Interval estimation. September 29, Outline Basic ideas Sampling variation and CLT Interval estimation using X More general problems

Transcription

1 Interval estimation September 29, 2017 STAT 151 Class 7 Slide 1

2 Outline of Topics 1 Basic ideas 2 Sampling variation and CLT 3 Interval estimation using X 4 More general problems STAT 151 Class 7 Slide 2

3 Example: Studying household expenditure from a population Population N i=1 µ = X i N X 1, X 2,, X N N σ 2 i=1 = (X i µ) 2 N Sample X 1, X 2,, X n X = n i=1 X i n n s 2 i=1 = (X i X ) 2 n STAT 151 Class 7 Slide 3

4 Point estimation Expenditure example Suppose household expenditure (X ) in the population has mean µ and variance σ 2 and we wish to estimate the parameter µ We can take a sample of observations of X to estimate µ Suppose our sample consists of n = 10 household expenditure: (X 1,..., X 10 )=(1874, 1642, 1603, 1931, 2103, 2068, 1948, 1798, 2364, 1918), an estimate of µ using X is = This type of estimate is called a point estimate There is no reference to the level of accuracy in a point estimate A SRS of independent observations STAT 151 Class 7 Slide 4

5 Interval estimation Our point estimate is a statistic calculated from a sample to estimate µ Recall that any statistic has sampling error defined by statistic parameter = point estimate µ Sampling error arises because we use a sample (only a part of the population) to infer about the entire population When we use X to estimate µ, we must account for the sampling error. Instead of saying µ = with no reference to our level of belief of our estimate, we say µ is within (a, b) with k% confidence. This type of estimate is called a confidence interval estimate STAT 151 Class 7 Slide 5

6 Confidence interval (CI) There are two basic components in a confidence interval estimate: Level of confidence a measure of our level of belief Margin of error a measure of the precision of our estimate Expenditure example We wish to say something like: we are 95% confident that the population household expenditure µ is between ± In this case Level of confidence = 95% Margin of error = The CI can also be written as ( , ) = ( } {{}, }{{} ) Lower limit Upper limit The width of a CI is upper limit - lower limit = ( ) = = 2 margin of error STAT 151 Class 7 Slide 6

7 Sampling error, variation and distribution Sampling error = µ is unknown and cannot be estimated The distribution of sampling errors can be studied and it tells us the likely values of the sampling error when X is used to estimate µ. The sampling error distribution is sometimes called a sampling distribution Population Sample k X =... X µ = Sample 2 X = µ = Sample 1 X = µ = Sampling distribution = distribution of = distribution of X STAT 151 Class 7 Slide 7

8 Sampling (error) distribution and Central Limit Theorem (CLT) Possible sampling errors = 0 The Central Limit Theorem (CLT) says that when using X from a reasonably big sample of n independent observations to estimate µ, the sampling (error) distribution is approximately a normal distribution The mean of the sampling (error) distribution is 0 and the variance of the sampling errors is var( ), which is also called the sampling variation CLT = X µ Normal }{{}}{{} (0, var( ) ) }{{} sampling error sampling sampling distribution variation We do not know where exactly is among the red s. However, using the empirical rules, we can be 95% certain that is no more than 0 ± 2 var( ) STAT 151 Class 7 Slide 8

9 From CLT to interval estimation In fact, the CLT says: = X µ Normal(0, σ 2 /n) X Normal(µ, σ 2 /n) Using CLT, our sampling error X µ behaves like one of the s in the following distribution 0 X 0 ± σ n 0 ± 1.96 σ n 0 ± 3 σ n 68% µ ± σ n 95% µ ± 1.96 σ n 99.7% µ ± 3 σ n Question: How do we translate these information into statements about µ? STAT 151 Class 7 Slide 9

10 95% confidence interval We are 95% certain that our sampling error X µ is no more than 0 ± 1.96 σ n (sometimes ±2 is used as an approximation) is equivalent to 1.96 σ n µ X 1.96 σ n 1.96 σ n + X µ X + X 1.96 σ n + X X 1.96 σ n µ X σ n We are 95% confident that µ is within ( X 1.96 σ n, X σ n ) ( X 1.96 σ n, X σ n ) is called a 95% confidence interval for µ The level of confidence is 95% and the margin of error is 1.96 σ n STAT 151 Class 7 Slide 10

11 Interpretation of a confidence level A confidence interval (CI) is a method for finding a plausible range for µ. Each time a CI is calculated using a random sample, we obtain a different interval. For example, a 95 % CI has the following property: If the method is used repeatedly, then 95% of the intervals will actually include µ. However, each time a 95% CI is calculated, the chance that µ is included in that particular interval is NOT 95% it is either { 0% (µ not inside CI, wrong estimate!) 100% (µ inside CI, correct estimate!). Therefore, our confidence in our interval is based on the fact that it may be one of the 95 (out of 100) that actually includes the unknown. STAT 151 Class 7 Slide 11

12 Other confidence intervals 90% confidence interval X ± 1.64 σ n 95% confidence interval X ± 1.96 σ n 99% confidence interval X ± 2.58 σ n n i=1 In practice σ is approximated by ˆσ = (X i X ) 2 n or n i=1 (X i X ) 2 n 1 90% confidence interval X ± 1.64 ˆσ n 95% confidence interval X ± 1.96 ˆσ n 99% confidence interval X ± 2.58 ˆσ n These approximations are reasonable as long as n is not too small. For very small n, the values 1.64, 1.96 and 2.58 are inflated by values in a table called the t-table. Unbiased estimator of σ STAT 151 Class 7 Slide 12

13 Which confidence interval? For any reasonably large sample of size n, we can construct a 90%, 95%, 99%, etc CI. In other words, we can make the following statements: We are 90% confident that µ is between X ± 1.64 ˆσ n We are 95% confident that µ is between X ± 1.96 ˆσ n We are 99% confident that µ is between X ± 2.58 ˆσ n In fact, there are infinitely many CIs we can construct. However, we report one interval that is meaningful. A meaningful interval should have: (a) a high level of confidence (b) a width that is not too wide Due to (a) and (b), we often use a 95% confidence interval. STAT 151 Class 7 Slide 13

14 Expenditure example Using (X 1,..., X 10 )=(1874, 1642, 1603, 1931, 2103, 2068, 1948, 1798, 2364, 1918), point estimates of (µ, σ 2 ) are n ˆµ = X = , ˆσ 2 i=1 = (X i X ) 2 = , n 1 and a 95% CI for µ is given by ˆµ ± 1.96 ˆσ n = ± ± = (1793.7, ) For comparison, we replace 1.96 with a value from a t-table. A t-table depends on a quantity called degree of freedom (df ), defined as df = n 1. Values for selected df s are below df = n >120 value In this example, n = 10, which gives df = 10 1 = 9; so we choose the value in the table to replace 1.96 to arrive at a 95% confidence interval of ± = ± = (1765.4, ), 10 which is wider than the interval using In fact, a confidence interval based on a t-table is always wider than its equivalence using the CLT. The idea is, for small samples, ˆσ 2 may not be a very accurate estimate of σ 2 and a wider interval accounts for this extra layer of uncertainty. STAT 151 Class 7 Slide 14

15 Improving upon a CI: Reducing the margin of error The margin of error for a 95% CI is 1.96 σ n, which depends on σ and n σ 2 = var(x ) measures the variation of X in the population, which is beyond our control. However, n is the sample size, which is under our control n can be increased to reduce the margin of error Example: What sample size m would reduce the margin of error by a factor of 1/2? We want 1.96 σ = 1 (1.96 σ ) n m 2 }{{}}{{} new margin of error old margin of error 1 = 1 ( ) 1 n m 2 1 m = 1 ( ) 1 4 n m = 4n To reduce the margin of error by a factor of 1/k, the sample size needs to be increased by k 2 times STAT 151 Class 7 Slide 15

16 Estimating parameters other than the mean CLT for MLE A 95% CI for 1,2 µ based on X is: X ± 1.96 var( X ) θ based on the MLE ˆθ is: ˆθ ± 1.96 var(ˆθ) This result holds because a similar CLT 1,2 says = ˆθ θ Normal }{{}}{{} (0, var( ) ) = Normal(0, var(ˆθ)) }{{} sampling error sampling sampling distribution variation 0 ± 1.96 var(ˆθ) 0 ˆθ 95% θ ± 1.96 var(ˆθ) 1 Using a reasonably large sample of n independent observations of X 2 True for X from most distributions STAT 151 Class 7 Slide 16

17 Expenditure example: Difference between two population means Suppose in addition to a sample of n household expenditures (X 1,..., X n ) with a population mean µ, we obtained a sample of m household expenditures (Y 1,..., Y m ) with a population mean ν and we wish to estimate the parameter θ = µ ν. We can estimate µ and ν by X and Ȳ, respectively, and hence ˆθ = X Ȳ var(ˆθ) θ ˆθ 1.96 var(ˆθ) 1.96 var(ˆθ)+ˆθ θ ˆθ+ˆθ 1.96 var(ˆθ)+ˆθ ˆθ 1.96 var(ˆθ) θ ˆθ var(ˆθ) X Ȳ 1.96 var( X Ȳ ) θ X Ȳ var( X Ȳ ) Assume populations follow a normal distribution Chapter 9 exercise STAT 151 Class 7 Slide 17