Statistical inference: confidence intervals and hypothesis testing

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  • Statistical inference: confidence intervals and hypothesis testing

  • ObjectiveThe objective of this session isInference statisticSampling theoryEstimate and confidence intervalsHypothesis testing

  • Statistical analysisDescriptivecalculate various type of descriptive statistics in order to summarize certain quality of the data

    Inferentialuse information gained from the descriptive statistics of sample data to generalize to the characteristics of the whole population

  • Inferential statistic application2 broad areasEstimationcreate confidence intervals to estimate the true population parameter

    Hypothesis testing test the hypotheses that the population parameter has a specified range

  • Population & Sample mean:standard deviation:

  • Sampling theoryWhen working with the samples of data we have to rely on sampling theory to give us the probability distribution pertaining to the particular sample statistics

    This probability distribution is known as the sampling distribution

  • Sampling distributionsAssume there is a population Population size N=4Random variable, X, is age of individualsValues of X: 18, 20, 22, 24 measured in years

    ABCD

  • Sampling distributionsSummary measures for the Population Distribution

    .3.2.1 0 A B C D (18) (20) (22) (24) Population mean DistributionP(X)

  • Sampling distributionsSummary measures of sampling distribution

  • Properties of summary measures Sampling distribution of the sample arithmetic mean

    Sampling distribution of the standard deviation of the sample means

  • Estimation and confidence intervalsEstimation of the population parameters:point estimatesconfidence intervals or interval estimators

    Confidence intervals for:MeansVariance

    Large or Small samples ???

  • Confidence intervals for meanslarge samples (n >= 30)apply Z-distribution

    Probability distributionconfidence interval

  • Confidence intervals for meanslarge samples (n >= 30)From the normally distributed variable, 95% of the observations will be plus or minus 1.96 standard deviations of the mean

  • Confidence intervals for meanslarge samples (n >= 30)The confident interval is given as

    95% confidence interval-1.96 SE+1.96 SEProbability distribution2.5% in tail2.5% in tail

  • Confidence intervals for meanslarge samples (n >= 30)

    95% confidence interval-1.96 SE+1.96 SEProbability distribution2.5% in tail2.5% in tail

  • Confidence intervals for meanslarge samples (n >= 30)Thus, we can state that:the sample mean will lie within an interval plus or minus 1.95 standard errors of the population mean 95% of the time

  • Confidence intervals for meanslarge samples (n >= 30)Examplewe have data on 60 monthly observations of the returns to the SET 100 index. The sample mean monthly return is 1.125% with a standard deviation of 2.5%. What is the 95% confidence interval mean ???

  • Confidence intervals for meanslarge samples (n >= 30)Example (contd)Standard error is calculated as

    the confidence interval would be

    The probability statement would be

  • Confidence intervals for meanslarge samples (n >= 30)Example (contd)The probability statement would be

    How does the analyst use this information ???

  • Confidence intervals for meansWhat about small samples (n < 30)apply t-distribution

    Probability distribution

  • Confidence intervals for means What about small sample ??? (n < 30)Apply t-distribution The confidence interval becomes

    The probability statement pertaining to this confidence interval is

  • Confidence intervals for means ExampleFrom 20 observations, the sample mean is calculated as 4.5%. The sample standard deviation is 5%. At the 95% level of confidence: the confidence interval is the probability statement is

  • Confidence intervals for variances Apply a distributionThe confidence interval is given as

    The probability statement pertaining to this confidence interval is

  • Confidence intervals for variances ExampleFrom a sample of 30 monthly observations the variance of the FTSE 100 index is 0.0225. With n-1 = 29 degrees of freedom (leaving 2.5% level of significant in each tail)the confidence interval is the probability statement is

  • Hypothesis testing 2 Broad approachesClassical approachP-value approach

    is an assumption about the value of a population parameter of the probability distribution under consideration

  • Hypothesis testing When testing, 2 hypotheses are establishedthe null hypothesisthe alternative hypothesis

    The exact formulation of the hypothesis depends upon what we are trying to establishe.g. we wish to know whether or not a population parameter, , has a value of

  • Hypothesis testing How about we wish to know whether or not a population parameter, , is greater than a given figure , the hypothesis would then be

    And if we wish to know whether or not a population parameter is greater than a given figure , the hypothesis would then be

  • The standardized test statistic In hypothesis testing we have to standardizing the test statistic so that the meaningful comparison can be made with theStandard normal (z-distribution) t-distribution distribution

    The hypothesis test may be One-tailed testTwo-tailed test

    MEANVARIANCE

  • Hypothesis test of the population mean Two-tailed test of the meanSet up the hypotheses as

    Decide on the level of significance for the test (10, 5, 1% level etc.) and establish 5, 2.5, 0.5% in each tailSet the value of in the null hypothesis Identify the appropriate critical value of z (or t) from the tables (reflect the percentages in the tails according to the level of significance chosen)

  • Hypothesis test of the population mean Two-tailed test of the meanApplying the following decision rule:

    Accept H0 if

    Reject H0 if otherwise

  • Hypothesis test of the population meanExampleConsider a test of whether or not the mean of a portfolio managers monthly returns of 2.3% is statistically significantly different from the industry average of 2.4%. (from 36 observations with a standard deviation of 1.7%)

  • Hypothesis test of the population meanExampleAn analyst claims that the average annual rate of return generated by a technical stock selection service is 15% and recommends that his firm use the services as an input for its research product. The analysts supervisor is skeptical of this claim and decides to test its accuracy by randomly selecting 16 stocks covered by the service and computing the rate of return that would have been earned by following the services recommendations with regards to them over the previous 10-year period. The result of this sample are as follows:The average annual rate of return produced by following the services advice on the 16 sample stocks over the past 10 years was 11%The standard deviation in these sample results was 9%

    Determine whether or not the analysts claim should be accepted or rejected at the 5% level of significant ???

  • Hypothesis test of the population mean One-tailed test of the mean (Right-tailed tests)Set up the hypotheses as

    Applying the following decision rule:Accept H0 if

    Reject H0 if

  • Hypothesis test of the population meanExampleIf we wish to test that the mean monthly return on the FTSE 100 index for a given period is more than 1.2. From 60 observations we calculate the mean as 1.25% and the standard deviation as 2.5%.

  • Hypothesis test of the population meanExampleWe wish to test that the mean monthly return on the S&P500 index is less than 1.30%. Assume also that the mean return from 75 observations is 1.18%, with a standard deviation of 2.2%.

  • Hypothesis test of the population mean Two-tailed test Applying the following decision rule:

    Accept H0 if Reject H0 if otherwise

    One-tailed test Applying the following decision rule:Accept H0 if Reject H0 if

    Left or right tailed test ???How bout the other ???

  • Hypothesis testing of the varianceTwo-tailed testThe standardized test statistic for the population variance is

    This standardized test statistic has a distribution

  • Hypothesis testing of the varianceExampleIf we wish to test the variance of share B is below 25. The sample variance is 23 and the number of observation is 40

  • The p-value method of hypothesis testingThe p-value is the lowest level of significance at which the null hypothesis is rejectedIf the p-value the level of significance ()accept null hypothesisIf the p-value < the level of significance ()reject null hypothesis

  • Calculation the p-valueIf we wish to find an investment give at least 13.2%. Assume that the mean annualized monthly return of a given bond index is 14.4% and the sample standard deviation of those return is 2.915%, there were 30 observations an the returns are normally distributed.

  • Calculation the p-value

    The test statistic is:

    With degree of freedom = 29 a t-value of 2.045 leaves 2.5% in the taila t-value of 2.462 leaves 1% in the tail

  • Calculation the p-valueCalculate p-value from interpolation

    P-value = 0.025 (0.50 x (0.025 0.01) = 0.0175 = 1.75%

    P-value (1.75%) < (5%), thus reject null hypothesis

  • Conclusion Meaning of statistical inferenceSampling theory Application of statistical inference Confidence intervalsEstimationHypothesis testin