Here we have "\\bar{X}=25500,n=128" and "\\sigma=3250"
i) Confidence interval is:
"\\bar{X}\\pm z_\\frac{\\alpha}{2}*\\frac{\\sigma}{\\sqrt{n}}"
Here, we have to find "95\\%" confidence interval, so "\\alpha=0.05"
Now, the value of "z_\\frac{\\alpha}{2}" Can be calculated using normal distribution table, which comes out to be "1.96"
So, the confidence interval will be
"25500\\pm 1.96*\\frac{3250}{\\sqrt{128}}"
"25500\\pm 6370\/\\sqrt{128}"
"25500\\pm 563.033"
"=[24936.967,26063.033]"
ii) Confidence interval is:
"\\bar{X}\\pm z_\\frac{\\alpha}{2}*\\frac{\\sigma}{\\sqrt{n}}"
Here, we have to find "99\\%" confidence interval, so "\\alpha=0.01"
Now, the value of "z_\\frac{\\alpha}{2}"
Can be calculated using normal distribution table, which comes out to be 2.58
So, the confidence interval will be
"25500\\pm 2.58*\\frac{3250}{\\sqrt{128}}"
"25500\\pm 8385\/\\sqrt{128}"
"25500\\pm 741.1363"
"[24758.8637,26241.1363]"
b)
Non-probability sampling represents a group of sampling techniques that help researchers to select units from a population that they are interested in studying. Some of them are:
Quota sampling: With proportional quota sampling, the aim is to end up with a sample where the strata (groups) being studied (e.g., males vs. females students) are proportional to the population being studied.
Convenience sampling: A convenience sample is simply one where the units that are selected for inclusion in the sample are the easiest to access.
Purposive sampling: Purposive sampling, also known as judgmental, selective or subjective sampling, reflects a group of sampling techniques that rely on the judgement of the researcher when it comes to selecting the units.
Self selection sampling: Self-selection sampling is appropriate when we want to allow units or cases, whether individuals or organisations, to choose to take part in research on their own accord.
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