True False 1.4 From the t-distribution table we can find values -ta, and ta, so that: P(-ta,
True False 1.4 From the t-distribution table we can find values -ta, and ta, so that: P(-ta,
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question
![True
False
| 1.13 B= P (type IIl error) = P(test statistic value is not in the rejection region
when H, is true)
| 1.14 The test statistic formula for testing a hypothesis concerning one variance
is x² :
(n-1)s²
1.15 In 1900 Karl Pearson proposed the following:
[nį – E (n¿)]²
[n¡ – np]?
x² = E
E(n,)
пр](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fd8a15d0f-2542-405a-ba8c-ae20af583e5b%2Fe0656201-0656-4698-9c03-8ebd0215fbee%2F48alax_processed.png&w=3840&q=75)
Transcribed Image Text:True
False
| 1.13 B= P (type IIl error) = P(test statistic value is not in the rejection region
when H, is true)
| 1.14 The test statistic formula for testing a hypothesis concerning one variance
is x² :
(n-1)s²
1.15 In 1900 Karl Pearson proposed the following:
[nį – E (n¿)]²
[n¡ – np]?
x² = E
E(n,)
пр
![True
False
1.4
From the t-distribution table we can find values -ta, and ta, so that:
P(-ta, <T < ta/, = 1 – a)
1.5
The small sample confidence interval for µ – 4z is developed from the
random variable
(Ý; – Ý2) – (41 – H2)
Z =
n2
1.6
The confidence interval for the standard deviation, o² is:
((n – 1)S² (n – 1)s²\
xả/2
xỉ-a/2
1.7
Type I and Type Il errors can be reduced by increasing the population size
1.8
The test statistic (for small samples) when testing the hypothesis for ß; is
Bi – Bio
T =
S/ cu
1.9
The test statistic when testing the hypothesis of = až is: F =
F has
n1 – 1 and n2 – 1 degrees of freedom
| 1.10 When testing the hypothesis ơf = ož the following assumptions are
needed: independent samples from normal populations
1.11 Let Y,,Y2, .Y, be a random sample with E [Y,] = µ and V [Y}] = o². Then
n- 2
is an unbiased estimator for o?
| 1.12 An unbiased estimator for a? can be calculated by dividing the error sum
of squares SSE by (n-2)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fd8a15d0f-2542-405a-ba8c-ae20af583e5b%2Fe0656201-0656-4698-9c03-8ebd0215fbee%2Ff4ofq8n_processed.png&w=3840&q=75)
Transcribed Image Text:True
False
1.4
From the t-distribution table we can find values -ta, and ta, so that:
P(-ta, <T < ta/, = 1 – a)
1.5
The small sample confidence interval for µ – 4z is developed from the
random variable
(Ý; – Ý2) – (41 – H2)
Z =
n2
1.6
The confidence interval for the standard deviation, o² is:
((n – 1)S² (n – 1)s²\
xả/2
xỉ-a/2
1.7
Type I and Type Il errors can be reduced by increasing the population size
1.8
The test statistic (for small samples) when testing the hypothesis for ß; is
Bi – Bio
T =
S/ cu
1.9
The test statistic when testing the hypothesis of = až is: F =
F has
n1 – 1 and n2 – 1 degrees of freedom
| 1.10 When testing the hypothesis ơf = ož the following assumptions are
needed: independent samples from normal populations
1.11 Let Y,,Y2, .Y, be a random sample with E [Y,] = µ and V [Y}] = o². Then
n- 2
is an unbiased estimator for o?
| 1.12 An unbiased estimator for a? can be calculated by dividing the error sum
of squares SSE by (n-2)
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