesis will be rejected.
Here, we need to compute the value of significance level <em>α</em>, the type I error probability.
A type I error occurs when we reject a true null hypothesis (H<em>₀</em>).
That is:
<em>α</em> = P (type I error)
<em>α</em> = P (Rejecting H<em>₀</em>| H<em>₀</em> is true)
*Use a <em>z</em>-table for the probability.
Thus, the significance level α is 0.02.
Answer:
Step-by-step explanation:
Hello!
To compete in the touch screen phone market a manufacturer aims to release a new touch screen with a battery life said to last more than two hours longer than the leading product which is the desired feature in phones.
To test this claim two samples were taken:
Sample 1
X: battery lifespan of a unit of the new product (min)
n= 93 units of the new product
mean battery life X[bar]= 8:53hs= 533min
S= 84 min
Sample 2
X: battery lifespan of a unit of the leading product (min)
n= 102 units of the leading product
mean battery life X[bar]= 5:40 hs = 340min
S= 93 min
The population variances of both variances are unknown and distinct.
To test if the average battery life of the new product is greater than the average battery life of the leading product by 2 hs (or 120 min) the parameters of interest will be the two population means and we will test their difference, the hypotheses are:
H₀: μ₁ - μ₂ ≤ 120
H₁: μ₁ - μ₂ > 120
Considering that there is not enough information about the distribution of both variables, but both samples are big enough, we can apply the central limit theorem and approximate the distribution of both sample means to normal, this way we can use the standard normal:
Z≈N(0;1)
I hope this helps!