stats lesson1

• Descriptive statistics


  Describe the sample data by determining some of its numerical characteristics (statistic)
• Measures of descriptive statistics


  • Measure of Central Tendency (mean, median, mode)
  • Measures of Variation
  • Measures of Skewness
  • Measures of Kurtosis

  
• Inferential statistics


  Conclude something about the numerical characteristics of the population (parameter) using the statistic
• Estimation


  The process of making inferences about the population using the obtained information from the sample
• Estimator


  A statistic that can be used to approximate the parameter
• Estimate


  A specific value or range of values of the estimate
• Types of estimates

  • Point Estimate (single number)
  • Interval Estimate (range of values)
• Properties of good estimators

  • Unbiased (zero bias)
  • Consistent (difference between estimator and parameter gets smaller as sample size grows)
  • Relatively efficient (smaller variance compared to other estimators)

  
• Bias


  Error or difference between points given and points plotted on the line in the training set
• Variance


  Error that occurs due to sensitivity to small changes in the training set
• Confidence Interval


  An interval or estimated range of values which is likely to include the true value of a parameter
• Confidence Level


  Probability that the confidence interval contains the true value of parameter
• Region of Rejection


  Contains values which are unlikely to be the true value of the parameter

  
• Critical Value (z-score)


  A value along the x-axis that separates the confidence interval from the region of rejection
• Error


  The difference between the estimate (specific value) and the true value of the parameter
• Margin of Error


  The maximum error