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#### Sampling and Estimation

###### Sample and Population
• Population: is a collection of all items
• Sample: is a subset of population used as a representation of the entire population
###### Central Limit Theorem
• If (X1, X2, …, Xn) is a random sample of size n, drawn from any population with mean μ and variance σ², then the sample has:
• Expected mean, μ
• Expected variance, σ²/n
• It forms a normal distribution:
• X ~ N (μ, σ²/n)
###### Examples
1. The weights of the trout at a trout farm are normally distributed with mean 1 kg and standard deviation 0.25 kg
1. Find, to 4 decimal places, the probability that a trout chosen at random weighs more than 1.25 kg
• Write down the distribution: X ~ N (1, 0.25²)
• Write down the desired probability:
• P(X > 1.25) = 1 – P(X < 1.25)
• Standardize and evaluate:
• 1 – P(Z < 1.25 – 1/0.25) = 0.1587
2. If X̄ kg represents the mean weight of a sample of 10 trouts chosen at random, state the distribution of Ȳ, evaluate the mean and variance. Find the probability that the mean weight of a sample of 10 trouts will be less than 0.9 kg
• Write down the distribution: X ~ N (1, 0.25²)
• For a sample, the mean remains equal but the variance changes. Find the new variance:
• Variance of sample = σ²/n = 0.25²/10 = 0.00625
• Write the distribution of the sample: Y ~ N (1, 0.00625)
• Write the desired probability: P(Y < 0.9)
• Standardize and evaluate:
• P(Z < 0.9 – 1/0.00625)
• = 1 – P(Z < 0.1/0.00625)
• = 0.103
###### Point Estimate and Confidence Interval
• A point estimate is a numerical value calculated from a set of data (sample) which is used as an estimate of an unknown parameter in a population
• Examples of point estimates are:
• Sample mean, x̄ estimates the population mean, μ
• Sample proportion, r/n estimates the population proportion, p
• Sample variance, s² estimates the population variance, σ²
• Point estimates are close to the population value but are not the value
• We can determine a confidence interval where the population value is likely to lie in (x̄ – δ, x̄ + δ)
###### The Variance
• Variance can be calculated/given for either a sample or a population and there is a difference between them
• Using the divisor, n: This is appropriate when:
• the data is given for the whole population and you are interested in the variance of the whole
• the data is given for the sample and you are interested in the variance of just the sample • Using the divisor (n – 1):
• Appropriate to use when the data is given for a sample and you are estimating the variance of the whole population
• The quantity of calculated, s², is known as the unbiased estimate of the population variance ###### Percentage Points for a Normal Distribution
• The percentage points are determined by finding the z-value of specific percentages
• For example, to find the z-value of a 95% confidence level, we can see that the 5% would be removed equally from both sides (2.5% on each side), so the z-value we would actually be finding would be of 100% – 2.5% = 97.5%  ###### Confidence Interval for a Population Mean
• A Sample Taken from a Normal Population Distribution with Known Population Variance: • z is the value corresponding to the confidence level required and n is the sample size
• The confidence interval calculated is exact
• Large Sample Taken from an Unknown Population Distribution with Known Population Variance:
• By the Central Limit Theorem, the distribution of x̄ will be approximately normal, so the same method as above • The confidence interval calculated is an approximate
• Large Sample Taken from an Unknown Population Distribution with Unknown Population Variance:
• As the population variance is unknown, you must first estimate the population variance, s, using the sample data • The confidence interval is an approximate
###### Examples
1. Heights of a certain species of animal are normally distributed with σ = 0.17 m. Obtain a 99% confidence interval for the population mean, with the total width less than 0.2 m. Find the smallest sample size required
• For a 99% confidence interval, find z where Φ(z) = 0.995 (think of the 1% cut from both sides):
• z = 2.576
• Subtract the limits of the interval and equate to 0.2: • Substitute the information given and find n:
• √n = 0.2/2 × 2.576 × 0.17
• n = 4126.53 ≈ 4130
###### Confidence Interval for a Population Proportion
• Calculating the confidence interval from a random sample of n observations from a population in which the proportion of successes is p and the proportion if failures is q
• The observed proportion of success p is r/n, where r represents the number of successes ###### Examples
1. A random sample of n people were questioned about their internet use. 87 of them had a high-speed internet connection. A confidence interval for the population proportion having high-speed internet connection is 0.1129 < p < 0.1771.
1. Write the mid-point of this confidence interval and hence find the value of n
• Find the midpoint of the limits, finding p:
• 0.1129 + 0.1771 – 0.1129/2 = 0.145
• The mid-point is equal to the proportion of people with high-speed internet use, so:
• 87/n = 0.145
• ∴ n = 600
2. This interval is an α% confidence interval. Find α
• Using the upper limit, this was calculated by:
• 0.1771 = 0.145 + z[√(87/600 × 513/600) ÷ 600]
• ∴ z = 2.233
• Use normal tables and find the corresponding probability
• Φ(z) = 0.9872
• The same area is chopped off from both sides of the graph, so:
• 1 – 2(1 – 0.9872) = 0.9744
• Hence α% confidence is 97.44%